Quantitative Estimation of Clay Mineralogy in Fine-Grained Soils

Quantitative Estimation of Clay Mineralogyin Fine-Grained Soils

Bhaskar Chittoori, Ph.D.1; and Anand J. Puppala, Ph.D., P.E., M.ASCE2

Abstract: Stabilization design guidelines based on soil plasticity properties have certain limitations. Soils of similar plasticity properties cancontain different dominant clay minerals, and hence, their engineering behavior can be different when stabilized with the same chemicaladditive and dosage. It is essential to modify stabilizer design guidelines by including clay mineralogy of the soil and its interactions withchemical additives used. Chemical properties of a soil including cation exchange capacity (CEC), specific surface area (SSA) and totalpotassium (TP) are dependent on clay mineral constituents, and an attempt is made in this study to develop a rational and practical meth-odology to determine both clay mineralogy distribution and dominant clay mineral in a soil by using three measured chemical soil propertiesand their analyses. This approach has been evaluated by determining and evaluating clay minerals present in artificial and natural clayey soilsof known and unknown clay mineralogy. A total of twenty natural and six artificial soils were considered and used in the chemical analyses.Test results and subsequent analyses including the development of artificial neural network (ANN) based models are evaluated and describedin this paper. DOI: 10.1061/(ASCE)GT.1943-5606.0000521. © 2011 American Society of Civil Engineers.

CE Database subject headings: Clays; Expansive soils; Neural networks; Estimation; Fine-grained soils; Plasticity; Soil properties.

Author keywords: Clay mineralogy; Montmorillonite; Kaolinite; Expansive soil; Mineral quantification; Artificial neural networks.

Introduction

Current stabilizer selection procedures reported in the literaturedepend on particle size, soil type, and plasticity index (PI) proper-ties (Hausmann 1990). These procedures have certain fundamentaldeficiencies because they do not account for the interactions be-tween the soil minerals and the chemical additives. As a result, suchstabilization designs can lead to failures in soils that contain highamounts of expansive clay minerals (Haussmann 1990; Al-Rawasand Goosen 2006). These failures are attributed to the loss of sta-bilizer over an elapsed time period, or the stabilizer was ineffectivein certain types of soils, whereas other soils with the same indexproperties responded well to the same chemical stabilizer treatment.This difference in distinct treated soil behaviors can be attributed tothe original mineralogical composition of the soils and the chemicalreactions between clay minerals and chemical additives used. Twodifferent soils with the same plasticity index or PI properties mayhave different mineral composition. Hence, incorporating the claymineralogy along with other soil properties including plasticityindex and gradation will be an important step in the stabilizationdesign methodology. This approach will provide a more rationaldesign and selection of chemical additive(s) and the amount ofstabilizer needed for the successful stabilization process of soils.

Assessing the clay mineral composition of the subsoil is com-plex because determining the clay and nonclay mineral composi-tion in a soil can be complicated and time consuming. Current

methods to identify and quantify clay minerals require expensiveand skill-oriented test devices such as X-ray diffractometer, scan-ning electron microscope and infrared spectrometers. Usage ofthis equipment in the routine geotechnical site characterizationis mostly confined to the arena of research and is rarely used in thepracticing community. Also, because of the small amounts of soilsamples used and the expensive nature of the equipment used, theaforementioned mineralogical methods have a limited role in thecurrent geotechnical characterization. Hence, it is important toestablish simple and inexpensive test procedures that can be usedto determine both clay mineralogy and the dominating clay mineralin a soil.

As a part of a research study conducted to modify the stabiliza-tion practices for the state transportation agency, an attempt wasmade to develop a protocol to identify the dominating clay min-eral(s) present in the finer fraction of a given soil. Because afew assumptions are required in the determination of the clayminerals with the method described in this study, the present de-termined clay mineral composition analysis should not be con-strued as true or exact clay mineralogical composition of thesoil. This is attributed to the clay mineral identification proceduresused in this study, which only explored stable clay mineral typessuch as kaolinite, illite, and montmorillonite. Nonstable clay min-erals are not considered in this analysis. Nevertheless, this meth-odology is appropriate as other nonstable clay minerals occupysmall fractions of a given soil, and they often undergo transforma-tions to stable clay minerals from various causes such as chemicaldecomposition, hydrothermal disintegration, and others. Hence,identification of dominating clay mineralogy of a soil is feasibleby only considering the determination of the stable clay mineralmatrix. This approach will eventually aid in the better design ofchemical stabilizers than the current stabilization approaches thatrely on physical soil properties alone.

Initially, various chemical gravimetric-based test proceduresused for the determination of chemical properties of the soil, whichare later used in the determination of clay minerals in soils, are

1Faculty Associate-Research, Dept. of Civil Engineering, The Univ. ofTexas at Arlington, Arlington, TX 76019. E-mail: [email protected]

2Professor, Dept. of Civil Engineering, The Univ. of Texas at Arlington,Arlington, TX 76019 (corresponding author). E-mail: [email protected]

Note. This manuscript was submitted on May 26, 2010; approved onFebruary 7, 2011; published online on February 10, 2011. Discussion per-iod open until April 1, 2012; separate discussions must be submitted forindividual papers. This paper is part of the Journal of Geotechnicaland Geoenvironmental Engineering, Vol. 137, No. 11, November 1,2011. ©ASCE, ISSN 1090-0241/2011/11-997–1008/$25.00.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING © ASCE / NOVEMBER 2011 / 997

J. Geotech. Geoenviron. Eng. 2011.137:997-1008.

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http://dx.doi.org/10.1061/(ASCE)GT.1943-5606.0000521




detailed. Second, this paper focuses on various analysis methodsincluding regression and artificial neural network-based modelsused to successfully identify the dominating clay mineral(s). A totalof 26 soils, including both natural and artificial clay mixtures, werecollected and subjected to chemical characterization tests. Thesetest results are analyzed to develop a rational test procedure forapproximate determination of clay mineral composition and iden-tification of dominant clay mineral in a given soil.

Background

Clay mineralogy identification in soils has been pursued by re-searchers in the field of soil science and agronomy (Sridharan et al.1988; Mitchell and Soga 2005). Very few geotechnical engineershave focused on clay mineralogy aspects of the soils, and their mainfocus is in the correlation of clay mineralogy with interpretationof engineering properties (Sridharan et al. 1988; Sridharan andPrakash 1999; Cerato and Lutenegger 2002; Mitchell and Soga2005). One of the applications of clay mineralogy can be foundin the stabilizer design guidelines and the following section de-scribes how the clay mineralogy can be used for better design ofstabilizers for ground treatment.

Applications of Clay Mineralogy in Soil StabilizationGuidelines

Current stabilization design guidelines for soils often use plasticityindex to determine both the type and amount of the stabilizer to beadded to the native soil to enhance its performance. However, theusage of plasticity index can be misleading because two soils withdifferent Atterberg limits can yield the same plasticity index prop-erty. By using the current design guidelines, both soils will betreated with the same type and amount of stabilizer. This leadsto poor performance of one of the subgrades as reported byChittoori et al. (2009) because soils with similar plasticity indicescan have different mineralogies and the same stabilizer cannotprovide effective treatment for both soil types with different claymineralogies.

The Chittoori et al. (2009) study revealed the need to incorpo-rate clay mineralogy in stabilization design because performance oftreated soils is dependent on clay mineral and stabilizer interac-tions. This mineralogy information will not only be valuable forthe design of shallow soil improvement, but will also aid deep soiltreatment design procedures as complex interactions betweendominating clay minerals in a soil and chemical additives influen-ces the performance of the treated soils. This has necessitated thedevelopment of clay mineralogy determination in the soils, which isconsidered to be the main objective of the research presented in thispaper. Test procedures for certain chemical properties are consid-ered, which are indicators of the presence of dominant clay min-erals in a soil. A simple and practical clay mineral quantificationmethod is developed on the basis of the chemical property measure-ments to determine and identify the dominating clay minerals ina given soil. Before this development, a review of the availableor reported clay mineral identification methods is made and theseresults are presented subsequently.

Current Quantification Methods

Several clay mineral quantification techniques using X-ray diffrac-tion (XRD) method have been developed by various researchersacross the world (Hughes et al. 1994; Salyn and Drits 1972; Smithet al. 1986; Jones 1989). Traditionally, the soil samples used for theclay mineral quantification were oriented specimens of the clayfraction, but the modern methods use random powdered samples

that may not be totally representative of a large soil specimen.Different methods involving XRD data analysis for clay mineralquantification are explained in this study.

Theoretically, the diffraction peaks are related to the diffractionplanes present in the soil sample, hence by using the relative inten-sities of the peaks, concentration of the clay minerals present in thetested specimen can be estimated (Whittig and Allardice 1986).However, factors such as crystal perfection, chemical composition,sample packing variations, crystal orientation, and presence ofamorphous substance influence diffraction peaks in a soil (Jackson1958; Whittig and Allardice 1986). The influence of these factorsin the mineral quantification process can be overcome by the use ofstandard minerals. A more detailed explanation of this methodol-ogy can be found in Whittig and Allardice (1986).

Quantitative determinations of the amount of clay minerals bysimple comparison of the diffraction peak heights or areas areuncertain owing to many factors such as differences in absorptioncoefficients, particle orientations, and crystallinity (Mitchell andSoga 2005). This led to a few other studies, and as a result, newmethods were introduced that accounted for the aforementionedfactors causing uncertainties in the clay mineral quantificationtechniques.

Chemical mass balancing was one such technique developedto quantify clay minerals in soils. Many researchers includingAlexaides and Jackson (1966), Hodgson and Dudeney (1984), andJohnson et al. (1985) have used these techniques to assess mineralpercentages in the soil samples. In these methods, the amount ofeach element was measured with the laboratory chemical analysisof the source soil sample, and this information was then used toformulate simultaneous equations, which upon solving yieldedthe percentages of various clay minerals in a soil.

Kolka et al. (1994) compared four methods of clay mineralquantification withg elemental mass balance methods and high-lighted their corresponding strengths and weaknesses. These meth-ods were primarily solving a set of simultaneous linear equationsthat were formulated by using elemental information of the soilspecimen and the minerals for which they were being analyzed.Various indirect methods involving the use of chemical speciesand physical characteristic measurements are also used to identifythe dominating clay minerals in the soils and quantify the dominat-ing clay minerals. In these methods, clay mineral quantificationmade by using the elemental data such as the amount of silicon,aluminum, magnesium, and others present in the soil has only beenexplored.

Resulting from the preceding literature work, it has been deter-mined that a methodology is possible in which different types ofchemical measurements can be used to determine the mineral com-position in a soil specimen. To accomplish this, the total number ofmeasurements should match with the number of unknown clay min-erals in the soil. Hence, it is hypothesized that three measurementsincluding cationic exchange capacity (CEC), specific surface area(SSA), and total potassium can be used to determine three dominat-ing and stable clay minerals in a given soil. This approach and theformulation were described in Mitchell and Soga (2005) and thispresent research was an extension of this formulation by investigat-ing various natural and artificial clayey soil mixtures for a compre-hensive assessment of dominant clay mineral in the soil.

Experimental Program

An initial X-ray diffraction study was performed on all 20 soilsto identify the minerals present in the soils. As a part of theexperimental program, three chemical properties of clays, cation

998 / JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING © ASCE / NOVEMBER 2011


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exchange capacity (CEC), specific surface area (SSA), and totalpotassium (TP) are determined for the mineral identificationanalysis.

Cation Exchange Capacity

The cation exchange capacity of a soil can be defined as the capac-ity or the ability of the soil to exchange free cations that are avail-able in the exchange locations. It is simply a measure of thequantity of readily exchangeable cations neutralizing negativecharged ions in the clay mineral (Chapman 1965; Camberato2001). CEC can be used to approximate mineral composition be-cause a high CEC value indicates the presence of a clay mineralsuch as montmorillonite mineral, whereas a low CEC indicates thepresence of nonexpansive clay mineral such as kaolinite mineral.

Numerous methods can be followed for the determination of cat-ionic exchange capacity of soil specimens. Rhoades (1982) has clas-sified these methods into the following four categories: summationmethod; direct displacement method; displacement after washingmethod; and radioactive tracer method. The CEC results obtainedfrom each of these methods can be significantly different becausemany complicating interactions exist between saturating, washing,and extracting solutions within the soil sample (Rhoades 1982).Also, CEC is not an independent and single valued soil propertyand depends on soil pH and organic content in the soil (Rhoades1982). Hence, care should be taken in selecting the most appropriatemethod for the determination of CEC of a given soil.

One of the earliest methods proposed by Chapman (1965) wasselected for this study because this method provides repeatable andreliable CEC results, requires simple apparatus for laboratory test-ing, and can be easily implemented or conducted in commercialand research laboratories. This method involves addition of a satu-rating solution to the soil sample and then removal of the adsorbedcations by using an extracting solution. In the method used for thiswork, ammonium acetate (NH4OAc) is the saturating solution,which has the ability to replace all the exchangeable cation loca-tions with the ammonium (NHþ

4 ) cation. Hence, cation exchangeprocess involving this saturating solution makes it appropriate forthe reliable estimation of CEC of a soil. Detailed procedural stepsare presented in Chittoori (2008).

Specific Surface Area

Specific surface area of a soil is the total surface area of soil par-ticles contained in a unit mass of soil. This property of the soil isprimarily dependent on the particle size distribution of the soilmass, and typically, soils with a large number of smaller particlesizes have higher specific surface areas. A clayey soil with highspecific surface area has high water holding capacity and greaterswell potential owing to the large surface area that is negativelycharged and attracts dipole-natured water.

Various approaches have been used to measure specific surfacearea, including adsorption of nitrogen and other gases on the soil(Yukselen and Kaya 2006). The most commonly used method inthe field of agronomy or soil science is the adsorption of ethyleneglycol monoethyl ether (EGME; Carter et al. 1986). This involvessaturation of the prepared soil specimen, equilibration of the speci-men in a vacuum over a calcium chloride-EGME (CaCl2-EGME)solvate and then determining the weight when the equilibrium ofconstant weight is achieved. Specific surface is then determinedfrom the mass of retained EGME in comparison with the amountretained by pure montmorillonite clay, which has a surface area of810 m2=g (Carter et al. 1986). This test procedure typically takestwo days to complete.

This method was fully evaluated for its potential application inthe geotechnical field by Cerato and Lutenegger (2002) and they

concluded that the method is applicable to a wide range of soilswith different clay mineralogies and is capable of determiningthe specific surface area ranging from 15–800 m2=g. They also in-dicated that the procedure is repeatable and gives reliable results.Hence, this method is used in the current research. The methodconsists of drying 1.1 g of soil sample in the oven at 100°C for12 h and then recording the initial dry weight of the soil sample.Then, 3 ml of EGME is added and stirred. This mixture is kept in adesiccator containing EGME-CaCl2 solvate. The weight of thesample is monitored for every 2 h until there is no further decreasein weight. It normally takes 24 h to complete the test. Detailed SSAprocedure followed in this study by using the EGMEmethod can befound in Chittoori (2008).

Total Potassium

Potassium is the interlayer cation of the clay mineral, illite, and thisstable clay mineral is the only one that possesses potassium in itsstructure. Hence, measuring the amount of potassium ions in thesoil gives a direct indication of the presence of illite. The test pro-cedure formulated by Knudsen et al. (1982) was followed in thisstudy to obtain the amount of total potassium present in the soil.The method involves a double acid digestion technique developedoriginally by Jackson (1958), which uses two acids, hydrofluoricand perchloric, to break the mineral structure of the soil and thenextract the potassium ions from the structure. Once the potassiumis extracted, its concentration in the solution can be obtained withthe help of a spectrophotometer or any other appropriate device.Procedural steps followed for the determination of total potassiumare outlined in Chittoori (2008).

Test Soils and Results

For the evaluation of clay mineralogy analysis, both natural andartificial clayey soils were collected and used. A total of 20 naturalclay samples were collected from various districts and regionsacross the state of Texas, and these soils represent diverse miner-alogies of the soils formed in different geological settings of Texas.Most of these tests were performed on the finer fraction (passing#200 sieve) of the test soils. The fines fraction in all the soils testedin this study was well above 80%. Soil gradation and Atterberglimits tests were also performed on these soils and the results aretabulated in Table 1. The majority of the tested soils exhibit PIvalues of 20 or above and this could be construed as an indicatorof potential expansive soils. Additionally, a total of six artificialclayey soils were fabricated in the laboratory by mixing knownamounts of the clay minerals, which are commercially acquired.The artificial clay property data was used for initial calibrationand subsequent validations studies.

All soils were subjected to the previously described three chemi-cal test procedures. Tests were conducted in triplicate for all soils.Average and standard deviations of randomly selected soil testresults were determined and are presented in Table 2. Results showthat the test procedures used in this research provided repeatablemeasurements because the highest standard deviations of CEC, TP,and SSA measured in the select test soils are 2:73 meq=100 g,0.12%, and 8:02 m2=g, respectively. These values are small in mag-nitude compared with original magnitudes of the correspondingchemical properties, and hence, demonstrate that the test proceduresused have provided repeatable measurements. Table 3 presentsmeasured CEC, SSA, and TP test results of all 20 natural soils.

Fig. 1 is plotted with the present natural soil test resultsalong with the ranges of CEC and SSA of the three pure minerals.None of the selected soils are within the vicinity of the mineral,kaolinite. Soils that are rich with illite and montmorillonitehave matched with those of pure minerals. Subsequently, a few



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additional artificial soils containing predominantly kaolinite andillite minerals are added to the total soils group, which will beneeded to balance the model development.

Clay Mineralogy Quantification

The proposed procedure involves an initial X-ray diffraction analy-sis of the powdered sample to qualitatively identify the clay min-erals present in the given soil sample. XRD results of all the soils

showed that kaolinite, illite, and montmorillonite are dominant andpresent in the finer fraction of the soils along with a few traces ofquartz in the nonclay minerals. After the XRD analysis, the CEC,SSA, and TPs were measured for each of the soils as noted in thepreceding experimental program. These results are used in theanalysis of the following three linear equations:

%M× CECM þ%K × CECK þ%I × CECI ¼ CECsoil ð1Þ

%M× SSAM þ%K × SSAK þ%I × SSAI ¼ SSAsoil ð2Þ

%M× TPM þ%K × TPK þ%I × TPI ¼ TPsoil ð3Þwhere %M, %K, and %I = percentages of the minerals, montmo-rillonite, kaolinite, and illite present in the soil sample, respectively;CECM, CECK, and CECI are the CEC values of the pure mineralsmontmorillonite, kaolinite, and illite present in the soil sample, re-spectively; SSAM, SSAK, and SSAI are the SSA values of the pureminerals montmorillonite, kaolinite, and illite present in the soilsample, respectively; TPM, TPK, and TPI are the TP values of thepure minerals montmorillonite, kaolinite, and illite present inthe soil sample, respectively; and CECsoil, SSAsoil, and TPsoil arethe values of the properties CEC, SSA, and TP of the whole soil,respectively. Details of this analysis can be found in Mitchell andSoga (2005).

Because CEC and SSA values of pure minerals have a widerrange, solving of the simultaneous equations for determining bothpercents of kaolinite and montmorillonite is developed on the basisof an iterative process. In this process, percentages of both clayminerals and their CEC and SSA values from the known mineralranges are first assumed. These results are used to determine thecomposite chemical characteristics of soils by substituting the val-ues in the preceding equations. Chemical properties (CEC andSSA) of the pure minerals are given in Table 4 because this rangeis mostly followed in the literature (Mitchell and Soga 2005). Tosolve this iterative problem, a spreadsheet-based model has beendeveloped by using the simple features of an optimization analysis.

Table 1. Atterberg Limits, Soil Gradation Information, and United Soil Classification System for the Twenty Natural Soils

Soil number Soil name

Percentage of soil based on gradation/hydrometer tests Atterberg limits United soilclassificationSand Silt Clay Liquid limit Plastic limit Plasticity index

1 Amarillo 17 25 58 55 18 37 CH

2 Arlington-1 10 34 56 56 33 23 CH

3 Arlington-2 18 32 50 60 21 39 CH

4 Atlanta 09 38 53 62 18 44 CH

5 Austin 05 38 57 41 17 34 CH

6 Bryan 13 40 47 45 14 31 CL

7 Bryan Silt 30 50 20 30 18 12 ML

8 Dewitt County 22 30 48 50 15 35 CH

9 El Paso 37 42 21 30 14 16 CL

10 Fort Worth 11 37 52 61 32 29 CH

11 Houston 10 33 54 51 17 34 CH

12 Jackson County-1 23 29 48 60 18 42 CH

13 Jackson County-2 40 25 35 40 25 15 CL

14 Keller 18 45 37 25 14 11 CL

15 Paris 09 45 46 60 24 36 CH

16 Pharr-A 02 39 59 67 22 45 CH

17 Pharr-B 03 55 42 56 19 37 CH

18 San Antonio 17 35 48 58 22 36 CH

19 Seymour 48 28 24 28 13 15 CL

20 Victoria County 25 30 45 58 23 35 CH

Table 2. Repeatability of Test Procedures

El Paso

Trial 1 Trial 2 Trial 3 MeanStandarddeviation

CEC 55.2 57.7 53.3 55.40 2.21

SSA 158 164 161 161.00 3.00

TP 3.6 3.6 3.8 3.67 0.12

Paris

Trial 1 Trial 2 Trial 3 Mean

Standard

deviation

CEC 130.1 133.9 135.4 133.13 2.73

SSA 431 424 440 431.67 8.02

TP 0.77 0.79 0.78 0.78 0.01

Bryan

Trial 1 Trial 2 Trial 3 Mean

Standard

deviation

CEC 77.4 79.1 75.2 77.23 1.96

SSA 207 202 204.9 204.63 2.51

TP 1.37 1.4 1.32 1.36 0.04

Note: CEC in meq/100 g; SSA in m2=g; and TP in %.



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This program utilizes “what-if” analysis function, which allowsobtaining an optimal value for a formula used in one cell, called thetarget cell, by adjusting the values in other cells, called changingcells. Fig. 2 shows a snapshot of the program used in this research.The following steps are followed in this analysis:1. Measured CEC, SSA, and TP values of a given soil are the

input parameters for the analysis.2. Percentage of illite determination is a direct step because this

step was based on TP measurements. The percent illite isdirectly calculated by dividing the measured TP value with 6.

3. Both montmorillonite and kaolinite minerals still need to beestimated, and hence, these two are designated as changingcells in the spreadsheet. Also, both CEC and SSA values ofpure minerals are designated as changing cells because theseCEC and SSA values have a wide range of magnitudes.

4. The target cell is the absolute error, which is defined as thedifference between the measured CEC and SSA values of thesoil and the calculated CEC and SSA values of the same soil.Calculated CEC and SSA values in this study are defined asthose that are obtained after substituting the acquired mineralpercentages from the original Eqs. (1)–(3).

5. Both changing cells are revised within their allowable rangeto obtain the results specified in the target cell (see Fig. 2).Constraints are applied to the changing cells to restrict thevalues within the pure mineral range to obtain a minimumabsolute error value close to zero in the target cell.It is necessary to check the accuracy of the proposed

spreadsheet-based method for calculating mineral percentages inthe clay fraction of soil. Hence, the test results of artificial clays withknown mineral percentages are used first for the assessment ofthe developed procedure. Six artificial soil specimens were preparedwith predetermined mineral percentages and these soils weregrouped into three categories; A, B, and C. Group A (soils M1andM2) clayey soil containsmontmorillonite as the dominating claymineral, whereas Groups B (soils K1 and K2) and C (soils I1 and I2)contain both kaolinite and illite minerals as dominating clay miner-als, respectively. Table 5 presents CEC, SSA, and TP measurementsfor artificial clays from the present three test procedures.

Fig. 3 presents individual comparisons made between the pre-determined and interpreted percentages of each clay mineral in thesoils. The figure shows that the proposed method predicts the min-eral percentages in the soil, which matched well with measured claymineralogy because the R2 value of the best fit line made betweenmeasured and interpreted clay minerals is close to 0.92.

One limitation of the analysis presented in this study is thatthree clay minerals were only considered in the finer fraction ofthe original soil formulation and subsequent model development.Presence of other clay minerals and nonclay fine and inert mineralsmay not yield close predictions of clay mineral percentages. How-ever, this limitation is not of a practical significance because soilswith other types of clay minerals are not prominent in the naturalground and are also not problematic from the shrink–swell pointof view. Hence, this limitation is not significant in the presentresearch, which is mainly aimed at evaluating the problematicexpansive soil characterization through understanding their clayminerals. Potential applications of the clay mineralogy informa-tion in the geotechnical stabilizer design guidelines should beconsidered.

Clay Mineralogy Database

The CEC, SSA, and TP values measured for all 20 natural soils areanalyzed by using the clay mineral interpretation program to deter-mine the percent clay mineral composition. Table 6 presents theinterpreted percentages of three clay minerals in each natural soil

Table 3. Chemical Properties of All Twenty Natural Soils

Soilnumber Soil type

CEC(meq=100 gm)

SSA(m2=g) TP (%)

1 Amarillo 66 175 0.97

2 Arlington-1 121 324 0.77

3 Atlanta 134 460 1.22

4 Austin 101 288 1.74

5 Bryan 77 205 1.36

6 Bryan Silt 89 210 1.12

7 Dewitt County 63 295 1.38

8 El Paso 57 161 3.75

9 Fort Worth 117 314 0.98

10 Arlington-2 118 265 0.80

11 Houston 76 236 1.76

12 Jackson County-1 125 355 0.83

13 Jackson County-2 75 240 0.95

14 Keller 71 133 1.10

15 Paris 133 431 0.79

16 Pharr-A 104 306 1.55

17 Pharr-B 76 132 1.65

18 San Antonio 96 269 1.10

19 Seymour 58 158 3.53

20 Victoria County 109 303 1.50

0 40 80 120 160Cation Exchange Capacity, meq/100g

0

200

400

600

800

Sp

ecif

ic S

urf

ace

Are

a, m

2 /g

Kaolinite

Illite

Montmorillonite

Fig. 1. Comparison of CEC and SSAs of present soils with the rangesreported for pure clay minerals

Table 4. Values for CEC, SSA, and TP of Pure Minerals

Mineral Type CEC (meq=100 gm) SSA (m2=g) TP (%)

Illite 15–50 80–120 6

Kaolinite 1–6 5–55 0

Montmorillonite 80–150 600–800 0

Note: The information in this table is from literature and in particular fromMitchell and Soga (2005).



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tested in this study. This clay mineral database is used in thefollowing section to develop artificial neural network models topredict the dominating clay minerals in soils.

ANN Models to Evaluate Clay Mineral Fractions

Considering the complexities involved in using spreadsheet-basedsolver programs, artificial neural network-based models were con-sidered and developed in this study to predict the percentage ofminerals by using the present test data. Artificial Neural Networks(ANNs) in general, simulate the biological structure of the humanbrain by means of their architecture. ANN technique has been in-creasingly applied in geotechnical engineering applications inwhich complexities and understanding of soil behaviors are diffi-cult to simulate with theoretical models (Shahin et al. 2001; Leeand Lee 1996; Teh et al. 1997). ANN models are appropriate topredict the relationships between the model input parametersand the corresponding output parameters. This has been achievedby repeatedly feeding the known examples of input/output relation-ships to the model and then minimizing the error function used tomeasure the variations between measured and predicted outputresults. Because of these reasons, ANN modeling was selected foranalyzing the present test results.

The development of the neural network model consists of select-ing model input/out parameters, data preprocessing, designing ap-propriate model architecture, model training, and model validation.Hence, the present chemical test database has been divided into twosets, model data containing 20 test soils and validation data of sixsoils as shown in Table 7. The data used to train the neural network

model include a total of 20 data sets out of which 60% of the datawas used to train the selected model, 20% of the data was used forthe testing, and the remaining 20% was used for internal modelverification. For validation, six artificial clay mixtures were used.The model development data and the model validation data werearranged as shown in Table 7 to improve the prediction capabilitiesof the neural network.

Data was preprocessed to ensure that both the training and thevalidation set contain different percentages of the clay minerals inthe present soils to represent the entire data set. After data prepro-cessing, the next step required is to consider the model architecturefor the present test data. In this step, the number of hidden layersand their corresponding nodes in each hidden layer are establishedand determined. According to Hornik et al. (1989), a network witha single hidden layer provided with various connection weights canbe used to approximate any continuous function. Accordingly, anetwork with a single hidden layer was trained with different num-bers of nodes (varying from 1–15). The network with a singlehidden layer having eight nodes is accepted as the best networkarchitecture for this present model analysis.

Overall, two ANN models with 2 and 3 input parametersare developed and these models are termed ANN-3 and ANN-2Models, respectively. For the validation of the first model, thepresent test data (validation set) was only used because this modelrequires all three input parameters. For the second model (ANN-2),both present test results and those from the literature containinga minimum of two measured input parameters (CEC and SSA)were considered and used in the validations. The results are de-scribed next.

ANN-3 ModelFor ANN-3, the input layer has 3 nodes, the hidden layer has 8nodes, and the output layer has 3 nodes. A schematic of ANN-3model is shown in Fig. 4. The training process is the step in whichthe connection weights are optimized. A variety of algorithms areavailable to train the neural network models, but in geotechnicalengineering, it is a common practice to use the back-propagationalgorithm as suggested by Goh (1994). However, when the numberof weights in a network is less than 300, the Levenberg-Marquardtalgorithm is proven to be effective. This method often performsconsiderably faster than other algorithms and finds better optima

Fig. 2. Microsoft Excel-based solver program for the quantification of clay minerals

Table 5. CEC, SSA, and TP Values of the Six Artificial Soils

Soilnumber

Soiltype

CEC(meq=100 gm)

SSA(m2=gm)

TP(%)

1 I1 27 172 4.19

2 I2 45 244 3.94

3 K1 6 40 0.60

4 K2 18 65 1.50

5 M1 125 650 0.52

6 M2 120 625 0.72



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than other algorithms (Roweis 2006). Hence, this algorithm hasbeen adopted to train the described network in this study.

After the completion of training stage, the functionality of thetrained model is ascertained by using the validation test data thatwas not used in both training and testing stages. The intent of thevalidation step is to evaluate the model’s predictions for a gener-alized data (in place of the training data) instead of memorizingthe input/output relationships that are contained in the training data.Fig. 5 presents the correlation between each of the predicted andmeasured six artificial clays in percentage. The coefficient of de-termination (R2) is typically used to evaluate the performance of thedeveloped model. The R2 values for the ANN-3 model are found tobe 0.94, 0.87, and 0.98 for the predicted kaolinite, illite and mont-morillonite minerals, respectively. These high values indicate thatthis ANN-3 model with three measured chemical properties hasproven to exhibit good prediction capabilities of three dominatingclay minerals in the soil.

ANN-2 ModelA two-parameter ANN model was also developed in this study be-cause the majority of the clay mineralogy information reported inthe literature utilize only two measurements, i.e., CEC and SSAproperties. Hence, an attempt is made to develop a two-parametermodel utilizing CEC and SSA as the two input parameters, whichin turn interprets the percentages of kaolinite, illite, and montmo-rillonite as an output clay minerals. For the validation of this model,test results from the present six artificial clays and external resultsfrom other recent studies reported by Cerato and Lutenegger (2002)and Yukselen and Kaya (2006) were used.

0 20 40 60 80 100

Interpreted Percentage Kaolinite

0

(a)

(c)

(b)

20

40

60

80

100

Pre

det

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ined

Per

cen

tag

e K

aolin

ite

R2 = 0.92

0 20 40 60 80 100

Interpreted Percentage Illite

0

20

40

60

80

100

Pre

det

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ined

Per

cen

tag

e Ill

ite

R2 = 0.92

0 20 40 60 80 100Interpreted Percentage Montmorillonite

0

20

40

60

80

100

Pre

det

erm

ined

Per

cen

tag

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on

tmo

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nit

e

R2 = 0.86

Fig. 3. Comparisons between predetermined and interpreted minerals of the present artificial soils

Table 6. Clay Mineralogy Interpreted for the Present Natural Soils

Soilnumber Soil Type

Illite(%)

Kaolinite(%)

Montmorillonite(%)

1 Amarillo 6 52 32

2 Arlington-1 3 24 63

3 Atlanta 0 5 74

4 Austin 29 18 53

5 Bryan 23 40 37

6 Bryan Silt 19 42 39

7 Dewitt County 23 22 56

8 El Paso 63 14 23

9 Fort Worth 16 23 60

10 Arlington-2 13 36 51

11 Houston 29 28 43

12 Jackson

County-1

14 29 57

13 Jackson

County-2

16 46 38

14 Keller 18 62 20

15 Paris 13 17 70

16 Pharr-A 26 26 48

17 Pharr-B 28 54 18

18 San Antonio 18 39 42

19 Seymour 59 22 19

20 Victoria

County

25 28 47



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Table 7. Categorized Data for Model Development and Validation

Soil number Soil Type CEC SSA TP Illite (%) Kaolinite (%) Montmorillonite (%)

Model development–data set

1 Amarillo 66 175 0.97 16 52 32

2 Arlington-1 121 324 0.77 13 24 63

3 Austin 101 288 1.74 29 18 53

4 Bryan 77 205 1.36 23 40 37

5 Bryan Silt 89 210 1.12 19 42 39

6 Dewitt County 63 295 1.38 23 22 56

7 El Paso 57 161 3.75 63 14 23

8 Fort Worth 117 314 0.98 16 23 60

9 Arlington-2 118 265 0.80 13 36 51

10 Jackson County-1 125 355 0.83 14 29 57

11 Keller 71 133 1.10 18 62 20

12 Paris 133 431 0.79 13 17 70

13 Pharr-A 104 306 1.55 26 26 48

14 San Antonio 96 269 1.10 18 39 42

15 Seymour 58 158 3.53 59 22 19

16 Victoria County 109 303 1.50 25 28 47

17 I1 27 172 4.19 70 10 20

18 I2 45 244 3.94 66 1 33

19 K1 6 40 0.60 10 86 4

20 M1 125 650 0.52 9 0 91

Model validation–data set

1 Atlanta 134 460 1.22 20 5 74

2 Houston 76 236 1.76 29 28 43

3 Jackson County-2 75 240 0.95 16 46 38

4 Pharr-B 76 132 1.65 28 54 18

5 K2 18 65 1.50 25 70 6

6 M2 120 625 0.72 12 1 87

Fig. 4. Optimized artificial neural network architecture with three input parameters (ANN-3)



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Fig. 6. Optimized artificial neural network architecture for two-parameter input model (ANN-2)

0 20 40 60 80 100Pecentage Kaolinte Predicted

0

(a)

(c)

(b)

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60

80

100

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aolin

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0 20 40 60 80 100Pecentage Illite Predicted

0

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100

Per

cen

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R2 = 0.87

0 20 40 60 80 100Pecentage Montmorillonite Predicted

0

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40

60

80

100

Per

cen

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on

tmo

r illo

nit

e In

terp

rete

d

R2 = 0.98

Fig. 5. ANN-3 model predictions (x-axis) versus interpreted clay minerals (y-axis) by using the present quantification method



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The steps followed for the model development and the networkarchitecture for this model are presented in Fig. 6 with two inputs,one hidden layer, and three outputs. The model was developedby training with all original 20 soils of this research. Fig. 7 presentsthe correlation between each of the predicted and measured clayminerals. This represents internal validation with the six artificialclay mixtures. The R2 values of the selected ANN model are foundto be 0.86, 0.72, and 0.89 for kaolinite, illite and montmorilloniteminerals, respectively. Comparisons indicate good capability ofthe model for predicting clay minerals in soils. Overall, this2-parameter model showed excellent predictions of the dominantclay mineral for all six artificial soils used in this research.

For external validation, the CEC and SSA data of other soilsfrom Cerato and Lutenegger (2002) and Yukselen and Kaya(2006) were used. These studies have provided dominant claymineralogies in the test soils that were used to compare with theANN-2 predictions. A total of 12 soils with dominating clay min-erals of kaolinite, illite, and montmorillonite were considered andthis analysis was repeated. Table 8 presents the details of previousstudies that are used in the validation analysis.

Fig. 8 presents the summary of the predictions with this modeland shows that 92% of the dominant clay predictions matched wellwith those reported. Only one soil containing illite as a dominantmineral was not predicted, and the dominant clay minerals of therest of the soils were predicted accurately. Overall, the presentanalysis shows capabilities of both ANN models in predicting thedominant clay mineral in the clay fraction of the soils.

RemarksA larger database of various soil types would lead to further refine-ments to these models. Nevertheless, the proposed clay mineralogymodels presented in this study will provide clay mineralogy detailsincluding dominant clay mineralogy in a given soil. This informa-tion will be valuable in the appropriate design of stabilizers andtheir dosages for different soil types. Chittoori (2008) and Pedarlaet al. (2010) showed that the soils with high amounts of mont-morillonite (exceeding 40%) require higher amounts of stabilizer

0 20 40 60 80Percentage Kaolinite Predicted

0

20

40

60

80

Per

cen

tag

e K

aolin

ite

Inte

rpre

ted

R2 = 0.86

0 20 40 60 80Percentage Illite Predicted

0

20

40

60

80

Per

cen

tag

e Ill

ite

Inte

rpre

ted

R2 = 0.72

(a) (b)

0 20 40 60 80 100Percentage Montmorillonite Predicted

0

20

40

60

80

100

Per

cen

tag

e M

on

tmo

rillo

nit

e In

terp

rete

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R2 = 0.89

(c)

Fig. 7. ANN-2 model predictions (x-axis) versus interpreted clay minerals (y-axis) using the present quantification method

Table 8. Validation Study Soils from Literature and Their Dominant ClayMinerals

SourceSoil

number CEC SSA Dominant mineral

Yukselen and

Kaya 2006

1 25 106 Kaolinite

2 7 37 Kaolinite

3 11 62 Illite

4 38 104 Montmorillonite




Cerato and

Lutenegger 2002

8 2 15 Kaolinite

9 3 26 Kaolinte






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dosages for sustaining stabilization for a longer time period. On thebasis of these studies, a few suggested modifications to the existingstabilization flowchart are proposed and results of these recommen-dations can be found in Chittoori (2008) and Veisi et al. (2010). Theprimary finding was that higher amounts of stabilizers are neededfor treating native soils with high amounts of montmorillonite(when they exceed 40%). Further details on the development of thesuggested modifications and durability studies can be found inChittoori (2008).

The preceding example explains the significance of clay min-eralogy estimation in the soils, which can lead to improved andbetter stabilization design practices that can reduce the prematurefailures of certain types of treated subgrades. Other extendedapplications are in the areas of enhanced soil characterizations forfoundation design in expansive soils, sulfate heave assessments,and soil-waste interactions.

Summary and Conclusions

This paper summarizes results from a comprehensive experimentalinvestigation in which three chemical measurements includingCEC, SSA, and total potassium were made on both natural andartificial clayey soils. These measurements were later used to de-velop ANN models to quantify and identify the dominant clay min-erals in the fine fraction of the soil. A few salient findings from thisresearch investigation are summarized in this study. CEC, SSA, andtotal potassium property measurements used in this study demon-strated that all three methods provide repeatable measurements.With these three measurements, a spreadsheet-based iterative pro-gram was developed to determine the clay minerals in percentagesin a soil mass. The program showed predictions that are matchedwell with the known clay fractions of artificial soil specimens. Alarge mineralogy database of both natural and artificial clayey soilsis then developed, and these results are used in ANN modeling todevelop two models to predict both clay mineralogy and dominantclay mineral in a given soil. This test database was segregated intotwo parts, model development data set and model validation dataset, such that both sets contain the data that represent clay mineralpatterns expected in natural soils. ANN-2 and ANN-3 models

utilize two and three chemical property measurements, respec-tively. ANN-3 was based on CEC, SSA, and TP measurementsand ANN-2 model used CEC and SSA measurements. Validationanalysis showed excellent capabilities of ANN-3 model in provid-ing the dominant clay mineral and mineralogy distribution in thetested soils. ANN-2 with two input parameters provided reasonablepredictions of dominant mineralogy and also showed good predic-tions of dominating clay mineral for soils tested from externalinvestigations.

Overall, the research presented both test methods and ANNtools that can be used to determine clay mineral fractions and dom-inant clay mineral in the soils. This clay mineral information alongwith plasticity information will improve current stabilization designguidelines because stabilization performance of a soil is dependenton chemical reactions and interactions between stabilizer additivesand clay minerals in a given soil. Other applications can be ex-tended for better characterization of geomaterials for their applica-tions as fill and backfill materials.

Acknowledgments

The authors would like to express their sincere appreciation to theTexas Department of Transportation (TxDOT) for the supportto this research. The authors would like to acknowledge MarkMcDaniel, Dr. German Claros of the TxDOT, and Dr. SoheilNazarian of the University of Texas at El Paso for their assistancewith the present research.

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Quantitative Estimation of Clay Mineralogy in Fine-Grained Soils

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