Queensland University of Technology

School of Physical and Chemical Sciences

Analysis of alternative water sources

for use in the manufacture of concrete

This thesis is submitted as partial fulfilment

of the requirements for the degree of

Maters of Applied Science

By

Leigh M. McCarthy B.Sc

Supervisor: Dr Serge Kokot

Assoc. Supervisor: Prof Ray L Frost

2

3

Abstract

In Australia and many other countries worldwide, water used in the manufacture of concrete

must be potable. At present, it is currently thought that concrete properties are highly

influenced by the water type used and its proportion in the concrete mix, but actually there is

little knowledge of the effects of different, alternative water sources used in concrete mix

design. Therefore, the identification of the level and nature of contamination in available

water sources and their subsequent influence on concrete properties is becoming

increasingly important. Of most interest, is the recycled washout water currently used by

batch plants as mixing water for concrete. Recycled washout water is the water used onsite

for a variety of purposes, including washing of truck agitator bowls, wetting down of

aggregate and run off.

This report presents current information on the quality of concrete mixing water in terms of

mandatory limits and guidelines on impurities as well as investigating the impact of recycled

washout water on concrete performance. It also explores new sources of recycled water in

terms of their quality and suitability for use in concrete production.

The complete recycling of washout water has been considered for use in concrete mixing

plants because of the great benefit in terms of reducing the cost of waste disposal cost and

environmental conservation. The objective of this study was to investigate the effects of

using washout water on the properties of fresh and hardened concrete. This was carried out

by utilizing a 10 week sampling program from three representative sites across South East

Queensland. The sample sites chosen represented a cross-section of plant recycling

methods, from most effective to least effective. The washout water samples collected from

each site were then analysed in accordance with Standards Association of Australia AS/NZS

5667.1 :1998. These tests revealed that, compared with tap water, the washout water was

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higher in alkalinity, pH, and total dissolved solids content. However, washout water with a

total dissolved solids content of less than 6% could be used in the production of concrete

with acceptable strength and durability. These results were then interpreted using

chemometric techniques of Principal Component Analysis, SIMCA and the Multi-Criteria

Decision Making methods PROMETHEE and GAIA were used to rank the samples from

cleanest to unclean.

It was found that even the simplest purifying processes provided water suitable for the

manufacture of concrete form wash out water. These results were compared to a series of

alternative water sources. The water sources included treated effluent, sea water and dam

water and were subject to the same testing parameters as the reference set. Analysis of

these results also found that despite having higher levels of both organic and inorganic

properties, the waters complied with the parameter thresholds given in the American

Standard Test Method (ASTM) C913-08. All of the alternative sources were found to be

suitable sources of water for the manufacture of plain concrete.

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Statement of Originality

The work contained in this thesis has not been previously submitted to meet requirements

for an award at this or any other higher education institution. To the best of my knowledge

and belief, the thesis contains no material previously published or written by another person

except where due reference is made.

Signature _____________________________________

Leigh M. McCarthy

Date _________________________

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Acknowledgements

This project would not have been possible without the support of many people. Many thanks

to Dr Serge Kokot and Prof. Ray Frost for their direction, assistance, and guidance. In

particular my supervisor, Dr Serge Kokot, who read my numerous revisions and helped

make some sense of the confusion. Thanks are also due to Mr. Glenn Carson, Dr Dak

Bakewash and Mr. Russel Gutsky from Readymix for their assistance and for providing me

with the financial means to complete this project. I would also like to thank Dr Wayde

Martens whose help was integral in the completion of this thesis. Also thanks to my fellow

postgraduate students, who sympathized with my complaints, understood my frustrations

and most of all offered guidance and support.

And finally, thanks go to my family and friends who endured this long process with me,

always offering support and love. Thanks to my parents who were unwavering in their

encouragement and support and who, through years of patience and hard work afforded me

a sense of ambition and self, allowing me to reach for my goals. To my sister Clare thanks

for your patience, understanding and tolerance of my disappointments and for sharing my

triumphs. Lastly, to my brother Sean whose own achievements served as a reminder that

only your best effort will do.

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Table of Contents

1 Introduction .................................................................................................................. 12

1.1 Prologue ............................................................................................................... 12

1.2 Concrete and its constituents ................................................................................ 15

1.3 Cement and aggregates........................................................................................ 17

1.3.1 Hydration reactions of cement ....................................................................... 21

1.3.2 Cement Hydration Products ........................................................................... 23

1.3.3 Admixtures ..................................................................................................... 25

1.4 Water Quality, its properties and influence on concrete......................................... 26

2 Methodology ................................................................................................................ 29

2.1 Sample Guidelines ................................................................................................ 29

2.2 Sample Preparation .............................................................................................. 29

2.3 Equipment and Materials ...................................................................................... 31

2.4 Chemical Preservatives ........................................................................................ 31

2.5 Sampling Methods and Procedures ...................................................................... 31

2.6 Preparation of Concrete Cylinders ........................................................................ 33

2.7 Instrumental Analysis ............................................................................................ 34

2.8 Instrumentation used for the analysis of water samples ........................................ 35

2.8.1 Measurement of pH ....................................................................................... 35

2.8.2 Measurement of Relative Alkalinity ................................................................ 36

2.8.3 Measurement of Electrical Conductivity ......................................................... 37

2.8.4 Measurement of Total Dissolved Solids ......................................................... 38

8

2.8.5 Measurement of Chloride ............................................................................... 39

2.8.6 Compressive strength Analysis of concrete samples ..................................... 40

2.9 Multi Criteria Decision Making Methods ................................................................ 42

2.9.1 Chemometric Analysis ................................................................................... 42

2.10 Multicriteria Decision Making (MCDM) .................................................................. 51

2.10.1 Preference Ranking Organisation Method for Enrichment Evaluation

(PROMETHEE) ............................................................................................................ 52

2.10.2 Geometric Analysis for Interactive Aid (GAIA) ................................................ 54

3 Compilation of baseline data for water quality .............................................................. 55

3.1 Washout Waters – Building a baseline .................................................................. 55

3.2 Concrete Plant sites throughout SE-Qld ................................................................ 56

3.2.1 Southport Concrete Plant ............................................................................... 56

3.2.2 Beenleigh Concrete Plant .............................................................................. 56

3.2.3 Murarrie Concrete Plant ................................................................................. 56

3.3 Analysis of Baseline Water ................................................................................... 58

3.4 Simple Analysis of Baseline Water Sample Results .............................................. 58

3.5 Chemometric interpretation of Water Quality data ................................................. 62

3.6 Chemometric Analysis of Baseline Samples ......................................................... 66

3.6.1 Principal Component Analysis ....................................................................... 66

3.6.2 PROMETHEE and GAIA ................................................................................ 72

3.7 Chapter Summary ................................................................................................. 80

4 Analysis of Alternative Water Sources for Comparison ................................................ 81

4.1 Location of alternative water source sampling sites .............................................. 81

9

4.2 Southport Sea Water ............................................................................................ 83

4.2.1 Southport Treated Effluent ............................................................................. 83

4.2.2 Kawana Treated Effluent................................................................................ 83

4.2.3 Coolum Bore Water ....................................................................................... 83

4.2.4 Gympie Bore water ........................................................................................ 84

4.2.5 Ipswich River Water ....................................................................................... 84

4.2.6 Murarrie Bore Water ...................................................................................... 84

4.2.7 Coomera Dam Water ..................................................................................... 84

4.2.8 Coomera Bore water ...................................................................................... 84

4.3 Analysis of Alternative Water Sources .................................................................. 86

4.4 Analysis according to water type ........................................................................... 88

4.4.1 Sea Water ...................................................................................................... 88

4.4.2 Treated Effluent ............................................................................................. 89

4.4.3 Bore Water .................................................................................................... 91

4.4.4 Dam and River Water .................................................................................... 91

4.5 Chemometric analysis of alternative water source samples .................................. 93

4.5.1 PCA analysis ................................................................................................. 93

4.5.2 SIMCA ........................................................................................................... 97

4.5.3 Fuzzy Clustering .......................................................................................... 103

4.5.4 PROMETHEE and GAIA .............................................................................. 105

4.6 Chapter Summary ............................................................................................... 116

5 Concluding Remarks .................................................................................................. 117

6 References ................................................................................................................ 119

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Table of figures

Figure 2.1 Testing Apparatus used to determine Compressive Strength ............................. 40

Figure 2.2 Example of Principal Component Analysis ......................................................... 46

Figure 3.1 Biplot with baseline sample results with IRMV and compressive strength results 67

Figure 3.2 PCA Biplot of PC1 vs. PC2 with all variables including compressive strength

results ................................................................................................................................. 69

Figure 3.3 GAIA plot showing reference variables and baseline sample sites with

compressive strength results ............................................................................................... 73

Figure 3.4 GAIA plot showing reference variables and baseline sample sites .................... 77

Figure 4.1 PC1 v PC2 for alternative water source results .................................................. 92

Figure 4.2 PC1 v PC2 all Alternative and all Baseline samples with compressive strength

results ................................................................................................................................. 94

Figure 4.3 Cooman Plot For Murarrie and Coomera Dam including RSD values ............... 100

Figure 4.4 Cooman Plot For Murarrie and Sea Water including RSD values ..................... 100

Figure 4.5 Plot of Discrimination power vs. variables for Southport & Murarrie.................. 106

Figure 4.6 GAIA plot for all Alternative and all Baseline samples ...................................... 109

Figure 4.7 GAIA Plot for all Alternative and all Baseline samples with compressive strength

results ............................................................................................................................... 114

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Table of tables

Table 1.1 - Chemical and physical composition of ordinary Portland cement ...................... 16

Table 2.1 - Specifications for sample preservation .............................................................. 30

Table 2.2 Preference functions in PROMETHEE ................................................................ 50

Table 2.3 – sample matrix ................................................................................................... 52

Table 3.1 Average Values over 10 week period for each plant and Tap Water compared to

the Tolerable Limits ............................................................................................................. 57

Table 3.2 Metal Concentrations from Southport, Beenleigh and Murarrie............................ 60

Table 3.3 Readymix Water Specification Guidelines ........................................................... 65

Table 3.4 PROMETHEE Net Ranking for Southport and Murarrie using Internal Readymix

Variables and Compressive strength results ....................................................................... 71

Table 3.5 PROMETHEE Net Ranking for the Baseline Data using Internal Readymix

Variables Only..................................................................................................................... 75

Table 4.1 Typical composition of sea water* ....................................................................... 82

Table 4.2 Comparison between Average Alternative Water Source Results and Compressive

strength Results with Baseline samples and Tap water ....................................................... 85

Table 4.3 PCA models for SIMCA ....................................................................................... 96

Table 4.4 SIMCA fit to Southport RSDcrit=0.45, p=0.05........................................................ 98

Table 4.5 SIMCA fit to Murarrie where RSDcrit = 1.13, p= 0.05 ............................................ 98

Table 4.6 Hard clustering for Alternative Water Samples .................................................. 102

Table 4.7 Soft clustering for Alternative Water Samples .................................................... 104

Table 4.8 PROMETHEE Net ranking of alternative water sources .................................... 107

Table 4.9 PROMETHEE Net Ranking for all Alternative and all Baseline samples with

Compressive strength Results .......................................................................................... 112

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1 Introduction

1.1 Prologue

Second only to water, concrete is the most consumed substance, with three tonnes used per

person per year [1]. Twice as much concrete is used in construction as all other building

materials combined [2]. Thus, there is little doubt that concrete will remain in use well into

the future. As demand increases for this fundamental building material, studies such as the

one presented here will continue to be carried out in the hope of optimising the

characteristics and properties, ensuring that concrete remains environmentally friendly and

affordable. This study is aimed at understanding the role of water quality in concrete

manufacture. In order to accomplish this aim, this study has set out to identify key elements,

variables and characteristics necessary for a water source to be considered a viable option

within the concrete industry.

Concrete consists of aggregates, sand, cementitious material, admixtures and water which

are mixed together to provide a uniform plastic material [3]. This plastic material gradually

sets after a period of one to three hours which increases in strength, particularly over the first

month of its life [4]. Varying the mix of cement, sand and aggregate used in a concrete blend

consequently enables its use in a wide range of applications. Products can be designed,

coloured and shaped to accommodate a variety of environmental conditions, architectural

requirements and to withstand a wide range of loads, stresses and impacts. With the

increasing demand for high quality water, a large quantity of chemical agents must be used

in the water purification process, which in turn generates enormous amounts of waste wash

water [5]. Of all the options for wash water disposal, reuse has been considered most

economical and environmentally sound.

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This study evaluated the possibility of incorporating wash water in the making of concrete.

The goal was to search for the optimal specifications to maximize the replacement of potable

water with an alternative source.

Within this scenario, re-envisioning industrial wastes as alternative raw materials becomes of

interest, both economically and logistically, for a wide range of applications which includes

the fabrication of concrete. As such, there have been comprehensive studies detailing

possible alternative water sources suitable for the production of concrete. Previous

research, such as that carried out by Al-Harty, Borger, and Chatveera has focused on what

effect minerals, salts and impurities contained in the water have on the properties and

performance of fresh and hardened concrete [1, 5, 6]. Results obtained in these studies

indicate that the use of non-potable water yields lower compressive strength in comparison

to concrete made with potable water.

Previous research in this field, carried out by Muszynski, Sandrolini and Su, has utilised

treated effluent water samples, water from streams, lakes and sea water for concrete

construction [2, 7, 8]. These studies however were not carried out within Australia, thus the

results found were not obtained in light of Australian Standards [9, 10]. Such studies also

imply that the concrete produced was not made of common aggregates and cement found in

Australia. And, whilst the results obtained will serve as a useful guideline to the behaviour of

wet, hardening and hardened concrete, the analytical results obtained in such studies have

not, as yet, been coupled with chemometrics methodology. Hence, this study aims to build

on previous investigations by combining instrumental and structural analysis with

chemometrics.

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The novel application of Multi-Criteria Decision Making (MCDM) methods of data analysis

will also be carried out to complement chemometrics findings. Chemometrics was applied to

compare and discriminate individual samples, as this MCDM is primarily concerned with the

extraction of significant information from the data which has been characterised into

chemical, physico-chemical and structural components [11, 12].

This study sought to research and develop, the combination of instrumental analysis with

chemometrics to provide a rapid method for assessing the suitability of a variety of water

sources for use in concrete production. These would be developed such there would be no

lasting harmful effects to its properties and characteristics of the resultant concrete. This was

performed with the primary objectives:

• To build a comprehensive water quality baseline with specific parameters outlining

the suitable elemental, physico-chemical and structural properties of water for use in

concrete manufacture.

• To develop elemental, physico-chemical and structural guidelines with the aid of

chemometrics, and the novel application of Multi-Criteria Decision Making Methods

ensuring optimal water and concrete results.

• To undertake an investigation into the suitability of alternative water sources,

employing chemometrics.

• To report the results clearly, enabling industry to use the information to ensure that

changes are made to increase the cleanliness and quality of water and subsequently

improve the performance of the resultant concrete.

The remainder of this chapter will focus on the role of water in concrete production, as well

as provide an introduction to the composition of concrete. It will also introduce the

chemometric techniques utilised for this study and conclude with an examination of the

15

instrumental techniques utilised in this project. Chapter 2 describes the materials,

procedures and chemometrics aspects utilised in this work. Chapter 3 is concerned with the

instrumental study of water samples and concrete test cylinders. Chapter 4 focuses on the

chemometric modelling abilities of water from a cross-section of treatment plants and will

concentrate on a novel investigation of alternative water sources through instrumental and

chemometric modelling.

1.2 Concrete and its constituents

Concrete is one of the most widely used construction materials [4, 13]. It is a durable and

high strength material that has a low permeability. Both the fresh and hardened states of

concrete must fulfil the intended purpose of its use. Consistency and cohesiveness are the

two most important properties when concrete is in its fresh state, as they must facilitate

compaction and transportation without segregation [14]. When in the hardened state, it is

imperative that the compressive strength of concrete lies within the required limits. The

compressive strength affects density, impermeability, tensile strength and chemical

resistance.

Concrete is a construction material that is made from cement, aggregate such as gravel and

sand, water and admixtures [15, 16]. It solidifies and hardens after mixing and placement

due to a chemical process known as hydration. In order for concrete to be manufactured,

water must react with the cement, which bonds the other components together, eventually

creating a stone-like material. Whilst concrete is the final monolithic product, cement is a

vital component and acts as the bonding material [17].

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Table 1.1 - Chemical and physical composition of ordinary Portland cement

*H. F. W. Taylor, Cement Chemistry, 2nd Ed., Academic Press, London (1997).

Component Ordinary

Portland Cement (%)

SiO2 21.95

Al2O3 4.95

Fe2O3 3.74

CaO 62.33

MgO 2.08

SO3 2.22

K2O 0.56

Na2O 0.32

TiO2 0.17

Mn2O3 0.05

Cl- 0.01

Initial setting time (min) 110

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1.3 Cement and aggregates

Cement is a basic ingredient of concrete, mortar and plaster. The most common type of

hydraulic cement, Portland cement, consists of a mixture of oxides of calcium, silicon and

aluminium can be seen in Table 1.1. Discovered by an English engineer Joseph Aspdin in

1824, it is manufactured primarily from limestone, clay minerals and gypsum in a high

temperature process that drives off carbon dioxide and chemically combines the primary

ingredients into new compounds [18, 19]. Hydraulic cements harden and set after the

addition of water as a result of chemical reactions with the mixing water and after hardening,

retain strength and stability even under water [18-20].

Cement and water form a paste coating each particle of stone and sand. The hydration

reaction causes cement paste to harden and gain strength [21, 22]. This reaction is vital for

the properties attained in the final concrete mix, and as such, the characteristics of the

concrete are determined by the quality of the paste. The strength of the paste, in turn,

depends on the ratio of water to cement which is measured but the weight of mixing [23-27].

This is the weight of the mixing water divided by the weight of the cement. High-quality

concrete is produced by lowering the water-cement ratio as much as possible but always

trying to retain enough water to ensure the workability of fresh concrete. A higher quality

concrete is produced if less water is used, provided the concrete is properly placed,

consolidated, and cured.

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A low water-to-cement (w/c) ratio is needed to achieve strong concrete. It would seem

therefore that by merely keeping the cement content high, one could use enough water for

good workability and still have a low w/c ratio [6, 24, 27]. The problem is that cement is the

most costly of the basic ingredients. Thus, in order to ensure an economical and practical

concrete mix, both fine and coarse aggregates are utilised to make up the bulk of the

concrete mixture. And as such the quality of aggregates is very important [28, 29]. Fine

aggregate (sand) is made up of particles which can pass through a 3/8 inch sieve whilst

coarse aggregates are larger than 3/8 inch in size [28]. It is however becoming common for

recycled aggregates from construction, demolition and excavation waste to be used as

partial replacements of natural aggregates, while a number of manufactured aggregates,

including air-cooled blast furnace slag and bottom ash are also permitted [30, 31]. While

recycling building materials is important, the shape, size, density and strength of such

aggregate particles can vary significantly, and can therefore adversely influence the

properties of the concrete.

Concrete is a blend of natural materials, and often has natural imperfections. The

performance of exterior concrete slabs is significantly influenced by the entrainment of

microscopic air bubbles into the concrete [32]. An air entrainment admixture causes

microscopic air bubbles to form throughout the concrete that function as relief valves when

water in the concrete freezes, helping to prevent surface deterioration.

During hydration and hardening, concrete needs to develop certain physical and chemical

properties. Among other qualities, mechanical strength, low moisture permeability, and

chemical and volumetric stability are necessary. There are many characteristics that affect

concrete and its properties, all of which depend on the specific mix being used [30]

19

Workability is the ability of a fresh or plastic concrete mix to fill the mould properly without

reducing the concrete's quality. Workability depends on water content, aggregate size,

cementitious content and level of hydration, but can also be modified by adding chemical

admixtures [33]. Raising the water content or adding chemical admixtures will increase a

concrete‟s workability. Excessive water will lead to increased bleeding and/or segregation of

aggregates with the resulting concrete having reduced quality. The use of an aggregate with

an undesirable gradation can result in a very harsh mix design with a very low slump, which

cannot be readily made more workable by addition of reasonable amounts of water [16].

Cement requires time to fully hydrate before it acquires strength and hardness, thus

concrete must be cured once it has been placed. Curing is the process of keeping concrete

under a specific environmental condition until hydration is relatively complete [16]. Good

curing is usually undertaken in a moist environment with a controlled temperature. This is

necessary as a moist environment promotes hydration, since increased hydration lowers

permeability and increases strength resulting in a higher quality material [34]. Improper

curing can lead to several serviceability problems including cracking, increased scaling, and

reduced abrasion resistance.

Compressive strength of concrete determines how much pressure concrete can withstand

before cracking and weakening. This compressive strength depends mainly on the

properties and quality of the cement paste and the aggregate [35, 36]. If the aggregate

consists of a soft or weak material, the concrete will also be weak. The strength of the

concrete can be controlled by choosing the mix proportions provided that good quality

aggregates are used and the correct manufacturing procedures are followed. If not enough

water was added to the mix, the cement paste remains too dry and stiff and the concrete will

be weak. If too much water was added, making the cement paste too thin, the concrete will

again be weak [37].

20

Concrete has relatively high compressive strength, but significantly lower tensile strength

and as a result, concrete always fails from tensile stresses. The practical implication of this is

that concrete elements subjected to tensile stresses must be reinforced. Concrete is most

often constructed with the addition of steel or fibre reinforcement. The reinforcement can be

by bars, mesh, or fibres [38].

As concrete is a liquid which hydrates to a solid, plastic shrinkage cracks can occur soon

after placement; but if the evaporation rate is high, they often can occur during finishing

operations. Aggregate interlock and steel reinforcement in structural members often negates

the effects of plastic shrinkage cracks, rendering them aesthetic in nature [38]. Properly

tooled control joints or saw cuts in slabs provide a plane of weakness so that cracks occur

unseen inside the joint, making a more aesthetic presentation. In very high strength concrete

mixtures, the strength of the aggregate can be a limiting factor to the ultimate compressive

strength. In concretes with a high water-cement ratio the use of coarse aggregate with a

round shape may reduce aggregate interlock [39, 40].

Concrete has a very low coefficient of thermal expansion. However, if no provision is made

for expansion very large forces can be created, causing cracks in parts of the structure not

capable of withstanding the force or the repeated cycles of expansion and contraction [41].

As concrete matures it continues to shrink, due to the ongoing reaction taking place in the

material, although the rate of shrinkage falls relatively quickly and keeps reducing over time.

The relative shrinkage and expansion of concrete and brickwork require careful

accommodation when the two forms of construction interface.

Since the hydration of cement is so significant, the following section examines in detail the

process of hydration, explaining the reactions that occur and their influence on the final

concrete product.

21

1.3.1 Hydration reactions of cement

The formation of water-containing compounds facilitates the hardening and setting of

hydraulic cements [42]. The reaction and the reaction products are referred to as hydration

and hydrates respectively. When cement and water are mixed together, the reactions which

occur are mostly exothermic. An indication of the rate at which the minerals are reacting, is

given by monitoring the rate at which heat is evolved using a technique called conduction

calorimetry.

During the process of heat evolution three principal reactions occur [6]. Firstly, during

hydration and hardening, concrete develops certain physical and chemical qualities including

mechanical strength, low moisture permeability, and chemical and volumetric stability. Each

of these characteristics effect the produced concrete however, the water to cement ratio

has the greatest effect on the quality of concrete [1, 43].

Almost immediately on adding water, some of the clinker sulphates and gypsum dissolve,

producing an alkaline, sulfate-rich solution. Soon after mixing, the crystals of calcium

aluminate (Ca3Al2O6), hereby annotated as (C3A), reacts with the water to form an

aluminate-rich gel [44]. The gel reacts with sulfate in solution to form small rod-like crystals

of ettringite ((CaO)6(Al2O3)(SO3)3·32 H2O). (C3A) hydration is a strongly exothermic reaction

but it does not last long, typically only a few minutes and is followed by a period of a few

hours of relatively low heat evolution. This is called the dormant or induction period.

The first part of the dormant period corresponds to the time when it is most beneficial for the

concrete to be placed. As the dormant period progresses, the paste becomes too stiff to be

workable. At the end of the dormant period, the alite (Ca3SiO5) and belite (Ca2SiO4) in the

cement start to hydrate, with the formation of calcium silicate hydrate and calcium hydroxide.

22

During this phase, an increase of calcium hydroxide occurs as a result of hydrolysis of

tricalcium silicate Equation 1.1.

Equation 1.1

2Ca3SiO5 + 6H2O 3 Ca(OH)2 + Ca3Si2O7.3H2O

Thus, on the addition of water, calcium silicate rapidly reacts to release calcium ions,

hydroxide ions, and a large amount of heat. The pH quickly rises to over 12 because of the

release of hydroxide (OH-) ions. This initial hydrolysis slows down quickly after heat

evolution begins to decrease. The reaction slowly continues producing calcium and

hydroxide ions until the system becomes saturated. Once this occurs, the calcium hydroxide

starts to crystallize. Simultaneously, calcium silicate hydrate begins to form. Ions are

precipitated out of solution accelerating the reaction of tricalcium silicate to calcium and

hydroxide ions. This corresponds to the main period of cement hydration, during which time

concrete strength increases. The cement grains react from the surface inwards, and the

anhydrous particles become smaller. (C3A) hydration also continues, as fresh crystals

become accessible to water.

The cement paste immediately stiffens and increases with time. After reaching a certain level

of hardness, a second reaction takes place which promotes the immediate set of the

concrete. .

Equation 1.2

Ca3(AlO3)2 + 6H2O Ca6(AlO3)2.6H2O

When gypsum (CaSO4 2H2O) is added to the cement, as it is in most hydraulic cements, the

hydration reaction of tricalcium aluminate is altered (Equation 1.3). The reaction of

tricalcium aluminate with water forms calcium aluminate trisulphate hydrate (ettringite). Once

23

the system is free of gypsum, calcium aluminate monosulphate hydrate (monosulphate)

forms as in Equation 1.4.

Equation 1.3

Ca3(AlO3)2 + 3CaSO4.2H2O + 26H2O Ca6[Al(OH)6]2(SO4)3.26H2O

Equation 1.4

Ca6[Al(OH)6]2(SO4)3.26H2O 3CaO.Al2O3.CaSO4.12H2O

1.3.2 Cement Hydration Products

The products of the reaction between cement and water are termed 'hydration products.'

When concrete is manufactured using Portland cement as the cementitious material there

are four main types of hydration product:

Calcium silicate hydrate: this is the main hydration product and is the main source of

concrete strength. It is often abbreviated, using notation, to 'C-S-H,' the dashes indicating

that no strict ratio of SiO2 to CaO is inferred. The Si/Ca ratio is somewhat variable but

typically approximately 0.45-0.50.

Calcium hydroxide - Ca(OH)2: often abbreviated, as 'CH.' CH is formed mainly from alite

hydration. Alite has a Ca/Si ratio of 3:1 and C-S-H has a Si/Ca ratio of approximately 2:1, so

excess lime is available from alite hydration and this produces CH.

Ettringite: Ettringite is present as rod-like crystals in the early stages of cement hydration.

The chemical formula for ettringite is Ca6[Al(OH)6]2(SO4)3.26H2O.

Monosulfate: Monosulfate tends to occur in the later stages of hydration, after a few days.

Usually, it replaces ettringite, either fully or partly. The chemical formula for monosulfate is

C3A.CaSO4.12H2O. Both ettringite and monosulfate are compounds of C3A, CaSO4

(anhydrite) and water, in different proportions.

24

Al-Fe-tri (Aft) and Al-Fe-mono (AFm) phases: Ettringite is a member of a group known as

AFt phases. The general definitions of these phases are somewhat technical [4, 40], but

ettringite is an AFt phase because it contains three (tri) molecules of anhydrite when written

as C3A.3CaSO4.32H2O and monosulfate is an AFm phase because it contains one (m-

mono) molecule of anhydrite when written as C3A.CaSO4.12H2O[45].

Important points to note about AFm and AFt phases are that:

They contain large amounts of water, especially the AFt phases.

They contain different ratios of sulfur to aluminium.

Aluminium can be partly-replaced by iron in both AFm and AFt phases.

Sulfate ion in the AFm phases can be replaced by other anions; a one-for-one

substitution if the anion is doubly-charged (e.g.: carbonate, CO2-) or one-for-two if the

substituent anion is singly-charged (e.g.: hydroxyl, OH- or chloride, Cl-). The sulfate in

ettringite can be replaced by carbonate or, probably, partly replaced by two hydroxyl

ions [4, 40].

Monosulfate gradually replaces ettringite in many concretes because the ratio of available

alumina to sulfate increases with continued cement hydration. On mixing cement with water,

most of the sulfate is readily available to dissolve, but much of the C3A is contained inside

cement grains with no initial access to water. Continued hydration gradually releases

alumina and the proportion of ettringite decreases as that of monosulfate increases.

If there is eventually more alumina than sulfate available, the entire sulfate will exist as

monosulfate, with any additional alumina present as the hydroxyl-substituted AFm phase. If

there is an excess of sulfate, the cement paste will contain a mixture of monosulfate and

ettringite. Near the concrete surface, carbonation will release sulfate as carbonate ions

replace sulfate in the ettringite and monosulfate phases.

25

1.3.3 Admixtures

Admixtures often strengthen, speed up or slow down the setting time, and help to protect

concrete against the effects of temperature changes and exposure. Therefore admixtures

tend to counteract any forces that negatively affect concrete [46]. Often in the form of

powder or fluids, admixtures are added to the concrete to give it certain characteristics not

obtainable with plain concrete mixes [46]. In normal use, admixture dosages are less than

5% by mass of cement, and are added to the concrete at the time of batching or mixing. The

most common types of admixtures are [46-48]:

Accelerators speed up the hydration (hardening) of the concrete.

Retarders slow the hydration of concrete, and are used in large or difficult pours

where partial setting before the pour is complete is undesirable.

Air-entrainers add and distribute tiny air bubbles in the concrete, which will reduce

damage during freeze-thaw cycles thereby increasing the concrete's durability.

Plasticizers (water-reducing admixtures) increase the workability of plastic or "fresh"

concrete, allowing it to be placed more easily, with less consolidating effort.

Superplasticisers (high-range water-reducing plasticizers) which have fewer

deleterious effects when used to significantly increase workability. Alternatively,

plasticizers can be used to reduce the water content of a concrete (and have been

called water reducers due to this application) while maintaining workability. This

improves its strength and durability characteristics.

Pigments can be used to change the colour of concrete, for aesthetics.

Corrosion inhibitors are used to minimize the corrosion of steel and steel bars in

concrete.

Bonding agents are used to create a bond between old and new concrete.

Pumping aids improve pumpability, thicken the paste, and reduce thinning of the

paste.

26

Invaluable to concrete production, mineral admixtures are used primarily to reduce the cost

of concrete construction, to modify the properties of hardened concrete, to ensure the quality

of concrete during mixing, transporting, placing, and curing and to overcome certain

emergencies during concrete operations. And, in order to ensure the reliability, attributes of

concrete remain constant, concrete is routinely analysed both chemically and physically.

Chemical analyses include extraction, as well as wet and spectrochemical methods.

1.4 Water Quality, its properties and influence on concrete

In general, the increasing industrial activity and the rising cost of natural mineral resources,

and forcing the ready-mixed concrete industry to review the logistics of raw materials supply.

A lack of potable water, an integral constituent of concrete, has resulted in the search for

possible alternatives. While almost any natural water that is drinkable and has no

pronounced taste or odour may be used as mixing water for concrete, the rising cost of such

waters has prompted research such as this [49]. However, the substitution of potable water

with another source has many associated problems and risks that must be eliminated in

order to ensure the quality and performance of concrete remains unchanged.

The principal considerations on the quality of mixing water are those related to the effect on

workability, strength and durability. The suitability of waters can be identified by carrying out

performance tests such as compressive strength, as well as their adherence to guidelines

which set limits for certain chemical properties, including sulphate, chlorides and total

dissolved solids, in order to ensure the durability of concrete.

Concrete is intended to be strong, durable and resilient; a key factor that determines these

properties is the water/cement (w/c) ratio. This ratio describes the mass ratio of water to

cement where a lower w/c ratio will yield a concrete which is stronger and more durable,

27

while a higher w/c ratio yields a concrete with a larger slump, so it may be placed more

easily. Cement paste is the material formed by combination of water and cementitious

materials; that part of the concrete which is not aggregate or reinforcing [13]. The workability

or consistency is affected by the water content, the amount of cement paste in the overall

mix and the physical characteristics (maximum size, shape, and grading) of the aggregates.

Each of the valuable characteristics requires the use of water that will support not hinder the

complex reactions that ensure the concrete manufacturing process is successful [13].

It is commonly thought that excessive impurities in mixing water not only may affect setting

time and concrete strength, but also may cause efflorescence, staining, corrosion of

reinforcement, volume instability, and reduced durability [7, 14]. Thus, specifications usually

set limits on chlorides, sulfates, alkalis, and solids in mixing water to ensure minimal adverse

effects to the resultant concrete. Currently, unless tests can be performed to determine the

effect the impurity has on various properties of fresh, hardening and hardened concrete

potable water must be used. Previous research coupling analytical techniques to determine

impurities, with chemometric methodologies to identify their relationships and influence was

used, by Shrestha and Kazama [13, 50]. This research facilitated the characterisation of

impurities as well as an evaluation of water quality and offered conclusions as to its

suitability for final use. These water quality indicators can be categorised as:

Biological: bacteria, algae

Physical: temperature, turbidity and clarity, colour, salinity, suspended solids, dissolved

solids

Chemical: pH, dissolved oxygen, biological oxygen demand, nutrients including nitrogen

and phosphorus, organic and inorganic compounds including toxicants.

Aesthetic: odours, taints, colour, floating matter

28

Measurements of these indicators can be used to determine and monitor changes in water

quality, and determine whether the quality of the water is suitable for use in concrete

production.

The design of water quality monitoring programs is a complex and specialised field and as

such, this study intended to incorporate as many parameters as possible. The range of

indicators measured throughout this research is further detailed in Chapter 2. Although

drinking water is currently the only water accepted by Australian standards for use in the

manufacture of concrete, some waters that are not fit for drinking may be suitable for

concrete [9, 10].

The water quality information gained by research and tests such as this can then be used to

develop management programs and action plans to ensure that suitable water sources are

adopted.

29

2 Methodology

2.1 Sample Guidelines

Water from various sources is often sampled and analysed in order to determine the

presence of any toxic chemicals, potentially harmful pathogens, its fitness for human

consumption, and as an important indicator of any environmental changes [51]. The design

of water quality monitoring programs is a complex and specialised field [52]. The range of

indicators that can be measured is wide and other indicators may be adopted in the future.

The cost of a monitoring program to assess all possible contaminants is often prohibitive, so

resources are usually directed towards assessing contaminants that are important for the

local environment or for a specific use of the water [52]. This water quality information can

then be used to develop management programs and action plans to ensure that water

quality is protected.

Correct sampling, storage and transportation are critical to the accuracy of analysis. When

trying to obtain meaningful and reproducible data it is important that many key elements are

undertaken to ensure that the results are accurate, precise and reliable [51]. Firstly, each

sample must be collected in a manner consistent with the handling and preservation

principles enunciated in Standards Association of Australia (1998) AS/NZS 5667.1:1998

[53].

2.2 Sample Preparation

Sample preparation is a key procedure in modern chemical analysis. By some estimates, 60-

80% of the work activity and operating cost in an analytical lab is spent preparing samples

for introduction into an analytical device [51].

30

Table 2.1 - Specifications for sample preservation

Analyte Container Preservative Transportation

TKN Polythene Acidify with H2SO4 Transport chilled to lab

pH, EC, Alkalinity Polythene Fill to exclude air Transport chilled to lab

BOD, COD, NH3,

NO2, NO3

Polythene Fill to exclude air Store at 1oC to 4oC

Oil & grease Glass 500mL None Store at 1oC to 4oC

Hydrocarbons, other

organics

Glass 500mL None Store at 1oC to 4oC

Br, B Cl, K, Ca, Mg, Na,

F, Si, TDS, Tubidity &

SO4

Polythene None None

Al, As, Ba, Cd, Ca, Cr,

Co, Cu, Fe, Pb, Mg,

Mn, Ni, Se, Zn

Polythene Acidify with 1mL HNO3

to pH<2

None

31

2.3 Equipment and Materials

It is important to use correct containers. Otherwise, the analysis may be adversely affected.

Whilst plastic bottles are normally sufficient for water sampling, other specific analysis types

require different sample bottle types. The most common type of analysis- Drinking water

analysis (DWA) requires 500 mL to 1L polyethylene container. The other most common

analysis of water samples is known as extended water analysis (EWA) and also requires a

500 mL Polyethylene in addition to a 100 mL glass bottle specially preserved for mercury.

For testing of oil and grease and TRH (Hydrocarbons) or other organics, two separate glass

bottles are required as the entire sample (500 mL) is used for each analysis. Specifications

and guidelines for the collection and preservation of water samples are further explained in

Table 2.1. To ensure sample integrity the samples were stored in refrigerated boxes, and

packed with ice whilst being transported to the laboratory for analysis.

2.4 Chemical Preservatives

Once a sample is taken, some of its quality characteristics can change naturally. To keep

these changes to a practical minimum, certain chemicals are added as preservatives. All

containers used throughout this study were pre-treated with the correct preservatives

ensuring the quality and integrity of all samples.

2.5 Sampling Methods and Procedures

The accuracy of a water analysis is very much dependent on the sampling method used and

the time elapsed between sampling and analysis. Firstly it was ensured that the sampling

vessels were cleaned prior to sampling by rinsing the bottle three times in the water to be

sampled.

32

The sample was then collected directly into the sample container, from the centre of the

sampling site, where the velocity is highest. It was important to hold the mouth of the

sampling container well above the base of the channel, to avoid picking up any settled

solids. As the water depth permitted, the mouth of the sample container was held

approximately 10 cm below the water surface. The bottle was then filled to the top with as

little air as possible remaining, and sealed tightly. The cap on the container was screwed

tight and the details on the container label were checked. All samples were properly labelled

with details of the source, date of sampling, sampler‟s name and address and the intended

use of the water. Once the sample had been successfully taken it was placed in plastic bag

that was then sealed with tape. This package was then packed in sample carrier. It was

ensured that crushed ice surrounded the containers.

When collecting the sample, great care must be taken to prevent accidental contamination of

the sterile sample container and the sample itself. To prevent this, these general rules were

followed:

- Do not touch the neck of the container, or the inside of the cap or stopper.

- Do not sneeze or cough into or over the sample container, or near the sampling point.

- Keep the container capped until immediately before filling. Once it is opened, avoid

breathing over it, for example turn your head or hold the container at arm’s length, moving it

upwards and away from yourself while sampling.

- Do not allow sample to become contaminated by any dirt or other foreign matter near the

sampling point; if practicable, remove it before collecting the sample.

- If sampling from a body of water which has enough depth to immerse the sterile container,

hold the container by the sides and keep it nearly upright as you lower it into the water.

Documentation accompanied these samples as specified in Standards Association of

Australia (1998) AS/NZS 5667.1:1998, and APHA (1998) section 1060 [9, 54]. Samples

were also analysed within the maximum holding times specified.

33

2.6 Preparation of Concrete Cylinders

Values obtained through compressive strength tests are often affected by the size and

shape of the specimen, batching, mixing procedures, the methods of sampling,

molding,fabrication technique, sample age, temperature and moisture conditions during

curing [55]. Thus there are specific protocols that should be followed to ensure each cylinder

is cured and tested following established Australian Association of Standards (AS1012.14 -

1991) and ASTM C93108 procedures [56, 57].

In order to properly prepare concrete cylinders for compressive strength testing it is

important to follow some critical procedures. Firstly, it is necessary to use molds that

conform to both the AS and ASTM Standards [56, 57]. These molds come in a variety of

shapes and sizes depending on the testing regime being used. Once the appropriate mold is

chosen, in this case 150mm, a standard rod or vibrator is used to consolidate the concrete,

which ensures no layers form. The completed cylinders are then initially cured at the jobsite

for the first 48 hours. They are then transported back to an accredited laboratory and

immersed in water which is maintained at a temperature of 27°C. After this initial curing the

cylinders are demolded and placed into another curing tank in accordance with ASTM

C91308. The mass of the cylinder and cylinder diameter are also recorded at this stage.

Once the cylinders reach the appropriate age, in this case 7 and 28 days, they are then

submitted for compressive strength testing.

34

2.7 Instrumental Analysis

Water samples are often analysed in a laboratory to gain important information. Chemical or

biological composition can adversely affect crops, soils, humans, animals, or equipment. The

accuracy of a water analysis is very much dependent on the sampling method used, as well

as the subsequent calibration and validation of all instruments and results. Thus,

measurements of specific indicators can be used to monitor changes in, water quality, and

determine whether the quality of the water is suitable for its intended use.

It is imperative to rely on testing fundamental properties of concrete in both its fresh and

hardened state. In order to successfully achieve this, concrete is typically sampled whilst it is

being poured, and testing protocols require that samples be cured under laboratory

conditions ensuring the reliability and consistency of results. The hardened compressive

strength as well as the durability of concrete and its slump, often referred to as workability,

all affect the overall quality of the hardened concrete, standard concrete tests measure the

plastic properties of concrete prior to and during placement .

For most analyses (High Performance Liquid Chromatography, Gas Chromatography,

spectrophometery, etc.), the sample must be properly prepared in solution for subsequent

analysis [58, 59]. Other analytical techniques require different sample preparation such as

drying and or sieving and each method is equally important when trying to ensure that the

accuracy and precision are fit for purpose.

In general, calibration is an operation that relates a dependant variable to an independent

variable for measuring a system under given conditions. Calibration includes the selection of

the model (its functional form), the estimation of model parameters as well as the errors, and

the validation and verification of the model.

35

2.8 Instrumentation used for the analysis of water samples

2.8.1 Measurement of pH

pH is a measure of the acidity or alkalinity of a solution. Solutions with a pH less than seven

are considered acidic, while those with a pH greater than seven are considered basic

(alkaline). pH 7 is considered neutral because it is the pH of pure water at 25 °C. pH is

formally dependent upon the activity of hydrogen ions (H+), but for very pure dilute solutions,

the molarity may be used as a substitute with some sacrifice of accuracy [60]. The pH

reading of a solution is usually obtained by comparing unknown solutions to those of known

pH, and there are several ways of doing this.

2.8.1.1 Apparatus

The pH was measured using a pH meter. A pH meter consists of a potentiometer connected

electrically to two electrodes, called indicator and reference electrodes [59]. The indicator

electrode contains a membrane of special glass separating two liquids: one is a solution of

known pH, the other the test solution, of unknown pH. The solution of known pH is in

contact with an electrolyte containing AgCl crystals while the reference electrode usually

contains a saturated solution of potassium chloride (KCl).

With the use of a voltmeter, a voltage difference was produced across the membrane,

proportional to the pH difference of the two liquids. This voltage was read using a voltmeter

with high input impedance. The e.m.f of the glass/reference electrode cell was measured

with the use of a 5.5 digit voltmeter, thus sensitivity was high, at + 0.001 pH units.

36

2.8.1.2 Procedure

After both the buffer solution and the water sample were brought to the same temperature

(25oC), the temperature of the sample was then measured and recorded so that the

temperature compensation control on the pH meter could be set. The electrode was then

rinsed and immersed in the sample. When the e.m.f reading stabilised a reading was taken.

2.8.2 Measurement of Relative Alkalinity

Acidity and alkalinity measurements are used to assist in establishing levels of chemical

treatment to control scale, corrosion, and other adverse chemical equilibria. In all of these

test methods the hydrogen or hydroxyl ions present in water by virtue of the dissociation or

hydrolysis of its solutes, or both, are neutralized by titration with standard acid (alkalinity).

2.8.2.1 Apparatus

In order to determine the relative alkalinity of the water samples using titration, it is

necessary to have a burette, volumetric pipette, graduated cylinder, digital balance and

Sulfuric acid titrant.

2.8.2.2 Procedure

Once the samples were correctly filtered and the pH system calibrated the electrodes,

sensors, beaker, stir bar, delivery tube were washed with deionized water. Then, a clean dry

burette was filled with 0.01639N sulfuric acid titrant and the selected volume of sample was

transferred to a clean beaker. Then using a magnetic stirrer the sample is made

homogenous. Next, the pH and temperature sensors were rinsed with deionized water. Once

the sensor has been inserted into the beaker, measurement of the initial pH and temperature

was conducted.

37

Once this has been completed titration can begin. While stirring the sample slowly and

continuously the pH is measured after each addition of titrant, ensuring that 15 to 20

seconds after each addition is allowed for equilibration, before recording the pH. The titrant

was cautiously added drop by drop in 0.01 mL increments.

It was then necessary to use the following equations to calculate alkalinity and carbonate

species from inflection points with 0.01639N sulfuric acid:

Alkalinity (mg/L as CaCO3) = 1000/mLs ´ (0.8202 ´ mLa) ´ CF

2.8.3 Measurement of Electrical Conductivity

Electrical conductivity of water is used as an indicator of its salinity and its concentration of

dissolved salts. Conductivity of a solution is found by measuring the resistance of the

solution inside a cell of known dimensions. That is, a water samples ability to conduct an

electric current when an electrical potential difference is placed across a conductor is

measured. It is the movement of the charges that gives rise to an electric current which is

then recorded. The conductivity σ is defined as the ratio of the current density to the electric

field strength.

2.8.3.1 Apparatus

Electrical Conductivity of the water samples was determined using a sensor consisting of

two metal electrodes which protrude into the water. A constant voltage (V) is applied across

the electrodes. An electrical current (I) flows through the water due to this voltage and is

proportional to the concentration of dissolved ions in the water. The more conductive the

water is, the higher the electrical current reported which is measured electronically. The

measuring system used controlled the voltage, frequency and current density so as to

minimize errors due to electrode polarization and capacitance.

38

2.8.3.2 Procedure

Before measurement could begin calibration was undertake. To do this, the standard

solution at (25oC) was poured into two containers the electrodes were rinsed with deionised

water and immersed in the calibration solution of known conductivity. The electrode was

then removed and rinsed with deionised water and placed into the sample 1 minute. Once

the voltage meter had stabilised a measurement was recorded.

2.8.4 Measurement of Total Dissolved Solids

The two principal methods of measuring total dissolved solids are gravimetry and electrical

conductivity. Gravimetric methods are the most accurate and involve evaporating the liquid

solvent to leave a residue which can subsequently be weighed with a precision analytical

balance (normally capable of .0001 gram accuracy) [61]. This method is generally the best,

and was utilised for this study.

2.8.4.1 Apparatus

The process of Gravimetry requires the drying and filtration of the water sample. For this

analytical balances were used. The analytical balance is the most accurate and precise

instrument in an environmental laboratory. Objects of up to 100 grams may be weighed to 6

significant figures. Volumetric glassware was also used and is accurate to no more than 4

significant figures.

2.8.4.2 Procedure

Each sample of water contained both dissolved and suspended solids and was therefore

treated using both filtration and evaporation. First, the sample was dried to a constant,

reproducible weight by drying in a 103° C to 110° C oven for 1 hour and allowed to cool to

room temperature in a desiccator. It was then weighed, and heated again for about 30

39

minutes. The sample was cooled and weighed a second time. The procedure was repeated

until successive weights agreed to within 0.3 mg. This weight was then recorded.

2.8.5 Measurement of Chloride

To detect chloride ions present, Ion-exchange chromatography is employed. Here retention

is based on the attraction between solute ions and charged sites bound to the stationary

phase. Ions of the same charge are excluded. Some types of Ion Exchangers include: (1)

Polystyrene resins- allows cross linkage which increases the stability of the chain. Higher

cross linkage reduces swerving, which increases the equilibration time and ultimately

improves selectivity. (2) Cellulose and dextran ion exchangers (gels)-These possess larger

pore sizes and low charge densities making them suitable for protein separation. (3)

Controlled-pore glass or porous silica. In general, ion exchangers favour the binding of ions

of higher charge and smaller radius.

2.8.5.1 Apparatus

Class A volumetric flasks were used. It was necessary to have an Ion chromatograph, with

an auto sampler. In this case a Dionex AS50 (AS-50) Autosampler was used. Class A

volumetric pipettes, and Vials, were also used. Certified anion standard reference solution,

containing 100-ppm chloride and sulfate was used as the reagent. Deionized or distilled

water, and Sodium bicarbonate eluent concentration, were also required for the ion

chromatograph.

2.8.5.2 Procedure

First, 50 ml of the filtered sample was transferred into a 500 ml volumetric flask and diluted

to the mark then shaken to ensure a homogenous mixture. Next, the ion chromatograph was

used to obtain a stable baseline using the eluent. Once a baseline was obtained, samples

40

were poured into properly labelled sample vials. Then, one prepared standard and one

deionized water blank were run after every four to five samples to check the accuracy of the

chromatograph. The samples were analysed using the ion chromatograph to determine the

concentration of the chloride ions.

2.8.6 Compressive strength Analysis of concrete samples

In most cases the type of construction initially chosen will have sufficient design and material

strength data available to satisfy one that the method and type of construction chosen is

suitable for the structure to be built. Material testing and practice plays a vital part to the

integrity of the material and the satisfaction of all parties concerned in the production of

concrete. In order to successfully achieve this, concrete is typically sampled whilst being

placed, and testing protocols require that samples be cured under laboratory conditions

ensuring the reliability and consistency of results.

Figure 2.1 Testing Apparatus used to determine Compressive Strength

The hardened compressive strength affects the overall quality of hardened concrete [10].

The compressive strength is measured by breaking cylindrical concrete specimens in a

compression testing machine [36, 62].

41

2.8.6.1 Apparatus

The compressive strength tests were conducted using an instrumented hydraulic ram to

compress a cylindrical or cubic sample to failure [57]. The loading rate on the hydraulic

machine should was maintained in a range of 0.15 to 0.35 MPa/s during the latter half of the

loading phase [36].

2.8.6.2 Procedure

The concrete was sampled and tested in accordance with AS 1012.1-1993 by an approved

NATA registered laboratory and the results are provided to the local government [57]. The

specimens were centred on the testing machine and loaded to complete failure. The failure

pattern was observed, with vertical cracks through the cap or a chip off the side indicating

improper load distribution. In order to comply with the strength requirements of a job

specification no single strength test should fall below f‟c by more than 3.45 MPa.

Concrete compressive strength requirements can vary from 17MPa for residential concrete

to 28 MPa and higher in commercial structures. The compressive strength of the concrete

(f‟c) is given by:

F’c = P/A Equation 2.1

Where P is the load at which the cylinder failed (MPa)

A is the cross-sectional area of the cylinder (mm)

The results of this compressive strength test are then immediately passed onto the

appropriate persons for further analysis. It should also be noted that ASTM C 1077 requires

that laboratory technicians involved in testing concrete must be certified.

42

2.9 Multi Criteria Decision Making Methods

2.9.1 Chemometric Analysis

An approach to analytical chemistry based on the idea of indirect observation, modern

chemometrics involves the process of taking measurements related to the specific aim of a

project, and subsequently inferring the value of a property of interest through mathematical

model [3, 5]. Primarily concerned with the achievement of meaningful data and the extraction

of useful information from that data, Massart best defines chemometrics as a “chemical

discipline that uses mathematics, statistics and formal logic [11]

To design or select optimal experimental procedures;

To provide maximum relevant chemical information by analysing chemical data; and

To obtain knowledge about chemical systems”

The introduction of multivariate calibrations enables scientists to model the relation between

a dependant variable and measured independent variables for increasingly intricate mixtures

[63]. Multivariate calibration allows for the simultaneous analysis of several measurements

from several samples or specimens. Once the data has been measured it is calibrated and

then predictions are made based on the calibration. However, in order to optimise the quality

of collected data, data pre-treatment techniques including the optimisation of experimental

parameters, design of experiments, calibration, signal processing are often utilised [64]. It is

also possible to use techniques to extract the best possible information from these data

including statistics, pattern recognition, modelling and structure-property-relationship

estimations. Chemometric methodologies including the removal of gross outliers,

linearisation of data, increased sample and variable selection as well as calibration allowing

for successful multivariate processing [11]. These techniques also ensure accurate, reliable

and relevant information is gathered, thus improving measurement quality.

43

Raw data are normally arranged in matrix form with rows representing objects i.e.: samples,

spectra, chromatograms etc, and columns being variables e.g.: different chemical, physical

or biological characteristics of objects, spectral frequencies or wavelengths, time, etc [65,

66]. The original data matrix compiled for analysis during this study contained data

pertaining to measurements taken in relation to water quality at three specific concrete plant

locations throughout South East Queensland (Appendix A). This research endeavours to

illustrate how chemometrics has advanced parallel with advancing analytical instrumentation

and computational capability by utilising both common and uncommon chemometric

techniques to analyse data pertaining to water quality.

Following pre-treatment, matrices were then imported into Sirius version 7.05, © Copyright

Pattern Recognition Systems AS, Bergen, Norway, 1987-2005.

2.9.1.1 Pre-treatment methods

Data pre-treatment is required in order to homogenise the data. Otherwise variables that

have a larger value will be the most significant within models [67]. The independent

variables differ in size, it is important to remove the influence of size [68]

Standardisation: The data matrix is column standardised, which means that each cell in a

given column is divided by the standard deviation of that particular column. Thus each

variable will now be of equal weighting with a standard deviation of one. This technique is

useful because many data analysis tools place more influence on variables with larger

ranges.

Normalisation: In normalisation, each value in a scan is divided by the sum of values in that

row, so that the total for each row becomes equal to one. This is performed in order, thus the

concentration between successive data points are removed.

44

Mean Centre: It is common to centre data such that each variable (column) in the analysis

has a mean of zero, and thus only the variation in the data is examined. Mean centring

allows the user to identify differences in the data more easily.

Auto Scale: This technique combines mean centring with standardisation resulting in all

dimensions becoming unit-less. Consequently scale does not dominate and each variable is

given an equal chance to contribute to the model in the exploratory data-analysis

techniques. Scaling of data is used if there are many variables in order to secure numerical

stability of the algorithm. When the results are to be interpreted, they are scaled back to the

original data in order to get correct units and interpretation. Thus, from a theoretical point of

view, scaling of data can always be used. It gives more precise solutions and prevents

numerical instability of the algorithm.

2.9.1.2 Principal Component Analysis

Belonging to the broader field of Factor Analysis [69], Principal Component Analysis aims to

reduce the number of variables on which the objects of a dataset were measured and detect

structure in the relationship between the variables. PCA achieves this by using linear

combinations of the original variables and is arguably the most common pattern recognition

method [70].

To apply this method, the data must first be arranged into a data matrix with the selected

variables defining the columns, and the rows referring to the sample measurements

(spectra, chromatograms): each row is called an object [11]. At this stage, this data array is

called the „raw matrix‟.

45

Used to simplify a dataset PCA is a qualitative multivariate data reduction method. The use

of PCA allows the number of variables in a multivariate data set to be reduced, whilst

retaining as much as possible of the variation present in the data set. This reduction is

achieved by taking variables X1, X2,…, Xp and finding the combinations of these to produce

orthogonal principal components (PCs) PC1, PC2,…, PCp, which are uncorrelated [71]. The

lack of correlation is a useful property as it means that the PCs are measuring different

"dimensions" in the data allowing us to expose the patterns of association that exist in many

data sets. Often, due to the high number of variables and objects within a matrix the

relationships between samples can be difficult to discover, thus the use of PCA emphasises

the natural groupings in the data, highlighting those variables which most strongly influence

those patterns.

Essentially PCA is a method for data transformation. After this, the data is represented on

new axes, principal components (PC), and chosen according to a linear model given by the

equation:

PCjk = aj1xk1 + aj2 xk2 +…+ajn xkn Equation 2.2

Where PCjk is the score for object k on component j

aj1 is the loading of a variable I on component k

xk1 is the measured value of a variable i on object k

n is the original number of variable

46

Figure 2.2 Example of Principal Component Analysis

By reducing multidimensional datasets to lower dimensions for analysis PCA transforms the

data to a new coordinate system such that the greatest variance by any projection of the

data comes to lie on the first coordinate (called the first principal component, PC1). With

subsequent coordinates representing progressively less variance. PCA can be used for

dimensionality reduction in a dataset while retaining those characteristics of the dataset that

contribute most to its variance, by keeping lower-order principal components and ignoring

higher-order ones.

The main advantage of PCA is a decrease in dimensionality, without any loss of data

variance, and it reveals which variables affect the data distribution and relationships. Figure

2.2 illustrates the correlation between the original data and the treated data. The figure

shows that if we take a normal plot (x vs. y), and draw our line of best fit (known now as

PC1), it lies in the direction that captures the most variation among the original points. The

variance that remains unexplained by the first axis can then be explained by a second axis

(known as PC2) that explains the greatest part of the remaining variance, while at the same

time is orthogonal in position to the first axis.

47

Once construction of a PCA plot is complete a score for each principal component is

calculated, determining the position of the object on the given principal component. Each

score describes the coordinates of the sample relative to the PC axes which is commonly

plotted against the next PC values giving a scores plot. A scores plot, as seen of PC1 and

PC2, highlights the largest combination of data variance.

Another useful way to further analyse the results produced in PCA, is to construct a loadings

plot which unlike scores, are dependent on the variables. Loading values are the cosine

angle between the PC and the original variables, and subsequently describe how much the

original variables are related to the new PC axes [71]. Variables that have a high positive or

negative weighting (value close to 1/-1) have an angle close to 0 or 180 degrees. This

means the variable lies parallel to the PC and contributes much variation for a given PC,

while low loadings values indicate the variable is perpendicular to the PC and does not

contribute to the variance described by that PC.

A visual representation highlighting which loadings influence which objects is called a biplot

which effectively displays the relationship between the objects and variables. A biplot is a

plot in which the axes are scaled to include the scores coordinates as well as the loadings

values.

2.9.1.3 Classification:

Classification is one of the principal goals in pattern recognition. It seeks to determine if an

object can be divided into certain classes, based on their values for certain variables [72].

Methods can be divided into supervised and unsupervised types. A supervised approach

requires a training set, containing samples of known origins and classification to be analysed

to create a model [73]. In unsupervised classification no training set is required.

48

2.9.1.4 Soft Independent Modelling of Class Analogy (SIMCA):

Developed by Wold and co-workers [74], SIMCA describes the class structure of a dataset.

A form of „soft modelling‟, where classes may overlap, allowing objects to belong to multiple

classes, it has an important role in the history of chemometrics [75]. A subset for each class

is created and a separate principal component analysis is executed for each class within the

training set [74]. Classes are modelled by one series of linear structures depending on the

number of components required to reproduce the class data [76]. Unknown samples are

then compared to the models and assigned a membership value in relation to their analogy

to the training sample [67].

After a PCA model is built for a subset, a decision on how many PC‟s are significant for each

class must be made. It is important to note that each class may be described by a different

number of PC‟s [75]. Each PC is calculated in turn, and then variables are removed

randomly. If the principal components remain the same (give or take) then the model is

judged to have converged [73].

Classification in SIMCA is based on how well an object fits the class model [74]. Residual

Standard Deviations (RSD) can be calculated for each object in or outside the class once

PCA has been performed. This value indicates how well an object is explained by a given

class. A critical RSD value, RSDCRIT, defines the class boundary at a chosen level of

significance [77], and hence is a measurement of class membership. The larger the RSD

value, the greater the difference between the objects and the model.

SIMCA is a powerful method for analysing multivariate data. Although it requires a greater

knowledge of mathematics and statistics, it is still a common choice [74]. This is due to the

fact is it able to handle asymmetric cases. It is simply developing a PCA model for a class

and the unknowns are compared with the model. SIMCA is also able to recognise outliers

and assess compactness of category clusters [74].

49

2.9.1.5 Fuzzy Clustering

It is often necessary in chemometrics to segregate a series of data into homogenous

classes, thus allowing the data to be compressed from a large number of samples into a

small number of representative clusters. Dividing data according to partitioning algorithms

allows similar data objects to be assigned to the same cluster whilst dissimilar data objects

belong to different clusters.

The classification is performed with the aid of a membership function which may be

specified. For example:

m(x) = 1- c x - ap Equation 2.3

Where a and c are constants and p is positive

However, in real applications there is very often no sharp boundary between clusters so that

fuzzy clustering is often better suited for the data. Class membership between zero and one

are used in fuzzy clustering instead of crisp assignments of the data to specific clusters. The

membership threshold can easily be calculated as:

Threshold = 1/n Equation 2.4

Where n = number of clusters

Thus if a matrix is classified into 2 clusters, a function >0.5 (above the threshold) has class

belonging. In fuzzy clustering, the data points can belong to more than one cluster and

associated with each of the points are membership grades which indicate the degree to

which the data points belong to each of the different clusters.

50

Table 2.2 Preference functions in PROMETHEE

Function Shape Threshold

Usual

No threshold

U shape

Q threshold

V shape

P threshold

Level

Q and P thresholds

Linear

Q and P thresholds

Gaussian

S threshold

51

2.10 Multicriteria Decision Making (MCDM)

Extracting information from multivariate chemical data using tools of statistics and

mathematics, chemometric analysis is typically used to explore patterns of association in

data or to track properties of materials on a continuous basis and to prepare and use

multivariate classification models. Combining chemometric and MCDM methods can

maximise information derived from data [78]. MCDM methods can offer partial pre-ordering

as well as full ordering/ranking of objects. Decision making supports methodologies with the

capacity and flexibility to offer diverse modelling options.

Previous studies have focused on the selection of the most appropriate MCDM method. Al-

Shemmeri et al.[79] evaluated the performance of 16 different MCDM methods, assessing

multiple criteria, finding PROMETHEE came out as the best performing method, although it

is often useful to apply more than one MCDM method to find the compromise solution to the

problem. This is often achieved by partnering the PROMETHEE methods with GAIA, a

modified biplot presentation of the data matrix [80].

For this project a Gaussian preference function was selected, using the standard deviation of

each column as the preference index. The Gaussian threshold S is a middle value that is

only used with the Gaussian preference function. The six preference functions available in

PROMETHEE are represented in Table 2.2 [81]. In order to promote discrimination between

the variables each principal component was set to maximise the standard deviation between

the data. Each PC was given a weighting equal to 1.

For MCDM applications in this project scores matrices (object vs. principal components)

from PCA analysis were imported into the commercially available software package,

Decision Lab 2000, © copyright, Visual Decision Lab Inc. Montreal, Canada, 1998-2003.

52

2.10.1 Preference Ranking Organisation Method for Enrichment Evaluation

(PROMETHEE)

PROMETHEE is a non-parametric method applied in Euclidian space to rank objects [63].

Each variable in the raw data matrix is set to maximise or minimise and then the data array

is converted to a difference, d, matrix similar to that of Table 2.3, where a1 , a2 , … ai , … an

are n potential alternatives and f1 , f2 , … fj , … fk are k evaluation criteria. Each evaluation

fx(a2) must be a real number [82]. This is achieved for each criterion by comparing all values

pair wise by subtraction in all possible combinations. Preference indices are computed for

each d value for each object with the use of mathematical preference function selected

independently for each variable.

Table 2.3 – sample matrix

Once this table has been completed, PROMETHEE requests additional information. For

each criterion a specific preference function must be defined. This function is used to

compute the degree of preference associated to the best action in case of pair wise

comparisons [83]. That is, a preference function P(a,b) defines how much outcome „a‟ has to

be preferred to outcome „b‟. In a preference function one plots P(a,b) against the difference

„d‟ of the two outcomes for some criterion.

F1 F2 … Fx

A1

A2

Fx(a2)

Ax

53

π (a, b) =

k

j 1 wj * Pj(a, b) Equation 2.5

Where

k

j 1 wj = 1 Equation 2.6

k is the number of criteria, and wj is the weight of each criterion. The values for π(a, b) are

between 0 and 1, and indicate the global preference of a over b.

The next steps involve calculating the positive and negative preference flows, + and -.

The former indicates how each object outranks all others, while the latter explains how each

is outranked by all others[78]. The higher + and the lower , the better the object.

+ (a) =

Ax π (a, x) Equation 2.7

(a) =

Ax π (x, a) Equation 2.8

Six possible shapes of preference functions are available in the Decision Lab 2000 software

(Table 2.2). These are described for instance in Brans et al (1986).

As explained in the equations above, PROMETHEE & GAIA calculates positive and negative

preference flows for each alternative [12, 63]. From the individual preference indices, the

global preference can be computed for each object and the positive and negative preference

flows, are calculated from these indices. The former indicates how each object out ranks all

others and the latter how each object is outperformed by all others. These outranking flows

are compared and produce a partial pre-order according to three rules:

1. One object is preferred to another,

2. There is no difference between the objects,

3. Objects cannot be compared.

54

It is rule (3) that gives rise to the alternative objects of the rank. This type of ranking is called

PROMETHEE. Based on these flows, the PROMETHEE I partial ranking (+,-) is obtained.

PROMETHEE I does not compare conflicting actions [63].

On the other hand PROMETHEE II provides a complete ranking (). It is based on the

balance of the two preference flows. Both PROMETHEE I and II help the decision-maker to

finalise the selection of a best compromise. A clear view of the outranking relations between

the alternatives is obtained.

2.10.2 Geometric Analysis for Interactive Aid (GAIA)

Since the assignment of weights to the different criteria is a crucial problem for MCD

methods, a sensitivity analysis is therefore highly recommended [83, 84]. The easiest way to

do this for PROMETHEE is to apply GAIA which is a visualizing method for PROMETHEE.

GAIA provides a graphical view of the criteria, the locations and the decision axis for full

ranking. If the criterion is oriented in the same direction as a cluster of objects, they are

correlated, and conflicting criteria have vectors in the opposite direction [58]. The method

decomposes the net outranking flows as follows:

(a) = Ax

k

j 1 [ Pj (a, x) – Pj (x, a)] * wj Equation 2.9

In addition to the representation of the alternatives and criteria, the projection of the weights

or decision vector in the GAIA plane corresponds to another axis (π, the PROMETHEE

decision axis) that shows the direction of the compromise resulting from the weights

allocated to the criteria. Thus, the projection of the vector can follow the order of complete

PROMETHEE ranking. Hence its interpretation as the decision axis for complete ranking. If

the axis is short the criteria are in conflict. If the axis is long, the best objects will be those

found in this direction and as far away from the origin as possible.

55

3 Compilation of baseline data for water quality

3.1 Washout Waters – Building a baseline

Water is an integral constituent of concrete, and a shortage of potable water has resulted in

the search for possible alternatives [8]. Almost any natural water that is drinkable and has no

pronounced taste or odour may be used as mixing water for concrete, but recent continuing

draught conditions have increased its cost. However, the substitution of potable water with

another source has many associated problems and risks that must be eliminated in order to

ensure the quality and performance of concrete remains unchanged.

One of the most common potential sources of water is the washout water obtained from the

cleaning of the agitator concrete bowls. The resultant mixture is a highly liquid/diluted

concrete, which is deposited into “above ground” washout pits. These then drain into a

series of “in ground” settlement pits. The water from these is recycled in a closed circuit to

wash out other agitator bowls. This study intends to determine the suitability of the wash

water as mixing water in concrete.

There is little detailed knowledge on how the properties of concrete are influenced by the

batch water type and proportion in the concrete mix [1, 5-7, 85]. Also, there is little

knowledge of the effects of different water sources used in concrete mix design, and as such

the identification of potentially useful water sources has become an important issue. The aim

of this project first required an understanding of the water currently in use throughout South-

East Queensland for the production of concrete. Thus, the first stage of this project was to

construct a baseline of the quality of water currently being used in a cross-section of

industrial plants, which were chosen to encompass all water treatment strategies currently

employed by Readymix.

56

3.2 Concrete Plant sites throughout SE-Qld

The following concrete plants were used to provide recycled waters for the baseline model.

Usually these plants consist of a number of water purifying processes, each aimed at filtering

any used water in order for it be reused and recycled. The plants chosen for this study offer

a cross-section of process varying from simple (Murarrie) to more complex (Southport).

3.2.1 Southport Concrete Plant

Southport – Concrete trucks are routinely washed out using potable tap water. The resultant

run off, commonly referred to as washout water, is directed towards a stirrer pit then passed

through a series of 6 settlement ponds. Once it has passed through these two purifying

processes it is pumped to a drawing tank where the water can be reused for the cleaning of

trucks, wetting of aggregates to prevent the effects of wind etc.

3.2.2 Beenleigh Concrete Plant

Beenleigh - Any water produced on the site is directed to the first of a series of six settlement

ponds, which act to reduce the amount of solids and waste products in the washout water.

3.2.3 Murarrie Concrete Plant

Murarrie - A very simple process of directing all washout water and other run off towards a

stirrer pit, which ensures all solids remain suspended in the water. Thus, there is no real

purification process utilised here.

57

Table 3.1 Average Values over 10 week period for each plant and Tap Water compared to the Tolerable Limits

Murarrie Southport Beenleigh Tap water

Tolerable Limits

pH 8.1 8.5 8.7 8.9 9.01

Residual Alkali 0.8 1.0 0.8 1

Hydroxide Alkalinity (CaCO3) 4320.0 626.0 2580.0 0.5501

Hydroxide as OH 1386.0 197.9 824.2 0.0182

Carbonate Alkalinity (CaCO3) 3600.0 812.0 1928 0.5833 10004

Carbonate as CO3 4380.0 911.5 2190.9 0.732

Bicarbonate Alkalinity (CaCO3) 11.5 7.53 4.77 53.1 4004

Bicarbonate Alkalinity (HCO3) 14.0 9.68 5.82 65.0

Free Carbon Dioxide 0.11 1.0 0.9 1.1

Free Carbon Dioxide at pH5 0.43 0.20 0.26 0.13

Total Hardness 770 956 324 53

Calcium Hardness 4234 946 3240 40

Magnesium Hardness 6.2 1.5 2.2 1.3

Total Organic carbon 38.6 17.9 20.1 5

Actual Sum -40.5 0.41 -20.9 0

Sum Anions 83.6 23.7 53.9 1.9

Sum Cations 123.4 23.3 73.5 1.9

Aluminium 97 87 86 58

Iron (Fe) 93 79 87 65

Calcium 305 32 33 16 Combined total <20003

Magnesium 19.5 5.6 2.3 3.1

Potassium 83.1 37.9 76.8 2.1

Sodium 168.3 72.7 314.8 16

Alkalinity Total 3565 813 1928 53 10005

Chloride 258.8 89.8 199.2 18 10005

Total Nitrogen 20.8 63.8 7.3 2.7

BOD5 17.5 10.3 11.1 2

Sulphate 322.2 235.1 305.2 12 30005

Conductivity 1291 874 1870 210

Total Dissolved Solids 2596 1170 2060 160 30005

1 The CIRIA Guide to Concrete Construction in the Gulf Region. Published in 1984, C.I.R.I.A. (London)

2 G.R. White, Concrete technology (3rd edn.), Hobar Publications, Delmar, Albany, NY (1977).

3 British Standards Institution. 1980. Water for Making Concrete. BS3148. London: Bristish Standard Institution.

4 Mindess S., Young JF, Darwin D. Concrete, 2nd Edition, Prentice Hall, Englewood Cliffs, NJ, 2002.

5 Neville, AM, Properties of Concrete, Wiley, New York, 1963.

6 ASTM C 171-97, "Specification for Sheet Materials for Curing Concrete," Annual Book of ASTM Standards, vol. 04.02, 1998.

58

3.3 Analysis of Baseline Water

It is commonly thought that excessive impurities in mixing water not only may affect setting

time and concrete strength [8, 33, 86, 87]. Thus, specifications usually set limits on

chlorides, sulfates, alkalis, and solids in mixing water to ensure minimal adverse effects to

the resultant concrete. Currently, potable water must be used unless tests can be performed

to determine the effect the impurity has on various properties of fresh, hardening and

hardened concrete.

3.4 Simple Analysis of Baseline Water Sample Results

To gain a better understanding of wash water properties, a number of tests, both physical

and chemical were conducted. By comparing the average results of each of the three

baseline sampling sites with tap water, it is possible to obtain a broad understanding of the

influence of the variables on concrete properties. It was found that the concentration of the

different constituents in recycled washout water did not exceed the threshold limits

suggested by the analysis of tap water (Table 3.1). However, it was clear that the recycling

process and the water product from the Southport Concrete Plant produced better concrete

performance than the water from the Murarrie Plant. As described earlier, the purification

process at Southport involves both stirrer pit and settling ponds, Beenleigh encompasses

settling ponds only and Murarrie uses only a stirrer pit. Results indicated that the use of non-

potable water yields lower compressive strength in comparison to when concrete was made

with potable water.

It was found that pH values and the chloride values of the three wash water samples were

quite high. This indicates that the wash water produced by the plants was alkaline. This

may be due to the presence of lime, salts, calcium silicate and fly ash, i.e all of the

59

components used in the production of cement or the batching of concrete [88]. The presence

of aluminium, calcium and iron at much higher concentrations than the other tested elements

confirms that the chemical composition of washout water is largely accounted for by the

presence of cement and other cementitious materials. Despite the increase in sediment and

cement particles found within the baseline samples, the total dissolved solid content of both

tap water and this washout waste water were within the allowed 3000ppm, indicating that

this water is suitable for use as mixing water in ready-mix concrete.

.

60

Table 3.2 Metal Concentrations from Southport, Beenleigh and Murarrie.

Metal (mg/L) Murarrie Southport Beenleigh

Copper 210.8 200.1 206.4

Zinc 543.5 524.5 537.5

Cadmium 1.4 1.2 1.3

Lead 75.6 70.6 72.7

Aluminium 42626.7 42696.7 42687.6

Nickel 63.4 62.7 63.1

Manganese 1279.3 1262.0 1271.2

Iron 68813.3 67350.0 68712.3

Chromium 105.6 103.4 104.9

Uranium 3.6 3.3 3.5

Cobalt 43.9 43.5 43.6

61

An increase in soluble material within wash water produces additional calcium hydroxide.

This subsequently has a negative effect on the drying shrinkage and the resistance of acid

attack [26]. The higher alkalinity found in washout water increased the thickness of the

duplex film, which is a layer within the interfacial transition zone between cement paste and

aggregate. This causes a weaker bond between the aggregate and the cement paste,

hence yielding a lower compressive strength [1, 26, 27].

Metal analysis (Table 3.2) demonstrates that there is little difference in metal concentrations

between each of the three sample sites. Three metals, aluminium, iron and mangnesia were

present in the samples in far higher concentrations than other metals. This can be explained

by referring to Table 1.1, the typical components of Portland Cement. Alumina (Al2O3), Iron

Oxide (Fe2O3) and Magnesia (MgO) are all major components in cement and thus account

for these three metals being present in such high concentrations. This further confirms the

assumption that the chemical characteristics of wash water are predominantly influenced by

cement and cementitious materials.

While this study indicated that increasing the percentage of sludge water reduces

compressive strength of concrete after curing for 28 days, the compressive strength with all

three waters was 90-103% of tap water [9, 89, 90]. And, according to ASTM C94 standard

provided that the sludge activated waste water yields a compressive strength not less than

90% of normal concrete it is suitable for use in the manufacturing process [27]. In general,

the results showed that while all specimens were within the required limits for compressive

strength, as the recycling process became simpler and less purification occurred (use of

stirrer pit only), the strength of the concrete decreased. Thus, Southport produced optimal

results, and Murarrie produced the least clean water.

These results are interpreted particularly well with the aid of chemometrics.

62

3.5 Chemometric interpretation of Water Quality data

Recent advances in computer and instrument technologies has led to the acquisition of data

in large quantities necessitating the formation of a new approach for solving scientific

problems [71]. The present study endeavours to illustrate how chemometrics has advanced

in parallel with advancing analytical instrumentation and computational capability, by

examining recent applications of chemometrics in the field of water quality and analysis [91,

92]. Thus, through critical review of various applications of chemometrics this work aims to

demonstrate the importance of such methodologies.

As drought ravages countries around the world, water has become an invaluable commodity.

The presence of contaminants, the suitability of recycling and the monitoring of water quality

and the identification of possible water sources has become an extremely important issue

[91]. Application of chemometric techniques to data collected in relation to some of these

problems offers substantial benefits over univariate processing, allowing complex data

matrices to be evaluated and interpreted. The development of new, computer interfaced

instrumentation has provided the chemist with tools that can generate data rapidly, often with

a capability to reliably measure hundreds of variables simultaneously [50, 93].

Shrestha and Kazama successfully applied the use of multivariate statistical techniques to

surface water samples taken from the Fuji river basin in Japan [50]. The large data matrix

generated from this study was subjected to different multivariate statistical techniques to

extract information about the similarities or dissimilarities between sampling sites,

identification of water quality variables responsible for spatial and/or temporal variations in

river water quality. Whilst the well-accepted chemometric techniques used by the above

authors such as cluster analysis and PCA, facilitated the characterisation and evaluation of

water quality, the results obtained by Kokot et al using the more advanced approaches of

63

ranking methods PROMETHEE and GAIA were found to be more informative [84]. This

approach offered better discrimination between water sources and more precise

determinations as to the suitability of water sources for human consumption, this study

highlights the importance of multivariate analysis for use in environmental and industrial

applications [12].

Using the non-parametric multi-criteria decision-making methods PROMETHEE and GAIA,

Kokot et al. successfully analysed the physico-chemical properties of surface water and

groundwater samples, enabling one source of water to be selected in preference to all others

[12, 94]. Determinations as to the suitability of specific water sources are imperative to

ensuring countries, particularly developing countries such as those described in this study

have access to water appropriate for human consumption [94, 95].

Similarly, Beamonte, et al applied various chemometric techniques to data pertaining to the

use of surface water from Spain for human consumption [96]. Treatment of the results

utilising statistical methods such as ANOVA allowed a more detailed comparison and

subsequent interpretation of the results generated by this study [66]. This paper described a

statistical comparison of the quality of water intended for human consumption at two different

locations. While the use of ANOVA highlighted statistically significant differences allowing

conclusions to be drawn as to the suitability of the water sources, Astel et al had more

success combining PCA with variance analysis which allowed a more thorough interpretation

of spatial and temporal variations in water quality [96].

Affording a better understanding of water quality and technical efficiency of the studied

systems allowing identification of solutions to water quality issues, Astel, et al highlight the

importance of monitoring drinking water samples with simple but powerful statistical tools

[97].

64

Chemometrics has also been applied to data regarding acceptable limits of elements,

chemicals and toxins. Vighi et al, Singh et al, and Gomez et al have all utilised statistical

approaches to generate baseline databases on levels, sources and fate of contaminants in

various water sources [98-101]. The data compiled for use in these various studies was

generated with the use of a variety of instruments and analytical techniques. However, due

to the large quantity of measurements, much of the data produced in each of these studies

was difficult to analyse for meaningful interpretation and thus data reduction methods were

employed. Once suitable data subsets were created, numerous common chemometric

techniques including PCA, PLS and SIMCA were applied, generating significant results [100,

101]. Interpretation of the data by multivariate techniques was successful in demonstrating

differences between water sources and quality as well as highlighting any discrepancies

between desired and real values for contaminants, toxins and metals. Each of these studies

demonstrates that although advances in computer interfaced instrumentation has lead to the

generation of large volumes of raw data, chemometrics has evolved providing new

approaches for the interpretation of such multivariate data matrices.

Finally, with water becoming an increasingly scarce commodity, recycling has become

essential, necessitating the application of chemometrics to water quality and the potential

use and reuse of various water sources. Both Teppola et al and Aguado et al collected data

from recycled industrial waste waters and subsequently utilised chemometric approaches to

investigate the possibility for the re-use of waste waters in industry [102-106]. Teppola et al

used a combined approach of partial least squares and fuzzy clustering for the monitoring of

activated-sludge wastewater treatment plant. Their method was found to be more effective

than the complex “multiway PCA” methodology employed by Aguado et al [76, 107-109].

65

Table 3.3 Readymix Water Specification Guidelines

*Refer to AS1379-1997

1 ASTM D 512-B. 2004. Standard Test Methods for Chloride Ion in Water.

2 ASTM D516-90 2004 - Standard Test Method for Sulfate Ion in Water.

4 ASTM C114 or EN 196-21 2004 – Test Methods for Chemical Analysis of Hydraulic

Cement

Impurity Maximum

Concentration

Test Method

* pH >5.0 AS 1580.505.1

1 Chloride as Cl 1000 ppm ASTM D512

2 Sulfate as SO4 3000 ppm ASTM D516

* Total Dissolved Solids 50 000ppm ASTM C1630

4 Alkalies as (Na2O + 0.658 K2O) 1500 ppm ASTM C114

66

Utilisation of various chemometric techniques allowed the continued monitoring and analysis

of recycled water for use in industry, facilitating the monitoring of water quality of recycled

wastewater intended for reuse [106]. Monitoring of water quality from water sources such as

those studied in each of these papers is important to ensure that all standards and

guidelines governing water quality are met.

Continued research in the field of chemometrics will further contribute to the design of new

types of instruments, generate optimal experiments that yield maximum information, and

catalogue and solve calibration and signal resolution problems [94, 110]. As the sustained

development of computer software provides the means to convert raw data into information,

chemometrics is able to utilise this information, converting it into knowledge that can be used

to improve measurement quality.

3.6 Chemometric Analysis of Baseline Samples

3.6.1 Principal Component Analysis

As detailed in (Sample Guidelines 2.1) each sample was collected in accordance with the

guidelines set out in Standards Association of Australia (1998) AS/NZS 5667.1:1998, and

APHA (1998) section 1060 [9, 54]. Readymix had previously constructed a set of internal

guidelines for measuring water quality (Table 3.3). Consequently, analysis of the results was

first undertaken using only these variables, with all objects as well as the compressive

strength measurements. Before the simplified data matrix (Table 3.1) was submitted to PCA

it was standardised and mean centred.

67

Figure 3.1 Biplot with baseline sample results with IRMV and compressive strength results

Where : (s) Southport, (m) Murarrie, (b) Beenleigh

Numbers represent the sample week eg: 1s is the first sample from Southport

68

Figure 3.1 displays a biplot of the relationship of the objects and variables. The objects

appear to group in order of water cleanliness. That is, Southport (s) having the best

purification process, followed by Beenleigh (b) and finally Murarrie (m) having the least clean

water due to its simplest purification process. Murarrie and Southport both group individually

and have negative scores with respect to PC1, while Beenleigh groups in the positive region

with respect to PC1. Southport and Murarrie are further separated with respect to PC2 with

Murarrie with positive score while Southport has negative scores. Most interestingly, on this

biplot are the positions of the crush 7 and 28 day compressive strength vectors. They appear

to be roughly perpendicular to the other variables, which implies that there is little correlation

between the two sets of loading vectors i.e., the variables are independent of the physical

crush properties or the water quality does not seriously affect the performance of the

concrete provided that the water lies within the specified variable thresholds [89].

It is also of interest that Cl, TDS and SO4 have the greatest effect on the Murarrie samples.

An inspection of the raw data matrix (Table 3.1) identifies that results for three particular

variables vary greatly from those of the tap water. Chloride is highly soluble and for this

reason is a common constituent of water. However, chloride ions accelerate corrosion of

metals especially stainless steel even at low concentrations. This supports the hypothesis

that any significant deviation from the accepted threshold limit will adversely affect water

quality and concrete. The levels of chloride found in each of these samples were not high

enough to cause any adverse affects. While the Murarrie samples did not exceed the

threshold for any variable, these three particular variables have much higher concentrations

than tap water.

69

Figure 3.2 PCA Biplot of PC1 vs. PC2 with all variables including compressive strength results

Where: (s) Southport, (m) Murarrie, (b) Beenleigh

Numbers represent the sample week eg: 1s is the first sample from Southport

70

In order to validate the findings of this simplified matrix, further PCA was then performed on

a matrix with all measured variables including the compressive strength (crush) results

(Figure 3.2). Analysis carried out during this study included the measurement of variables

including but not limited to pH, Electrical Conductivity (C), Total Dissolved Salts (TDS(t)),

Sodium (Na), Potassium (K), Calcium (Ca), Magnesium (Mg), Iron (Fe), Copper (Cu), Lead

(Pb), Chloride (Cl-), Sulfate (SO42-), Nitrate (NO3

-), Nitrite (NO2-), Bicarbonate (HCO3

-),

Carbonate (CO32-), Hydroxide (OH-) and total oil and grease (TOG). The total variables

measured were identified in Table 3.1.

The 40x7 auto-scaled objects versus variable data matrix, Table 3.1 was submitted to PCA

analysis. The resulting PC1 versus PC2 score plot (Figure 3.2) explained 61.3% of variance.

When the objects are projected onto PC1 it can be seen that both the 7 and 28 crush vectors

have positive scores. The variables Mg, TOC, Fe, TDS, Cl and pHv also have positive

scores. Whilst TDSalts, FC, CO3 and pH criteria have negative scores along PC1. When

analysis is preformed along PC2 it can be seen that most variables have positive scores with

the exception of 7 and 28 day crush results as well as AS, SAR, pH, BA and CO3 variables.

From this biplot it can be seen that the objects, 5m, 6m, 6b and 10m are all highly negative

along PC1 and it is noted that these samples perform abnormally well. These objects appear

to behave atypically and are situated well away from their expected source group. However,

these objects actually group correctly because at the time of sampling, the source sites

were, for one reason or another, inundated by potable or equivalent quality water as can be

seen by the values in the data matrix. This change in water quality effectively shifted the

membership of the objects from the group associated with poorly clarified water to that

reflecting the well clarified water quality.

71

Table 3.4 PROMETHEE Net Ranking for Southport and Murarrie using Internal Readymix Variables and Compressive strength results

Where: (s) Southport, (m) Murarrie,

Numbers represent the sample week eg: 1s is the first sample from Southport

Water Sample ф net

6m 0.5645

2s 0.3216

1s 0.2965

6s 0.2863

5s 0.1969

3s 0.1544

10s 0.1424

4s 0.065

9s 0.0538

7m -0.0062

7s -0.0108

8s -0.0503

3m -0.1361

9m -0.1641

1m -0.1661

2m -0.1703

4m -0.2511

8m -0.289

10m -0.3622

5m -0.4752

72

It is also evident that the 7 and 28 day crush results are strongly affected by the AS results.

Analysis of the test results Table 3.1 shows that the outliers identified earlier, 5m, 6mb, 10m

have abnormally low results. This indicates that those samples that have results outside the

threshold amounts for any variable, particularly AS adversely affect the compressive

strength results.

Despite the obvious atypical samples the separations seen in this biplot support the

hypothesis that the three sampling sites are ordered according to the water quality i.e.

Southport (s; well clarified water), Beenleigh (b; moderately clarified) and Murarrie (m; poorly

clarified). This discussion is supported by results from a completely independent

chemometrics method of analysis i.e. PROMETHEE ranking (Section 3.6.2).

3.6.2 PROMETHEE and GAIA

As discussed previously (Section 2.10.1), MCDM methods are particularly suitable for

assistance with multi-variate problems. Both PROMETHEE and GAIA were designed for site

selection, thus these two methods were chosen to explore the ranking of water quality

objects and to investigate relationships between the physiochemical criteria and the sites

considered. Like the PCA and Fuzzy Clustering analysis, this PROMETHEE Net Ranking

order was also derived from variables measured over a 10 week period, and therefore, in

principle, may be used as a relative baseline for comparison of any further measurements of

water quality from these or any other sites to be sampled in the future.

Before PROMETHEE and GAIA were carried out on a matrix containing all three baseline

samples, it was decided to use only Southport and Murarrie to confirm that these two

samples groups represent the cleanest and lest clean waters respectively (Table 3.4). This

reference plot can then be used to identify any inconsistencies with the predicted model.

73

Figure 3.3 GAIA plot showing reference variables and baseline sample sites with compressive strength results

Where : (s) Southport, (m) Murarrie, (b) Beenleigh

Numbers represent the sample week eg: 1s is the first sample from Southport

74

The ranking algorithm was applied to the 20 object by 7 variable data matrix. The variables

were modelled as follows (Section 2.10.1):

Preference function: Gaussian

Ranking Sense: Top-down

Weights: 1 for all variables.

This PROMETHEE model shows that the cleanest water, Southport objects rank more or

less as a block (red) while the Murarrie objects form a separate block. Two Murarrie water

samples (6m and 7m) are found amongst the Southport waters. As previously discussed

these two samples were significantly diluted, especially week 6 (6m). The narrow range of

values indicates that whilst the samples group clearly, they are very closely related,

suggesting that there is only small differences in quality. This PROMETHEE results

demonstrates how the object samples may be rank ordered to produces a reference data

set, and how any samples that do not fit into the reference object subsets may be compared

to the reference blocks. Thus, clearly, object 7m displays properties of the Southport

samples but also it compares favourably with the poorer performing waters of that class. On

the other hand, the 6m object suggests by its rank order and index value that it is the

cleanest water sample in the data set. Thus, this Southport/Murarrie sample set may be

used to compare the quality of other waters, e.g Beenleigh and alternative waters (chapter 4)

on a relative basis with the indices indicating on a semi-quantitative scale the quality of each

water object vis-a-vis all others.

This newly presented data matrix has been compared and ordered by the PROMETHEE

method, with each measured object being awarded a position on the relative scale. With

the application of GAIA (Figure 3.3), The GAIA plot demonstrates that the Southport objects

mostly group with positive scores on PC1 and the Murarrie ones with negative scores on the

same PC. Object 6m has the highest score on PC1 and object 7m very low negative one,

75

Table 3.5 PROMETHEE Net Ranking for the Baseline Data using Internal Readymix Variables Only

Where: (s) Southport, (m) Murarrie, (b) Beenleigh

Numbers represent the sample week eg: 1s is the first sample from Southport

Water Sample ф net 6m 0.0576 6b2 0.0366 6s2 0.0362 2s 0.0343

6s2 0.0309 5s 0.0304 1s 0.03

10s 0.0263 3s 0.0198 9s 0.0186 7s 0.016

9b2 0.0146 4s 0.0121 8s 0.0102

3s2 0.0094 9b2 0.0077 7m 0.0072 3b 0.0048

3b2 0.0023 1b -0.0082

10b -0.0099 9m -0.0118 3m -0.0121 2m -0.0144 1m -0.0149 7b -0.0153 2b -0.0177 6b -0.0203 9s2 -0.0215 4m -0.0257

10m -0.0265 8b -0.0282 5b -0.032 8m -0.0381 5m -0.0428 4b -0.0656

76

just mixing into the Southport group (on PC1) with objects 8s and 7s. TDS, Cl, pH and So4

criteria discriminate the Southport water group on PC1 from the Murrarie group. The other

variables RA and 7 and 28 day crush practically do not contribute to the discrimination either

on PC1 or PC2. There is no clear discrimination of the Southport and Murarrie objects with

positive scores on PC2 on the basis of the criteria TDS, Cl and pH but the SO4 vector

strongly discriminates Murarrie objects 1m, 2m, 7m and 9m which were all high on this

variable (Table 3.1).

Consequently, a 36 object x 5 variable data matrix consisting of the Southport, Beenleigh

and Murarrie water samples was analysed by the PROMETHEE method (Table 3.5). The

extracted indices showed that most of the objects with positive values belong to the

Southport group of samples (s). In general, they are, therefore, the preferred objects. The

6m sample which contains high levels of potable water, and is therefore relatively clean is

the preferred object. The week 6 Beenleigh sample (6b2) appears to go against the general

trend, as most other Beenleigh samples are below the Southport (s) group. This can be

attributed to the high level of pure rainwater in that sample as a result of heavy rainfall in the

days preceding its collection.

It can be seen that the 6s object, the (b) samples and the (m) ones have a relatively mixed

order of ranking depending on the exact state of water clarity on the particular sampling

week. However, there is a tendency for the (m) samples to trend to lower values e.g. most

of the (m) objects are below = -0.0118 values, but there are many (b) samples above this.

In this manner, the PROMETHEE Net Ranking order reflects and supports the trend

observed on the PC biplots (Figure 3.1,Figure 3.2), particularly along PC1.

77

Figure 3.4 GAIA plot showing reference variables and baseline sample sites

Where : (s) Southport, (m) Murarrie, (b) Beenleigh

Numbers represent the sample week eg: 1s is the first sample from Southport

78

Once again, the range of the net is between +0.0576 to -

0.0656, which is relatively narrow and suggests that the samples are fairly similar. However,

even in these circumstances the model is able to group the samples, which supports the

clustering seen in PCA analysis (Figure 3.1). The Beenleigh objects are interspersed

through the Murrarie reference subset and also have a block of samples which are very

similar to each other ( range = 0.0077 - -0.0099) and also have a considerable number of

water samples right on the borderline of the Southport and Murarrie sets.

As expected, this PROMETHEE analysis highlights that Southport has the best quality,

cleanest water, followed by Beenleigh and then lastly Murarrie. This indicates to us that the

Southport has optimal water treatment and recycling processes, whilst the stirrer pit used at

Murarrie is not sufficient to ensure that the water is recycled to an acceptable level. It should

be noted, however, as discussed previously that all three sampling sites produced water of

sufficient quality to use in the manufacture of concrete.

Thus, PROMETHEE analysis provided a very useful technique for establishing the relative

order of water quality for the three indentified water plants. This indicates that this is a useful

reference data set, which can be utilised to help classify other waters on the bases of their

quality.

The complex information in each set of samples sampled has been successfully re-

packaged with no loss of information into new variables by means of PCA. Once again, the

data matrix here was subjected to GAIA and this newly presented information has been

compared and ordered by the PROMETHEE method, with each measured object being

given a position on the relative scale. With the application of GAIA (Figure 3.4), tighter

clustering between the each set of samples is observed. Furthermore the GAIA plot reflects

a greater degree of separation between the Southport and Beenleigh samples. This result

79

re-enforces the findings that the Southport has optimal water treatment and recycling

processes, whilst the stirrer pit used at Murarrie is not sufficient to ensure that the water is

purified to an acceptable level.

The GAIA biplot of this dataset is essentially quite similar to that on Figure 3.3 The Southport

and Murarrie water samples are effectively similarly distributed as on this diagram and the

intermediate Beenleigh water samples fall in between the sets. There is no significant

change with the variables contributing to the discrimination of the objects (Figure 3.4).

80

3.7 Chapter Summary

1. Washout water samples collected at three different concrete manufacturing sites

were analysed to assess their quality on the basis of the industry standard Ready Mix

Monitoring Variables. Subsequently concrete cylinders made with the use of these

waters were tested for crush MPa.

2. Analysis of the multivariate data matrix of the crush properties indicated that the

water quality does not seriously affect the performance of the concrete provided that

the water lies within the specified limits.

3. The quality of the waters was ranked on the basis of their multivariate variables and

a baseline relative order was obtained that can be applied on any other water

samples for comparative purposes in the future.

4. Concrete manufactured with washout water appears to have minimal detrimental

effects on concrete strength properties and appears to be a feasible means of

reducing wash water as a waste product by allowing its reuse in subsequent batches.

5. Whilst the three experimental water sites were clearly ranked according to

cleanliness using PROMETHEE analysis, all three were found to be suitable for use

in the manufacture of concrete.

81

4 Analysis of Alternative Water Sources for Comparison

4.1 Location of alternative water source sampling sites

As described in Chapter 3, washout water samples collected at three different concrete

manufacturing sites were analysed to assess their quality on the basis of the industry

standard monitoring variables. Subsequently concrete cylinders were made with the use of

these waters and tested for crush MPa. Analysis of the multivariate data matrix of the

variables and the crush properties with the use of PCA indicated that the water quality does

not seriously affect the performance of the concrete provided that the water lies within the

standard industrial water quality limits. The quality of the waters was ranked on the basis of

their multivariate variables and a baseline relative ranking scale was obtained that was

applied for comparative purposes against a range of alternative water sources.

The recycling and re-use of industrial, urban and agricultural waste materials into new

building material results in economical, technological, ecological and energy saving

advantages. As such, for the purpose of this study, sources of non-fresh water were tested

for use in concrete mixtures. These included sea and alkali waters, lake and bore waters,

waters containing sewerage and industrial wastes. The effects of each element on concrete,

whether beneficial or detrimental will be discussed during this chapter. Samples for this

study were collected from the same location each week to ensure that continuity of the

samples was sustained.

82

Table 4.1 Typical composition of sea water*

Element Amount (gL-1)

Na+ 11.0

K+ 0.40

Mg2+ 1.33

Ca2+ 0.43

Cl- 19.80

SO42- 2.76

A.G. Dickson & C. Goyet.(1994) Handbook of methods for the analysis of the various parameters of the carbon dioxide system

in sea water.

83

4.2 Southport Sea Water

While there have been many studies previously conducted on the possible use of sea water

in the manufacture of reinforced concrete, it was included in this study primarily as a control

measure [110]. Table 4.1 outlines the general composition of sea water.

4.2.1 Southport Treated Effluent

Like sea water, there have been many studies aimed at assessing the viability of effluent as

mixing water in concrete [109]. These previous studies came to the conclusion that treated

effluent is suitable for the manufacture of concrete, provided it is of a certain grade.

The effluent samples, were taken from a controlled environment from a government

approved effluent site. Each week the samples were taken from the same tank to ensure

that the results were comparable.

4.2.2 Kawana Treated Effluent

Similar to those effluent samples taken from Southport, the effluent samples taken from

Kawana were taken from the end of a sewerage line. The samples were taken from the

same place each week.

4.2.3 Coolum Bore Water

Like the other bore water samples, the water collected from here was collected from a tap,

sourced directly from the bore via pipeline. Once again, these samples were subject to

rainfall and runoff during the sampling period.

84

4.2.4 Gympie Bore water

The Gympie bore was linked to the Readymix site via a series of pipelines and as such the

samples were collected from a tap directly linked to this pipeline.

4.2.5 Ipswich River Water

The Ipswich river water samples were collected from a tap that pumps directly out of the

river. These samples were also subject to change due to an increase in fresh, rain water and

runoff.

4.2.6 Murarrie Bore Water

Like the other bore water samples, the water collected from here was collected from a tap,

sourced directly from the bore via pipeline. Once again, these samples were subject to

rainfall and runoff during the sampling period.

4.2.7 Coomera Dam Water

The Coomera dam water samples were taken from a reservoir contained on a Readymix

site. The samples were acquired using an inbuilt pipeline. Pumped from the centre of the

dam, the water passed through a pipeline and was collected from a tap directly linked to the

pipeline. The water samples collected from this site were subject to an influx of fresh water

due to rainfall and runoff.

4.2.8 Coomera Bore water

The Coomera Bore water samples were collected from holding tanks present on the

Coomera Concrete plant. This water was subject to fluctuations in fresh water due to rainfall.

85

Table 4.2 Comparison between Average Alternative Water Source Results and Compressive strength Results with Baseline samples and Tap water

pH RA (ppm)

Cl

(ppm) SO4

(ppm) T D S (ppm)

7 days (MPa)

28 days (MPa)

Tap Water 8.9 1.0 18 12 160 22.5 36.0

Southport (s) 6.5 1.0 89 235 1178 25.8 39.3

Beenleigh (b) 6.1 0.8 199 305 2060 28.5 41.5

Murarrie (m) 5.7 0.8 258 272 2596 26.8 36.5

Coomera bore (cb) 8.2 0.6 51 265 330 27 39.6

Kawana (k) 8.5 1 116 256 425 25 40.3

Ipswich (i) 8.8 0.8 56 198 783 26.2 41.7

Coomera dam (cd) 9.2 0.2 6 102 114 26.4 40.9

Sea water (sw) 7.9 0.0 18000 2500 36335 27 37.5

Gympie (g) 7.8 1.2 26 265 348 25.3 38.5

Murarrie bore (mb) 8.4 0.2 1026 268 1875 25.7 37.3

Effluent (e) 8.2 0.8 293 282 856 25.9 37.1

Coolum bore (cb) 8.2 0.9 61 235 458 26.2 38.3

86

4.3 Analysis of Alternative Water Sources

To compare the nine alternative water sources and the baseline results, each value of the

Internal Readymix Variables for each alternative source was compared with Murarrie and tap

water (Table 4.2). The reasoning behind this approach is that, as outlined in Chapter 3,

Murarrie was the worst performing Readymix plant. However, it was confirmed that as each

measured variable for the Murarrie water was within the allowed thresholds, this water was

suitable for use as mixing water in the manufacture of concrete. Thus, only alternative water

samples which performed as well as or better than Murarrie could be deemed to be within

the above Readymix Internal Monitoring Variable threshold and hence, in principle suitable

for concrete manufacture.

When comparing the average values of each of the alternative water sources over the ten

week period, with the reference standards, the Southport samples are still the cleanest but

the Coomera Bore and Coomera Dam waters both perform better than the Beenleigh and

Murarrie ones. Both the Coomera Bore and Coomera Dam waters are heavily influenced by

outside factors e.g any rain or run off in the surrounding areas helps to mitigate any adverse

affects from factors such as TDS and Relative Alkalinity, and therefore susceptible to dilution

which contributes to their cleanliness. The same can be said for the Ipswich River water,

which generally performed well.

With the exception of seawater, each of the alternative waters had much lower TDS results

than the reference samples. Reduced levels of RA in each of these samples is more likely a

result of the high levels of cementitious particles found in the reference washout water

samples.

87

It was found that the pH of tap water was between 8 and 9 whilst the sea water, Coomera

dam and Gympie bore waters were approximately the same, presumably due to a very small

presence of residual cement particles. It was also found that the total chloride ion [CI-] and

sulfate ion [S042-] in all the alternative waters greatly exceeded the levels found in tap water

but only sea water performed worse than the worst baseline plant, Murarrie. Thus, each

alternative water source, excluding sea water, was well within the acceptable standard limits.

Only sea water performed more poorly than Murarrie (Table 4.2). Therefore, it may be

concluded that each of the eight other waters is suitable for use as mixing water in ready-

mixed concrete. Sea water contained high levels of Cl as well as SO4 and TDS. This is not,

however, unexpected. High salt levels in the sea water samples inflate the levels of both Cl

and TDS readings. Whilst these values are not outside the acceptable limits, it should be

noted that sea water is not suitable for use in reinforced concrete as these findings indicate

sea water is corrosive. The high levels of Cl and TDS did not adversely affect the

performance of the water of either the 7 or 28 day crush results, and thus, this water can be

used to manufacture concrete where reinforcement is not required.

The compressive strength crush results of each of the alternative water sources were also

compared to those of the baseline samples (Table 4.2). The average compressive strength

of the concrete cylinders made with alternative water should not fall to less than 90% of the

average strength of the control cylinders in order to be classed as acceptable. The results

showed that treated effluent increases the compressive strength and setting time when

compared with potable water. The same results were also found for both Southport and

Kawana alternative water sources.

None of the Internal Readymix Testing Variables were at the extreme of any range,

indicating that each of the alternative water sites is generally clean, and thus, also suitable

for the use in the manufacture of concrete.

88

4.4 Analysis according to water type

4.4.1 Sea Water

Previous studies carried out using sea water as mixing water determined that concrete made

with this water had about 6 – 8% lower strength than the fresh water concrete [33]. Also,

while such concrete could have higher early strength than normal concrete, on aging the

strength may fall [111, 112]. This was also true for a study carried out by Neville who stated

that the use of seawater may cause a moderate reduction in ultimate strength [113]. As can

be seen in Table 4.2, both the 7 and 28 day strength results for cores made with sea water

were within the acceptable limits, i.e. reached tolerable compressive resistance.

The pH of surface sea waters was relatively constant at between 7 and 8 which is consistent

with previous studies [110, 113, 114]. The chemical attack of sea water on concrete is

mainly due to attack by magnesium sulphate (MgSO4) [113, 115]. And as can be seen in

Table 4.2, sea water has a very high level of SO4 when compared with tap water. An

increase in sulphate content can lead to an expansion of concrete and subsequent cracking

[1]. Concrete deterioration is apparently due to the loss of part of its constituents; calcium

hydroxide and calcium sulphate are more soluble in sea water and are more readily leached.

Reinforced concrete exposed to sea water is susceptible to its corrosive effects. It should be

noted here, that all concrete specimens manufactured using sea water as mixing water did

not contain steel reinforcing. As such, when compared to tap water the levels of chloride,

total dissolved salts and sulphate are of significant importance. As can be seen in Table 4.2,

the sea water results for each of these variables are much higher than those levels detected

in tap water. The other variable of importance is the total dissolved salts. Sea water, which

contains about 35 000 mg/l dissolved salts or more, has been found to be harmful to the

strength of plain concrete [110,111]. Values for these variables were so high that they

89

exceed the literature threshold values, however, as will be discussed later in this chapter the

compressive strength results were not adversely affected. These results are supported by

previous studies, which have indicated that sea water can be used in mixing plain concrete

without any loss of performance on aging [114]. Risk of corrosion of embedded metals limits

the use of seawater in reinforced concrete.

4.4.2 Treated Effluent

Sewage water originates mainly from domestic sources and comprises 99.9% water and

0.1% organic and inorganic solids in settleable, suspended, and soluble forms [112].

Untreated sewage is a hazard to both public health and to the environment. Therefore,

sewage water is treated in a sewage treatment plant before being discharged into an inland

waterway. With proper water quality control, this treated effluent can also be considered as a

potential water resource for specific applications. While the treated effluent is widely utilised

for irrigation purposes, data is limited for its use in the mixing and curing of concrete.

According to El-Nawawy and Ahmad [116], effluent water is being used to prepare cement

slurry at a cement factory in Dubai [117]. The results of an investigation regarding the use of

reclaimed wastewater for concrete mixing, suggested that such water can be used for this

purpose without any harmful effects [118]. In a similar study conducted by Cebeci and

Saatci, the results (setting time, mortar, and concrete strength test) showed that biologically

treated average domestic sewage is indistinguishable from distilled water when used as

mixing water.

When comparing the effluent samples with Murarrie and tap water on the basis of the

Internal Readymix Variables (Table 4.2), the main discrepancies were identified as the levels

of chloride and sulphate concentrations. It can be seen that with the exception of sea water,

the effluent samples of Kawana and Southport effluent samples had the highest chloride

90

levels. It should be noted however, that the chloride levels were above tap water but were

lower than those levels found in the Murarrie samples. Despite this, the effluent samples and

Murarrie samples were all within the tolerable limit of 1000ppm [113]. It was also evident

through simple chemical analysis using the Internal Readymix Variables that the effluent

sulphate results were higher than the other alternative water samples and tap water, but

lower than Murarrie samples and sea water.

The results discussed throughout this chapter show that the biologically treated domestic

wastewater used in this study is indistinguishable from distilled water when used as mixing

water for concrete. Thus, treated wastewater such as that produced at both Southport and

Kawana should be suitable for mixing concrete provided that the variables used are all within

the tolerable limits given in the standards [9, 54, 89]. The fresh concrete properties and

strength characteristics for concrete made with this mixing water were all similar to those

produced using tap water.

The increased compressive strength results that were observed in this study may be due to

the higher concentration of sodium and the presence of calcium salt in treated effluent, when

compared to potable water. Mindess and Young report that sodium chloride and calcium

chloride may increase early strength while reducing the ultimate strength [119]. Standard

testing methods state that the average compressive strength of the concrete cylinders made

with alternative water should not be less than 90% of the average strength of the control

cubes [57, 89]. This is shown in Table 4.2 where it can be seen that at 7 day compressive

strength results performed equally well as each of the three baseline samples. The crush

results were not adversely affected by the use of effluent.

91

4.4.3 Bore Water

While there have been previous studies using both sea water and treated effluent, there are

no current studies related to bore water for use as mixing water in concrete. However, the

sea water and treated effluent studies help in determining the suitability of bore water for

concrete manufacture. Table 4.2 shows that while the measured levels of both chloride and

total dissolved solids for bore water are higher than tap water, they are within the accepted

literature values and lower than the levels found in Murarrie samples [57]. This is further

demonstrated when the pH and RA results are analysed, showing that the bore water

samples performed better than Murarrie. These results, combined with the 7 and 28 day

crush results indicate that bore water is suitable for the manufacture of concrete.

4.4.4 Dam and River Water

Comparison of Ipswich river water with Coomera dam water shows that the Coomera dam

water performs better. This can be seen in Table 4.2 as results for each of the measured

Readymix Internal Variables for the dam water are much closer to the tap water samples. In

this context these waters actually performed the best of each of the alternative water

samples when compared to tap water.

While Ipswich river water performed worse than that from the Coomera dam, its results for

all of the measured variables were within reasonable limits. Each of the variables was within

the accepted literature variables, thus making it suitable for use as mixing water in concrete

[57]. This is confirmed by the 7 and 28 day compressive strength results which indicate that

there was no significant decrease in the MPa values obtained. The concrete cylinders made

with dam and river water reach acceptable load limits.

92

Figure 4.1 PC1 v PC2 for alternative water source results

Where : (s) Southport, (m) Murarrie, (b) Beenleigh

Numbers represent the sample week eg: 1s is the first sample from Southport

For all other labels see Table 4.2 for abbreviations

93

4.5 Chemometric analysis of alternative water source samples

4.5.1 PCA analysis

This 90 object x 5 variable data matrix was autoscaled where the data was y-mean centred

and column standardised (Figure 4.1) [15, 17]. This means that all dimensions are unitless

so the scale of a variable will not dominate and each variable is given an equal chance to

contribute to the model in the exploratory data-analysis techniques (Figure 4.1) [11, 18].

The PCA Biplot explains 73.7% of the variance and effectively illustrates the relationships

between the variables on the two PCs. Here it can be seen that pH, (residual alkali) RA and

(total dissolved solids) TDS have high positive loadings on PC1, and Cl has a high negative

loading along this PC, i.e as RA and pH lie along the same axis and are opposite to Cl along

PC1. It can be said that the former two vectors are strongly correlated but they are

negatively related to Cl. It is also noticeable that Cl has a relatively small negative loading

on PC2 and the SO4 vector a high one. TDS has a high positive loading along PC2. Also, RA

and pH are approximately orthogonal to both the Cl and TDS vectors, i.e. the RA and pH

variables are independent from Cl and TDS.

Objects such as 4e and 4cb exhibit atypical behaviour being situated away from their

expected source group. When the simplified data matrix for these particular objects (Table

4.2) is further analysed it can be seen that each of the values for the Internal Readymix

Variables was lower than for any of the other weeks. This can be attributed to an influx of

rain water during the week preceding sampling.

94

Figure 4.2 PC1 v PC2 all Alternative and all Baseline samples with compressive strength results

Where : (s) Southport, (m) Murarrie, (b) Beenleigh Numbers represent the sample week eg: 1s is the first sample from Southport

For all other labels see Table 4.2 for abbreviations

(a):Total Biplot: Vectors 7 and 28 (compressive strength) appear perpendicular to vectors on

PC1

(b):Expanded area highlighting the relationships between objects and vectors along PC‟s

95

Next the raw data matrix was then enlarged with the addition of the two physical properties

of concrete i.e. compressive strength which were reported as MPa crush values at 7 and 28

days (Figure 4.2)

This PCA plot has many similarities to the PCA results containing only the baseline samples

and compressive strength results, which is expected. Like that plot (Figure 3.1), the

compressive strength vectors appear to be approximately perpendicular to other vectors,

indicating that they are independent. This plot also shows tight clustering of all of the sample

groups with the exception of sea water (sw). This sample group appears to be highly

influenced by the SO4, TDS and Cl vectors along PC1, which agrees with the chemical and

physical analysis of the samples, which indicated sea water had high levels of Cl, which is

known to affect the TDS and SO4 levels. Interestingly, each of the other water samples

cluster together, and do not show any clear separation with the exception of mb and g which

are largely separated along PC1. This figure also shows that PC2 separates the b sample

group from the others with reasonable discrimination. Samples 4b and 3s2 also appear as

outliers, highly influenced by the compressive strength results (7 days, 28 days) along PC2.

Analysis of the results, (Table 4.2) shows that each of these samples has abnormally high

crush results, explaining their positions.

96

Table 4.3 PCA models for SIMCA

Subset Number of

Objects

PC Variance (%) Total Variance for

all PCs (%)

RSD Critical

Southport 65 63.8, 32.7 96.5 0.45, p=0.05

Beenleigh 65 58.3,36.6 95.9 0.77, p=0.05

Murarrie 50 42.9,47.6 90.9 1.13, p=0.05.

97

4.5.2 SIMCA

Three reference subsets were created using the baseline data previously analysed (Table

3.1, i.e , Southport (clean), Murarrie (least clean) and Beenleigh (intermediate)). However, in

order to build a class model the number of significant PC‟s in that class must first be

determined.

There are a number of approaches to decide how many PC‟s to retain in a SIMCA class

model. The preferred method is based on the use of statistically significant PC‟s. This was

ineffective in the present case because of the relatively small subsets available. Since it was

impossible to obtain more samples, the less satisfactory but nevertheless indicative method

for SIMCA class model building is the use of the total variance described as the criterion for

the retention of PC‟s. For this model 2 PC‟s were saved for each class and then the new 54

objects were compared to the three reference subsets – Southport, Murarrie and Beenleigh,

respectively, on the basis of the RSDcrit values (Table 4.3).

The RSDcrit value is an indicator of the extent to which an object belongs to a class model.

To test which of the alternative water sources belong to which baseline group, it was

determined that the „Fit Object‟ option within the Sirius 7.0 program was utilised. The RSDcrit

for the Southport class was: 0.45, p=0.05, which meant that an object with a higher value did

not belong to the class. RSDcrit for Beenleigh was: 0.77, p=0.05 and lastly the RSDcrit for

Murarrie: 1.13, p=0.05. It is interesting to note that the RSDcrit of the dirtiest water, Murarrie

is almost 3 times that of the cleanest water, Southport. Presumably, this reflects the broader

distribution of sample parameters with the Murarrie samples.

98

Table 4.4 SIMCA fit to Southport RSDcrit=0.45, p=0.05

Table 4.5 SIMCA fit to Murarrie where RSDcrit = 1.13, p= 0.05

99

As can be seen in Table 4.4 the 4c belongs to the subset- Southport. However, 3g and 6i are

very close to having an acceptable RSDcrit. Finally, Table 4.5 shows that 1cb, 1cd, 1c, 2c,

2cd, 2cb, 3cb, 3i, 5cd, 5c and 6g belong to Murarrie according to their critical RSD values.

These results are not unexpected as it has been previously established that each of the

alternative water source sites are more closely related to Murarrie in terms of water quality.

100

Figure 4.3 Cooman Plot For Murarrie and Coomera Dam including RSD values

(m) is Murarrie, (cd) Coomera Dam

Figure 4.4 Cooman Plot For Murarrie and Sea Water including RSD values

(m) is Murarrie , (sw) Sea water

101

Just as SIMCA uses the features of PCA to create significant limits for pre-specified classes

of samples, the Cooman's plot is a way to visualise the SIMCA information for the two class

case. It can be seen that the behaviour of Coomera Dam (cd) results in relation to Murarrie

(m) is also evident when analysing Cooman plots (Figure 4.3). In particular, to assist the

visual display, distance between classes is indicated by the values within the brackets. For

this case, 0.920 and 1.056 respectively. In general these interclass distances may be

interpreted on a scale of 1-3. If the interclass distance >3, the samples are very different and

if the difference is closer to 0 the more similar the classes [84]. All cd objects appear in the

lower right hand quadrant whereas most m samples are in the upper left hand quadrant. It is

also of interest that 5cd and 6m appear very close together. As has been discussed

previously (Section 3.1), this is a result of an influx of potable water before testing was

carried out. This produced samples with compositions similar to tap water. This is further

evident when PROMETHEE analysis is used to rank the waters (Section 4.5.4, Table 4.7)

On this basis, these samples show separation but still have some degree of commonality.

As in Figure 4.3, when Murarrie is compared to Sea Water (sw) using Cooman plots, it is

clear that the two sample groups have a degree of similarity. This plot highlights that whilst

the two groups cluster separately, the small residual distance values which are 1.221 and

1.030, indicate that there is a likeness in their chemical and physical compositions. Unlike

Figure 4.3, there is no outlier in either group, which was expected. These results are also

further confirmed when looking at the PROMETHEE and GAIA results (Section 4.5.4).

102

Table 4.6 Hard clustering for Alternative Water Samples

Where P = 1.2 1/n = 0.33

103

4.5.3 Fuzzy Clustering

Fuzzy clustering (FC) is an unsupervised method of data classification which is based on a

measurement of similarity between objects [120]. Conventional clustering requires objects to

belong to unique classes but for FC objects membership may be spread over several

classes [121]. Fuzzy clustering has no unique algorithm, and seeks to highlight similar

objects while providing information about the relationship of each object to that cluster [122,

123]. In this work, the model available in Sirius 7.0 was applied to provide an indication of

fuzzy membership in the data set.

In FC models, it is necessary to nominate the number of classes (n) to which the objects can

belong. It is also necessary to choose a value of the p-index (1<p>3) to indicate a given

degree of fuzzy modelling i.e if p = 1, then each object is placed into its most preferred class

and if p = 2-3 then each object seeks to find membership of more than one class. The

membership of a class is indicated by a value between 0-1. Definition of class belonging is

given by 1/n.

Previous analysis indicated that the baseline samples can be broken into three groups

according to their cleanliness. As such, in this work, initially three classes were specified, i.e,

all the data should be divided into three classes - cleanest, intermediate and least clean.

As explained previously (Section 2.9), there are two types of cluster analysis, hard and soft.

In hard clustering, data is divided into three crisp clusters where each data point belongs to

exactly one cluster (Table 4.6). The value of p was chosen as 1.2.

104

Table 4.7 Soft clustering for Alternative Water Samples

P= 2.5, 1/n=0.166

105

For soft clustering, p values are set between 2 and 3, and class membership is allowed to

spread over several classes (Table 4.7). In this case, the value of p was chosen as 2.5 and

the number of allowable clusters was expanded to six. This clustering analysis was

expanded to include six clusters, as a three cluster analysis did not offer any meaningful

separation. By expanding the number of clusters, the data was able to group more clearly

and less fuzzy samples were obtained. Again, the higher the membership value for a given

class, the more likely the object belongs to that class. As seen from table 4.7, for soft

clustering, the samples taken from Coomera Dam (cd) appear as fuzzy objects belonging to

clusters 3 and 6. Interestingly, the 5cd object, which performed the best according to the

PROMETHEE ranking, shows membership to cluster 6, suggesting that this is the preferred

class. It can also be seen that the baseline samples (s), (m) and (b) belong to clusters 2 and

5, with the exception of 6m which belongs to cluster 6, again confirming the findings of PCA

and PROMETHEE analysis (Section 4.5.1 and Section 4.5.4).

4.5.4 PROMETHEE and GAIA

The alternative water samples were ranked in order of best quality to worst quality with the

aid of PROMETHEE and GAIA. As Southport performed the best and Murarrie performed

the worst among the three reference treatment plants (Section 3.6), the alternative waters

were ranked against these two subsets.

All of the alternative waters had less than 4000 ppm of total dissolved solids as specified by

ASTM C94 (2004) and thus, when being used for mixing water in concrete, the compressive

strength did not fall by more than 10% . The pH and alkalinity of the alternative water

samples were higher than the tap water. The increased TDS content results in high alkalinity

levels in both the alternative and baseline samples. The increased alkalinity level affects the

hydration reaction and causes the dissolution of calcium carbonate and calcium silicate

106

hydrate.

Concrete mixed with washout water containing residual cement tends to have a higher pH.

Analysis of the Internal Readymix Variables in order to determine the most important,

showed that pH is a major contributor to the quality of water. Figure 4.7 suggests a moderate

rather than significant separation of the variables, thus indicating that whilst pH is the

independent variable, it can be influenced by other variables. As can be seen in the figure

below, pH is the independent variable. This was also evident in Figure 3.0 where pH was

strongly correlated to RA whilst the other variables seem rather uncorrelated to pH. Many

articles report that concrete quickly attains a pH of about 12.4 or 12.5 due to the

development of a saturated solution of calcium hydroxide [15, 16]. The calcium hydroxide

then precipitates, forming portlandite, and the pH rises due to the influence of the hydroxyl

ions from sodium and potassium hydroxide.

Figure 4.5 Plot of Discrimination power vs. variables for Southport & Murarrie

As pH and Relative Alkalinity are related, high pH levels often result in high relative alkalinity

of the mixing water which can affect the performance of fresh and hardened concrete. For

example, concrete may fail to set for prolonged periods because of the concentration of

alkalies in the concrete mixing water.

107

Table 4.8 PROMETHEE Net ranking of alternative water sources

Where : (s) Southport, (m) Murarrie, (b)

Beenleigh, (cd) Coomera Dam, (cb)

Coomera Bore, (sw) sea water, (e)

Effluent, (mb) Murarrie Bore, (i)

Ipswich, (g) Gympie, (c) Coomera

Numbers represent the sample week

eg: 1s is the first sample from

Southport

108

Thus, compared to tap water, the alternative waters were higher in alkalinity, pH, and total

dissolved solids content, however, as they had total dissolved solids content of less than 6%,

they produced concrete with acceptable strength and durability. And, as each of the

alternative waters performed equally well as the Murarrie washout water, each of these

waters meets the requirements of the ASTM specification C94. Thus, they may be reused as

mixing waters for concrete production with no significant effects on the properties of the

concrete.

The ranking algorithm (Multi Criteria Decision Making Methods 2.9) was applied to the 90

object by 5 variable data matrix. The variables were modelled as follows:

Ranking Sense: Top-down

Weights: 1 for all variables

Preference Function: Gaussian

PROMETHEE analysis was first performed using all 90 samples, i.e all Alternative waters as

well as all Baseline samples with the Internal Readymix variables, without taking into

account the crush variables. The PROMETHEE Net Ranking order shows the full ranking of

the 90 objects according to the value of the net ranking index () (Table 4.8). Those waters

sampled from the Coomera dam perform best and the sea water samples are the worst.

Analysis of the water quality testing results showed that levels of both the chloride ion (Cl−)

and sulfate ion (SO42−) in each of the alternative waters, excluding sea water and the three

reference samples, were below the limits for tap water as specified in the standard [9, 54,

111]. As a group the Sea Water (sw) have the lowest ranking, and its poor performance can

be attributed to the high levels of chloride found within the samples. For this reason, sea

water has not yet been adapted to produce concrete reinforced with steel due to the risk of

corrosion, particularly in warm and humid atmospheres. Sea water can also cause

efflorescence and humidity in surfaces of concrete exposed to the air and the water.

109

Figure 4.6 GAIA plot for all Alternative and all Baseline samples

Where : (s) Southport, (m) Murarrie, (b) Beenleigh, (cd) Coomera Dam, (cb) Coomera Bore,

(sw) sea water, (e) Effluent, (mb) Murarrie Bore, (i) Ipswich, (g) Gympie, (c) Coomera

Numbers represent the sample week eg: 1s is the first sample from Southport

110

Table 4.8 shows that 5cd outranks all the other objects with the highest value of .

Therefore, 5cd is the water sample with the best quality overall. It can also be seen that

many objects that have positive values of belong to the Coomera bore (cb) and Coomera

dam (cd) locations followed by Gympie (g) and Ipswich (i). Both the Kawana (k) and (c)

samples are interspersed with very similar indices. This is not unexpected, as both samples

are forms of treated effluent. The Murarrie bore samples (mb) and Southport effluent (e)

appear at the end of the better performing samples. It can also be seen that 5m is outranked

by all the other objects with the lowest value of . From the positioning of all sea water (sw)

and the Murarrie (m) results throughout the PROMETHEE Net Ranking it can be said that

they have the lowest ranking. This finding supports the hypothesis that sea water would

perform worst out of all of the alternative water sources due to its high level of salts. Whilst

Murarrie performed the worst, it should be noted, that, as discussed previously, all sampling

sites produced water of sufficient quality to use in the manufacture of concrete.

Just as in the PROMETHEE Net Ranking of the baseline datasets, this Net Ranking

highlights weeks 6m and 6b2 as outliers, whilst all water sources group together fairly well.

Of note is the difference between the last Murarrie value ( =-0.328, 7m) and first Coomera

Bore sample value (= 0.136, 5cb), which suggests that they are well separated.

This same dataset was then interpreted with the aid of GAIA (Figure 4.6), and allows us to

view the data in a 2-dimensional plane [2]. Also, the pi axis shows the direction of the

compromise resulting from the weights allocated to the criteria. The alternatives are to be

considered to locate in the direction of the pi axis [3]. Figure 4.6 shows that the criteria pH

and SO4 are grouped tightly as are Cl and TDS. It can also be seen that RA is almost

orthogonal to Cl indicating that RA is independent of Cl.

111

112

Table 4.9 PROMETHEE Net Ranking for all Alternative and all Baseline samples with Compressive strength Results

Where : (s) Southport, (m)

Murarrie, (b) Beenleigh, (cd)

Coomera Dam, (cb) Coomera

Bore, (sw) sea water, (e)

Effluent, (mb) Murarrie Bore, (i)

Ipswich, (g) Gympie, (c)

Coomera

Numbers represent the sample

week eg: 1s is the first sample

from Southport

113

Analysis of figure 4.6 shows that sw objects have high negative score on PC1 and the cd

objects have high positive scores on the same PC. Both Murarrie (m) and the seawater (sw)

objects are not clean but the relative uniformity of the sw characteristics as opposed to that

of m samples is quite evident by the scatter of the m group. The Southport (s) objects are

mostly grouped with low negative scores on PC1 and the remainder of the alternative water

objects are distributed with positive scores on this PC. Considering the direction and the

length of the decision axis, 5cd is preferred to all the other objects, supporting the findings

from the PROMETHEE Net Ranking.

In order to confirm the findings of these PROMETHEE and GAIA analysis plots, the same

interpretation methods were applied to a second data matrix, containing compressive

strength results. The new matrix contained 90 object by 7 variables. The PROMETHEE Net

Ranking order shows the full ranking of the 90 objects according to the value of the net

ranking index () (Table 4.9).

Once again, it can be seen that the Coomera dam (cd) samples perform best as a group

while the Murarrie (m) and Sea water (sw) samples appear to be the least clean.

Interestingly, there appear to be no significant changes in the Net Ranking Order of the

objects. This confirms the earlier hypothesis that compressive strength results appear to be

independent of each of the other variables.

This PROMETHEE analysis tends to follow the same ranking order of the baseline sets,

where 6m appears as an outlier. Each of the rest of the sample groups rank closely as there

were no outside influences changing the composition of the water dramatically. This

PROMETHEE analysis supports the PCA results obtained in Section 4.5.1. Here, Figure 4.2,

with the exception of sw, did not show any clear separation of the groups from each other,

indicating that they are all very similar in composition, making it hard for the PCA to

differentiate between the sample groups.

114

Figure 4.7 GAIA Plot for all Alternative and all Baseline samples with compressive strength results

Where : (s) Southport, (m) Murarrie, (b) Beenleigh, (cd) Coomera Dam, (cb) Coomera Bore,

(sw) sea water, (e) Effluent, (mb) Murarrie Bore, (i) Ipswich, (g) Gympie, (c) Coomera

Numbers represent the sample week eg: 1s is the first sample from Southport

115

Once again, GAIA was applied to the same data matrix, allowing us to view the data in a 2-

dimensional plane [2]. Once again, Figure 4.7 shows that the criteria pH and SO4 are

grouped tightly as are Cl and TDS. It can also be seen that RA is almost orthogonal to both

the 7 and 28 day compressive strength results, indicating that RA is independent of these

variables. The objects are distributed similarly on the Figure 4.6. Considering the direction

and the length of the decision axis, 5cd is preferred to all the others, supporting the findings

from the PROMETHEE Net Ranking. Also of note, is the close proximity of 6m to sample

5cd, which were identified in the PROMETHEE analysis (Table 4.9) as being the best

performers. It can also be seen that 6s and 6b samples, which were also affected by influx of

potable water, appear away from their source group and closer the cd sample group,

indicating their increased cleanliness.

This GAIA plot also identifies sw samples as an outlier group, as was found in the PCA

analysis (Figure 4.2). However, whilst the PCA did not differentiate clearly between the

sample groups, this plot shows clearly defined sample groups, even though they are similar

in nature, i.e Figure 4.7 highlights the specific groupings of the baseline samples and their

separation from the alternative samples. This is interesting as it reinforces the hypothesis

that while the baseline samples are clearly separated into three groups, the groups

themselves are not altogether dissimilar.

116

4.6 Chapter Summary

1. Water samples collected at nine different alternative water sampling source sites

were analysed to assess their quality on the basis of the Industry Standard Readymix

Monitoring Variables. Subsequently, concrete cylinders were made with the use of

these waters and tested for crush MPa. The analysis of these alternative water

sources was carried out following the exact same procedures as those used to

analyse the baseline data to ensure comparability.

2. Analysis of the multivariate data matrix of the crush properties and water quality with

the use of PCA indicated that the water quality does not seriously affect the

performance of the concrete provided that the water lies within the specified limits.

3. The quality of the waters was ranked on the basis of their multivariate variables and

subsequently compared to the baseline relative ranking scale. This facilitated the

new ranking system in which Coomera performed the best, and sea water performed

worst.

4. Each of the alternative water sources did not appear to have detrimental effects on

concrete properties and as such appear to be a practicable means of reducing the

need for fresh potable water in concrete manufacture.

117

5 Concluding Remarks

All water sources commonly contain a wide range of dissolved chemicals and suspended

solids. One of the cleanest types of water, potable water still contains a number of both

chemical and physical impurities. Thus, as this type of water is currently the only acceptable

source for the manufacture of concrete, it is possible that other water sources containing

similar levels of impurities are also suitable for concrete production. However, in order to

ensure that no detrimental effects are experienced, each alternative water must be subject to

a series of tests and meet acceptable industry standards in regard to both the physical and

chemical characteristics of the concrete [10, 57, 124].

The aims and objectives (Section 1.1) were addressed by focusing on the effects of

impurities in mixing water on concrete performance as well as constructing a baseline for

acceptable threshold limits. This study effectively assessed the use of recycled washout

water as well as a range of alternative water sources for use in concrete manufacture.

Analysis of the water sources currently in use in South East Queensland was conducted

first, enabling the construction of a baseline to which all alternative water sources could then

be compared. Throughout this study, each of the baseline water samples as well as the

alternative water samples, were subject to testing to determine their adherence to the

acceptable limits for impurities in the mixing water, (Table 3.1), and then subjected to 7 and

28 day compressive strength testing. Subsequent analysis of the results indicated that the

water quality does not seriously affect the performance of the concrete provided that the

water lies within the specified limits. This study found that concrete manufactured with

washout water appears to have minimal detrimental effects and is a feasible means of

reducing wash water as a waste product by allowing its reuse in subsequent batches. These

results were based on the performance requirements of National Standards on concrete

mixing water including recycled water [9, 10, 54, 56, 57, 124].

118

After the completion of the baseline threshold limits, the information collected through a

series of elemental, physico-chemical and structural testing was then interpreted

successfully using chemometric modelling. Each of the three baseline water sites were

clearly ranked according to cleanliness using PROMETHEE analysis. However, the

separation between each group was small, indicating that all water sources were within the

acceptable threshold limits, although some conditions applied (eg seawater only suitable for

use in plain concrete). The quality of the waters was ranked on the basis of their multivariate

variables and a baseline relative order was obtained that can be applied on any other water

samples for comparative purposes in the future.

Based on the experimental work carried out during this study, the suitability of other non-

potable water sources was able to be assessed. This study found that whilst the limits

provided in AS1379 and ASTM C94 are specific for potable water, much larger impurity

concentrations can be tolerated, i.e, generally, water contaminated with industrial wastes

and alternative reclaimed waters, are acceptable water sources for the manufacture of

concrete. This investigation into the chemical composition of each water source, as well as

the physical properties such as compressive strength, allowed the identification of key

elements, variables and characteristics.

There is not sufficient research in the area of alternative, reclaimed water sources to compile

specific limits for each impurity tested here. However, this study found that when new

sources of water are identified, provided they are tested for their effects on strength

development of a standard concrete mix compared to the same mix prepared with potable

tap water, and no adverse effects identified, the waters can be used to manufacture

concrete.

119

6 References

1. Borger, J., R.L. Carrasquillo, and D.W. Fowler, Use of recycled wash water and returned plastic concrete in the production of fresh concrete. Advanced Cement Based Materials, 1994. 1(6): p. 267-274.

2. Sandrolini, F. and E. Franzoni, Waste wash water recycling in ready-mixed concrete plants. Cement and Concrete Research, 2001. 31(3): p. 485-489.

3. Al-Harthy, A.S., et al., The properties of concrete made with fine dune sand. Construction and Building Materials, 2007. 21(8): p. 1803-1808.

4. Chindaprasirt, P., et al., Cement paste characteristics and porous concrete properties. Construction and Building Materials. In Press, Corrected Proof.

5. Chatveera, B., P. Lertwattanaruk, and N. Makul, Effect of sludge water from ready-mixed concrete plant on properties and durability of concrete. Cement and Concrete Composites, 2006. 28(5): p. 441-450.

6. Al-Harthy, A.S., et al., Effect of water quality on the strength of flowable fill mixtures. Cement and Concrete Composites, 2005. 27(1): p. 33-39.

7. Muszynski, L., A. Chini, and M. Bergin, Re-using wash water in ready-mixed concrete operations. Concrete. 36(2): p. 16.

8. Su, N., B. Miao, and F.-S. Liu, Effect of wash water and underground water on properties of concrete. Cement and Concrete Research, 2002. 32(5): p. 777-782.

9. A/NZ, Australia and New Zealand Standards. AS/NZS 5667.1:1998 Water quality – Sampling – Guidance on the design of sampling programs, sampling techniques and the preservation and handling of samples. (1998a). Australia and New Zealand.

10. Australia, S., AS 1379 Specification and supply of concrete. 2007. 11. Massart, D.L., et al., Chemometrics tutorials : Elsevier, Amsterdam, 1990 (ISBN 0-

444-88837-3). viii + 427 pp. Price Dfl.130.00/US$66.75 (paperback). Analytica Chimica Acta, 1991. 245: p. 290-439.

12. Ayoko, G.A., et al., Exploratory multivariate modeling and prediction of the physico-chemical properties of surface water and groundwater. Journal of Hydrology, 2007. 336(1-2): p. 115-124.

13. Kosmatka, S.H.P., W.C, Design and Control of Concrete Mixtures, in Portalnd Cement Association. 1988. p. 17, 42, 70, 184.

14. Oktar, O.N., H. Moral, and M.A. Tasdemir, Factors determining the correlations between concrete properties. Cement and Concrete Research, 1996. 26(11): p.

1629-1637. 15. Tsivilis, S., et al., An analysis of the properties of Portland limestone cements and

concrete. Cement and Concrete Composites, 2002. 24(3-4): p. 371-378. 16. Tsivilis, S., et al., Properties and behavior of limestone cement concrete and mortar.

Cement and Concrete Research, 2000. 30(10): p. 1679-1683. 17. Lo, T.Y., W.C. Tang, and H.Z. Cui, The effects of aggregate properties on lightweight

concrete. Building and Environment, 2007. 42(8): p. 3025-3029. 18. Girao, A.V., et al., Composition, morphology and nanostructure of C-S-H in white

Portland cement pastes hydrated at 55 [degree sign]C. Cement and Concrete Research, 2007. 37(12): p. 1571-1582.

19. Chindaprasirt, P., S. Rukzon, and V. Sirivivatnanon, Effect of carbon dioxide on chloride penetration and chloride ion diffusion coefficient of blended Portland cement mortar. Construction and Building Materials. In Press, Corrected Proof: p. 1410.

20. Matschei, T., B. Lothenbach, and F.P. Glasser, Thermodynamic properties of Portland cement hydrates in the system CaO-Al2O3-SiO2-CaSO4-CaCO3-H2O. Cement and Concrete Research, 2007. 37(10): p. 1379-1410.

21. Buffo-Lacarriere, L., et al., Multiphasic finite element modeling of concrete hydration. Cement and Concrete Research, 2007. 37(2): p. 131-138.

120

22. Loukili, A., A. Khelidj, and P. Richard, Hydration kinetics, change of relative humidity, and autogenous shrinkage of ultra-high-strength concrete. Cement and Concrete Research, 1999. 29(4): p. 577-584.

23. Almusallam, A.A., et al., Effect of mix proportions on plastic shrinkage cracking of concrete in hot environments. Construction and Building Materials, 1998. 12(6-7): p.

353-358. 24. Skalny, J., J.C. Phillips, and D.S. Cahn, Low water to cement ratio concretes.

Cement and Concrete Research, 1973. 3(1): p. 29-40. 25. Ficcadenti, S.J., Effects of Cement Type and Water to Cement Ratio on Concrete

Expansion Caused by Sulfate Attack Structural Engineering, Mechanics and Computation, A. Zingoni, Editor. 2001, Elsevier Science: Oxford. p. 1607-1613.

26. Yasar, E., Y. Erdogan, and A. Kilic, Effect of limestone aggregate type and water-cement ratio on concrete strength. Materials Letters, 2004. 58(5): p. 772-777.

27. Sahu, S., et al., Determination of water-cement ratio of hardened concrete by scanning electron microscopy. Cement and Concrete Composites Scanning electron microscopy of cements and concretes, 2004. 26(8): p. 987-992.

28. Tasdemir, C., et al., Effects of silica fume and aggregate size on the brittleness of concrete. Cement and Concrete Research, 1996. 26(1): p. 63-68.

29. McCarter, W.J., A parametric study of the impedance characteristics of cement-aggregate systems during early hydration. Cement and Concrete Research, 1994. 24(6): p. 1097-1110.

30. Zimbelmann, R., A contribution to the problem of cement-aggregate bond. Cement and Concrete Research, 1985. 15(5): p. 801-808.

31. Struble, L. and S. Mindess, Morphology of the cement-aggregate bond. International Journal of Cement Composites and Lightweight Concrete, 1983. 5(2): p. 79-86.

32. Cheilas, A., et al., Impact of hardening conditions on to stabilized/solidified products of cement-sewage sludge-jarosite/alunite. Cement and Concrete Composites, 2007. 29(4): p. 263-269.

33. Yigiter, H., H. Yazici, and S. Aydin, Effects of cement type, water/cement ratio and cement content on sea water resistance of concrete. Building and Environment, 2007. 42(4): p. 1770-1776.

34. Saito, M., M. Kawamura, and S. Arakawa, Role of aggregate in the shrinkage of ordinary portland and expansive cement concrete. Cement and Concrete Composites, 1991. 13(2): p. 115-121.

35. del Viso, J.R., J.R. Carmona, and G. Ruiz, Shape and size effects on the compressive strength of high-strength concrete. Cement and Concrete Research. In Press, Corrected Proof.

36. Yazici, S. and G. Inan Sezer, The effect of cylindrical specimen size on the compressive strength of concrete. Building and Environment, 2007. 42(6): p. 2417-2420.

37. Youssef, M.A. and M. Moftah, General stress-strain relationship for concrete at elevated temperatures. Engineering Structures, 2007. 29(10): p. 2618-2634.

38. Behnood, A. and H. Ziari, Effects of silica fume addition and water to cement ratio on the properties of high-strength concrete after exposure to high temperatures. Cement and Concrete Composites. In Press, Corrected Proof: p. 2634.

39. Kilic, A., et al., The influence of aggregate type on the strength and abrasion resistance of high strength concrete. Cement and Concrete Composites. In Press, Corrected Proof.

40. Persson, B., Hydration and strength of high performance concrete. Advanced Cement Based Materials, 1996. 3(3-4): p. 107-123.

41. Gesoglu, M., E. Guneyisi, and T. Ozturan, Effects of end conditions on compressive strength and static elastic modulus of very high strength concrete. Cement and Concrete Research, 2002. 32(10): p. 1545-1550.

42. Pacheco-Torgal, F., J. Castro-Gomes, and S. Jalali, Alkali-activated binders: A review: Part 1. Historical background, terminology, reaction mechanisms and

121

hydration products. Construction and Building Materials. In Press, Corrected Proof. 43. Bishr, H.A.M., et al., Assessment of concrete compressive strength using the Lok

test. Construction and Building Materials, 1995. 9(4): p. 227-237. 44. Paulini, P., A through solution model for volume changes of cement hydration.

Cement and Concrete Research, 1994. 24(3): p. 488-496. 45. Ramachandran, V.S., Concrete Admixtures Handbook - Properties, Science, and

Technology. (2nd Edition). . ed. (1995): William Andrew Publishing/Noyes. 46. Jolicoeur, C. and M.-A. Simard, Chemical admixture-cement interactions:

Phenomenology and physico-chemical concepts. Cement and Concrete Composites, 1998. 20(2-3): p. 87-101.

47. Gallias, J.L., R. Kara-Ali, and J.P. Bigas, The effect of fine mineral admixtures on water requirement of cement pastes. Cement and Concrete Research, 2000. 30(10): p. 1543-1549.

48. Xu, Y. and D.D.L. Chung, Reducing the drying shrinkage of cement paste by admixture surface treatments. Cement and Concrete Research, 2000. 30(2): p. 241-

245. 49. COLIN LOBO, P.D., New standards for mixing water. Concrete Products. 108(1): p.

57. 50. Shrestha, S. and F. Kazama, Assessment of surface water quality using multivariate

statistical techniques: A case study of the Fuji river basin, Japan. Environmental

Modelling & Software Special section: Environmental Risk and Emergency Management, 2007. 22(4): p. 464-475.

51. Arnold, K. and M. Stewart, Water Sampling Techniques Surface Production Operations (Third Edition). 2008, Gulf Professional Publishing: Burlington. p. 672-

675. 52. Madrid, Y. and Z.P. Zayas, Water sampling: Traditional methods and new

approaches in water sampling strategy. TrAC Trends in Analytical Chemistry Emerging tools as a new approach for water monitoring, 2007. 26(4): p. 293-299.

53. Australia., S.A.o., AS/NZS 5667.1:1998: Water Quality - Sampling- Part 1:Guidance on the design of Sampling Programs, Sampling Techniques and the Preservation and Handling of Samples. 1998.: Homebush, NSW.

54. APHA. , Standard Methods for the Examination of Water and Wastewater. American Public Health Association,, (1998). 20th edition.(Washington, DC.).

55. Karihaloo, B.L., Fracture toughness of plain concrete from compression splitting tests. International Journal of Cement Composites and Lightweight Concrete, 1986. 8(4): p. 251-259.

56. ASTM-C913-08 Standard Specification for Precast Concrete Water and Wastewater Structures , E.

57. Australia, S., AS 1012.14 Methods of testing concrete- Method for serucing and testing cores from hardened concrete for compressive strength. 1991.

58. Ni, Y., et al., Application of high performance liquid chromatography for the profiling of complex chemical mixtures with the aid of chemometrics. Talanta. In Press, Corrected Proof: p. 94.

59. Bates, R.G.R.G., 1912-. Determination of pH : theory and practice. 1964, New York:

Wiley. 60. Westcott, C.C., pH measurements. 1978, New York: Academic Press,. 61. Versari, A., G.P. Parpinello, and S. Galassi, Chemometric Survey of Italian Bottled

Mineral Waters by Means of their Labelled Physico-chemical and Chemical Composition. Journal of Food Composition and Analysis, 2002. 15(3): p. 251-264.

62. Oner, A. and S. Akyuz, An experimental study on optimum usage of GGBS for the compressive strength of concrete. Cement and Concrete Composites, 2007. 29(6): p.

505-514. 63. Keller, H.R., D.L. Massart, and J.P. Brans, Multicriteria decision making: A case

study. Chemometrics and Intelligent Laboratory Systems, 1991. 11(2): p. 175-189. 64. Kowalkowski, T., et al., Application of chemometrics in river water classification.

122

Water Research, 2006. 40(4): p. 744-752. 65. Alberto, W.D., et al., Pattern Recognition Techniques for the Evaluation of Spatial

and Temporal Variations in Water Quality. A Case Study: : Suquia River Basin (Cordoba-Argentina). Water Research, 2001. 35(12): p. 2881-2894.

66. Astel, A., et al., Chemometrics in monitoring spatial and temporal variations in drinking water quality. Water Research, 2006. 40(8): p. 1706-1716.

67. Bermejo-Barrera, P., et al., Application of multivariate methods to scalp hair metal data to distinguish between drug-free subjects and drug abusers. Analytica Chimica Acta, 2002. 455: p. 253-265.

68. Zhang, G., et al., Authentication of vegetable oils on the basis of their physico-chemical properties with the aid of chemometrics. Talanta. In Press, Corrected Proof.

69. Thanasoulias, N.C., N.A. Parisis, and N.P. Evmiridis, Multivariate chemometrics for the forensic discrimination of blue ball-point pen inks based on their Vis spectra. Forensic Science International, 2003. 138(1-3): p. 75-84.

70. Kokot, S., et al., Data Interpretation by some common chemometrics methods. Electroanalysis, 1998. 10(16).

71. Zhou, F., et al., Chemometrics data analysis of marine water quality and source identification in Southern Hong Kong. Marine Pollution Bulletin. In Press, Corrected Proof: p. 94.

72. Massart, D.L., et al., Handbook of Chemometrics and Qualimetrics Part A. 1997,

Amsterdam: Oxford: Elsevier. 73. Brereton, R.G., Chemometrics in analytical chemistry: A review. Analyst, 1987. 112:

p. 1635-1657. 74. Haswell, S.J., Practical Guide to Chemometrics. 1992, New York: Marcel Dekker. 75. Brereton, R.G., Chemometrics: Data Analysis for the Laboratory and Chemical Plant.

2002, New York: Wiley. 76. Hopke, P.K., The evolution of chemometrics. Analytica Chimica Acta ANALYTICAL

HORIZONS - An International Symposium celebrating the publication of Volume 500 of Analytica Chimica Acta, 2003. 500(1-2): p. 365-377.

77. Gilbert, C. and S. Kokot, Discrimination of cellulosic fabrics by diffuse reflectance infrared Fourier transform spectroscopy and chemometrics. Vibrational Spectroscopy, 1995. 9(2): p. 161-167.

78. Kokot, S. and G.A. Ayoko, Multicriteria Decision Making, in Encyclopaedia of Analytical Sciences, 2nd Edition, P.J. Worsfold, A. Townsend, and C.F. Poole,

Editors. 2005, Elsevier: Oxford. 79. Al-Shemmeri, T., B. Al-Kloub, and A. Pearman, Model choice in multicriteria decision

aid. European Journal of Operational Research, 1997. 97(3): p. 550-560. 80. Keller, H.R. and D.L. Massart, Multicriteria Decision Making: A Case Study.

Chemometrics and Intelligent Laboratory Systems, 1991. 11: p. 175-189. 81. Marques, R.C., et al., Principal component analysis and discrimination of variables

associated with pre- and post-natal exposure to mercury. International Journal of Hygiene and Environmental Health, 2008. 211(5-6): p. 606-614.

82. Al-Shiekh Khalil, W., et al., Selection of hydrothermal pre-treatment conditions of waste sludge destruction using multicriteria decision-making. Journal of Environmental Management, 2005. 75(1): p. 53-64.

83. Khalil, W.A.-S., et al., Use of chemometrics methods and multicriteria decision-making for site selection for sustainable on-site sewage effluent disposal. Analytica Chimica Acta, 2004. 506(1): p. 41-56.

84. Kokot, S., et al., Application of chemometrics for the selection of microwave digestion procedures. Analytica Chimica Acta, 1992. 268(1): p. 81-94.

85. Su, N. and C.-F. Wu, Effect of magnetic field treated water on mortar and concrete containing fly ash. Cement and Concrete Composites, 2003. 25(7): p. 681-688.

86. Raupp-Pereira, F., et al., Effects of potable water filtration sludge on the rheological behaviour of one-coat plastering mortars. Journal of Materials Processing

123

Technology, 2007. 190(1-3): p. 12-17. 87. Raupp-Pereira, F., et al., Potable water filtration sludge: Use as set retarder in one-

coat plastering mortars. Construction and Building Materials Fracture, Acoustic Emission and NDE in Concrete (KIFA-4), 2007. 21(3): p. 646-653.

88. Gani, M., Cement and Concrete. 1997, London: Chapmen and Hall. 89. Standards Australia, AS 1379 Supplement 1-1997, ―‖Specification and Supply of

Concrete—Commentary (Supplement 1 to AS 1379-1997), incorporating Amendment 1-2000,. 2001.

90. A/NZ, Australia and New Zealand Standards AS/NZS 5667.5:1998 Water quality – Sampling – Guidance on sampling of drinking water and water. (1998b): AS/NZS .

91. Brodnjak-Voncina, D., et al., Chemometrics characterisation of the quality of river water. Analytica Chimica Acta, 2002. 462(1): p. 87-100.

92. Barbieri, P., et al., A three-way principal factor analysis for assessing the time variability of freshwaters related to a municipal water supply. Chemometrics and Intelligent Laboratory Systems, 2002. 62(1): p. 89-100.

93. Geiss, S., J. Einax, and K. Danzer, Multivariate correlation analysis and its application in environmental analysis. Analytica Chimica Acta, 1991. 242: p. 5-9.

94. Miettinen, T., et al., Multivariate monitoring of a biological wastewater treatment process: a case study at Melbourne Water's Western Treatment Plant. Chemometrics and Intelligent Laboratory Systems 8th Scandinavian Symposium on Chemometrics (SSC8), Mariehamn, Aland, Finland 14-18 June 2003, 2004. 73(1): p.

131-138. 95. Lynggaard-Jensen, A., Trends in monitoring of waste water systems. Talanta, 1999.

50(4): p. 707-716. 96. Beamonte, E., et al., A statistical study of the quality of surface water intended for

human consumption near Valencia (Spain). Journal of Environmental Management, 2007. 83(3): p. 307-314.

97. Astel, A., Chemometrics based on fuzzy logic principles in environmental studies. Talanta, 2007. 72(1): p. 1-12.

98. Vighi, M., et al., QSAR and Chemometric Approaches for Setting Water Quality Objectives for Dangerous Chemicals. Ecotoxicology and Environmental Safety, 2001. 49(3): p. 206-220.

99. Singh, K.P., et al., Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)--a case study. Water Research, 2004. 38(18): p. 3980-3992.

100. Gomez, V., A. Pasamontes, and M.P. Callao, Factorial design for optimising chromium determination in tanning wastewater. Microchemical Journal, 2006. 83(2):

p. 98-104. 101. DelValls, T.A., et al., Determining contamination sources in marine sediments using

multivariate analysis. TrAC Trends in Analytical Chemistry, 1998. 17(4): p. 181-192. 102. Teppola, P., S.-P. Mujunen, and P. Minkkinen, Kalman filter for updating the

coefficients of regression models. A case study from an activated sludge waste-water treatment plant. Chemometrics and Intelligent Laboratory Systems, 1999. 45(1-2): p. 371-384.

103. Teppola, P., S.-P. Mujunen, and P. Minkkinen, A combined approach of partial least squares and fuzzy c-means clustering for the monitoring of an activated-sludge waste-water treatment plant. Chemometrics and Intelligent Laboratory Systems, 1998. 41(1): p. 95-103.

104. Teppola, P., S.P. Mujunen, and P. Minkkinen, Partial least squares modeling of an activated sludge plant: A case study. Chemometrics and Intelligent Laboratory Systems, 1997. 38(2): p. 197-208.

105. Aguado, D., et al., Comparison of different predictive models for nutrient estimation in a sequencing batch reactor for wastewater treatment. Chemometrics and Intelligent Laboratory Systems

Selected papers presented at the 9th Scandinavian Symposium on Chemometrics

124

Reykjavik, Iceland 21-25 August 2005 - SSC 9, 2006. 84(1-2): p. 75-81. 106. Aguado, D., et al., Multivariate SPC of a sequencing batch reactor for wastewater

treatment. Chemometrics and Intelligent Laboratory Systems, 2007. 85(1): p. 82-93. 107. Wu, M.-L. and Y.-S. Wang, Using chemometrics to evaluate anthropogenic effects in

Daya Bay, China. Estuarine, Coastal and Shelf Science, 2007. 72(4): p. 732-742. 108. Saurina, J., et al., Determination of calcium and total hardness in natural waters

using a potentiometric sensor array. Analytica Chimica Acta, 2002. 464(1): p. 89-98. 109. Simeonov, V., et al., Multivariate statistical study of simultaneously monitored cloud

water, aerosol and rainwater data from different elevation levels in an alpine valley (Achenkirch, Tyrol, Austria). Talanta, 2003. 61(4): p. 519-528.

110. Sarbu, C. and H.F. Pop, Principal component analysis versus fuzzy principal component analysis: A case study: the quality of danube water (1985-1996). TalantaOptical waveguide analysis, 2005. 65(5): p. 1215-1220.

111. Abrams, D.A., Tests of impure waters for mixing concrete. Proceedings of the

American Concrete Institute 20, ACI p. p. 442–86 . 112. O.Z. Cebei and A.M. Saatci, p., Domestic sewage as mixing water in concrete. ACI

Mater. J. Am. Concr. Inst. : p. .pp. 503–506. 113. Neville, A., Seawater in the mixture. S Concr. Int. Am. Concr. Inst, (2001): p. pp. 48–

51. 114. Mohammed, T.U., H. Hamada, and T. Yamaji, Performance of seawater-mixed

concrete in the tidal environment. Cement and Concrete Research, 2004. 34(4): p.

593-601. 115. Mendiguchia, C., et al., Using chemometric tools to assess anthropogenic effects in

river water: A case study: Guadalquivir River (Spain). Analytica Chimica Acta

Papers presented at the 5th COLLOQUIUM CHEMIOMETRICUM MEDITERRANEUM, 2004. 515(1): p. 143-149.

116. Nawawy, O.A.a.A., S. Use of treated effluent in concrete mixing in and v. an arid climate. Cement & Concrete Composites, pp. 137-141., Use of treated effluent in concrete mixing in an arid climate. . Cement & Concrete Composites, (1991). v. 13: p. pp. 137-141.

117. Bailey, M.C., Sewage and sewage treatment in Dubai Middle East Water and

Sewage,, (1980 ). p. pp. 161-170. 118. Tay, J.H.a.Y., W. K., Use of reclaimed water in cement mixing. . Environmental

Engineering, , (1987). . v. 113: p. pp. 1156-1160. 119. Mindness, S., and J. F. Young. , Concrete. 1981., New Jersey: Prentice-Hall.

120. P. Bermejo-Barrera, A.M.-P., Analytica Chimica Acta 455, 253 (2002). 121. Adams, M.J., Chemometrics in Analytical Spectroscopy. 2nd ed. 2004, Cambridge:

Royal Society of Chemistry. 122. S. Kokot, M.G., H. Panayiotou, T. D. Phuong, Electroanalysis 10 (1998). 123. M. J. Adams, C.i.A.S.R.S.o.C., Cambridge, ed. 2nd, 2004). 124. Australia, S., AS 3600-2001, ―Concrete Structures,‖ incorporating Amendments No. 1

and 2,. 2001,: p. 165 pp.