UNIVERSITY OF GOTHENBURG Department of Earth Sciences Geovetarcentrum/Earth Science Centre ISSN 1400-3821 B963

Master of Science (120 credits) thesis Göteborg 2017

Mailing address Address Telephone Telefax Geovetarcentrum Geovetarcentrum Geovetarcentrum 031-786 19 56 031-786 19 86 Göteborg University S 405 30 Göteborg Guldhedsgatan 5A S-405 30 Göteborg SWEDEN

Evaluation of and comparison between petrographic and technical properties of rock samples from the supposed railway corridor between

Gothenburg and Jönköping, SW Sweden

Camilla Lindström

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Table of Contents

1. Introduction ....................................................................................................................... 6

1.1 Background .................................................................................................................. 6

1.2 Objectives ..................................................................................................................... 7

2. Geological Setting .............................................................................................................. 7

2.1 The Western Segment .................................................................................................. 8

2.2 The Mylonite zone ....................................................................................................... 8

2.3 The Eastern Segment.................................................................................................... 8

2.4 The Protogine zone ...................................................................................................... 8

2.5 The Transscandinavian Igneous Belt ........................................................................... 8

3. Crushed Rock Material .................................................................................................... 9

3.1 Crushed Rock Material in Road and Railway Ballast .................................................. 9

3.2 Crushed Rock Material in Concrete ............................................................................. 9

4. Technical properties of rocks ......................................................................................... 10

4.1 Grain Size and Grain Size Distribution ...................................................................... 11

4.2 Mineralogy ................................................................................................................. 11

4.3 Grain Boundaries ....................................................................................................... 12

4.4 Perimeter .................................................................................................................... 12

4.5 Foliation ..................................................................................................................... 12

4.6 Micro Cracks .............................................................................................................. 12

4.7 Secondary Alterations ................................................................................................ 13

4.8 Porosity ...................................................................................................................... 13

5. Earlier Work ................................................................................................................... 13

6. Materials and Methods ................................................................................................... 13

6.1 Technical Analysis ..................................................................................................... 14

6.1.1 Los Angeles Test ................................................................................................ 14

6.1.2 Studded Tyre Test .............................................................................................. 15

6.1.3 MicroDeval Test ................................................................................................. 15

6.2 Micro Analysis ........................................................................................................... 15

6.2.1 Mineralogy ......................................................................................................... 15

6.2.2 Grading of Grain Boundaries ............................................................................. 15

6.2.3 Secondary Alterations ........................................................................................ 15

6.3 Image Analysis ........................................................................................................... 16

6.3.1 Mineral Grain Size and Grain Size Distribution ................................................ 16

6.3.2 Perimeter Analysis ............................................................................................. 18

7. Results .............................................................................................................................. 18

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7.1 Technical Analysis ..................................................................................................... 18

7.2 Mineralogy ................................................................................................................. 24

7.3 Micro Analysis ........................................................................................................... 26

7.3.1 Grading of Grain Boundaries ............................................................................. 26

7.3.2 Altered Plagioclase ............................................................................................. 28

7.4 Image Analysis ........................................................................................................... 29

7.4.1 Grain Size and Grain Size Distribution .............................................................. 31

7.4.2 Perimeter ............................................................................................................ 32

7.4.3 Crystal Alignment .............................................................................................. 33

8. Discussion ......................................................................................................................... 33

8.1 Technical Analysis ..................................................................................................... 33

8.2 Mineralogy ................................................................................................................. 33

8.3 Micro Analysis ........................................................................................................... 34

8.3.1 Grading of Grain Boundaries ............................................................................. 34

8.3.2 Evaluation of Method ......................................................................................... 34

8.3.3 Altered Plagioclase ............................................................................................. 35

8.4 Image Analysis ........................................................................................................... 35

8.4.1 Grain Size and Grain Size Analysis ................................................................... 35

8.4.2 Perimeter ............................................................................................................ 35

8.4.3 Crystal Alignment .............................................................................................. 35

8.4.4 Evaluation of Method ......................................................................................... 36

9. Conclusions ...................................................................................................................... 37

10. Acknowledgments ........................................................................................................... 38

11. References ........................................................................................................................ 39

12. Appendix .......................................................................................................................... 42

12.1 Appendix A: General and technical data ................................................................... 42

12.2 Appendix B: Grade of Intergrowth and Grade of Altered Plagioclase ...................... 48

12.3 Appendix C: Mineralogy............................................................................................ 51

12.4 Appendix D: Area and Perimeter ............................................................................... 54

12.5 Appendix E: Texture and Grain Size ......................................................................... 55

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Abstract

Natural gravel is considered a very important, and finite, resource given its significant role in various groundwater-related issues. Natural gravel has been used as ballast and aggregates for many decades, given its favourable properties such as well-rounded and well-sorted grains. As an alternative to natural gravel, it is possible to use larger quantities of crushed bedrock material instead. This does however place great demands on the bedrock's geological and technical characteristics. The strength and resistance of the bedrock is evaluated with the Los Angeles (resistance to fragmentation; LA) and Studded Tyre (resistance to abrasion; AN) tests, which will indicate whether the material is suitable enough to be used as construction material. The strength of a rock material is in many ways a product of its petrographic characteristics, such as mineralogy, grain size, grain boundary complexity, perimeter, microcracks and amount of alteration, amongst others. The rock samples analysed in this thesis originate from different lithotectonic units, why they are expected to show varying petrographic and technical properties. The infrastructure in Sweden is intended to be extensively upgraded within the near future. The project “Götalandsbanan”, a railway for high-speed trains between Gothenburg and Stockholm, is intended to be a part of this upgrade. This construction does however place great demands on its surroundings, as large amounts of high quality crushed bedrock material will be needed for concrete aggregates and road- and railway constructions, preferably recovered in the vicinity of the locality where it will be used. The Swedish Geological Society is currently working on updating and expanding the geological data in the vicinity of the planned track profile between Gothenburg and Jönköping, why they have kindly contributed with sampled material and geological data from this area, enabling this thesis to be carried out. A relatively large number of bedrock samples of predominantly granitic composition, together with corresponding technical data, have been analysed and evaluated in thin section with regards to their technical properties, mineralogy, grain boundary complexity, grain size and amount of altered plagioclase. This has been done by comparing the data at hand, i.e. the technical data with mineralogical data, and furthermore comparing the technical data with results from image analysis (grain size, perimeter, etc.), grading of grain boundary complexity and grading of amount of altered plagioclase. Image analysis has been carried out with image analysis software’s Adobe Photoshop CS6, ImageJ 1.50i and CSD Corrections 1.53. The results of this thesis reveal that there is a correlation between the rocks LA value and its grain boundary complexity, were more interlocking and complex grain boundaries will strengthen the rock. The same correlation is seen between the rocks LA value and its total amount of altered plagioclase, where increased amounts of altered plagioclase seems to strengthen the rock as well. The AN value on the other hand seems to be more affected by the mineralogy of the rock, were increased amounts of mica and decreased amounts of feldspars most definitely lowers the rocks resistance to abrasion. No correlation between grain size, grain size distribution or perimeter and the rocks technical values was found, which might only reflect the fact that these properties of the rock samples were to alike in this case. Regarding the different lithotectonic units, the most favourable (strength and resistance-wise) rock types were as expected found in the Transscandinavian Igneous Belt to the east, in comparison to the Western and Eastern Segments to the west. Key Words: Rock quality, crushed bedrock material, quantitative petrographic analysis, image analysis, grain boundaries, perimeter, Los Angeles test, Studded tyre test Supervisors: Johan Hogmalm, University of Gothenburg, and Thomas Eliasson, The Geological Society of Sweden

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Sammanfattning

Naturgrus anses vara en mycket viktig, och ändlig, naturtillgång med tanke på dess betydande roll i olika grundvattenrelaterade frågor. Naturgrus har använts som ballast och aggregat inom byggindustrin i många årtionden, mycket på grund av dess fördelaktiga egenskaper såsom välrundade och välsorterade korn. Som ett alternativ till naturgrus är det möjligt att använda sig av större mängder krossat berg istället. Detta ställer dock stora krav på berggrundens geologiska och tekniska egenskaper. Bergets styrka och beständighet kan utvärderas med hjälp av Los Angelestest (motstånd mot fragmentering; LA) och Kulkvarnstest (motstånd mot nötning; AN), tester som indikerar om materialet är lämpligt nog att användas som byggmaterial. Bergmaterialets styrka är i mångt och mycket ett resultat av dess petrografiska egenskaper såsom mineralogi, kornstorlek, typ av korngränser, perimeter, mikrosprickor och mängden omvandlad plagioklas, bland mycket annat. Bergproverna som analyserats i denna avhandling kommer från olika litotektoniska enheter, varför de kan förväntas uppvisa varierande egenskaper sinsemellan. Infrastrukturen i Sverige står inför kraftiga uppgraderingar inom en snar framtid. Projektet ”Götalandsbanan” är en sträcka av en ny järnvägsförbindelse mellan Göteborg och Stockholm avsedd för höghastighetståg. En konstruktion som denna innebär dock högt ställda krav på sin omgivning, främst med tanke på att det kommer krävas stora mängder krossat berg av hög kvalitet till betongballast samt väg- och järnvägskonstruktioner, företrädesvis utvunnet i närheten av där materialet ska komma att användas. Sveriges Geologiska Undersökning (SGU) arbetar för närvarande med att uppdatera och utöka geologiska data i området avsett för den planerade spårprofilen mellan Göteborg och Jönköping, varför de vänligen bidragit med provtaget material och geologiska data från det berörda området, vilket möjliggjort detta projekt. Ett relativt stort antal berggrundsprov av granitisk sammansättning, tillsammans med motsvarande tekniska data, har i tunnslips-form analyserats och utvärderats främst med avseende på tekniska egenskaper, mineralogi, typ av korngräns, kornstorlek och mängden av omvandlad plagioklas. Detta har gjorts genom att jämföra tillgängliga data, dvs tekniska värden, med provernas mineralogi, samt genom att jämföra de tekniska värdena med resultat från bildanalyser (kornstorlek, perimeter, etc.), gradering av korngränsernas komplexitet och gradering av mängden omvandlad plagioklas. Bildanalyserna har utförts med bilanalysprogrammen Adobe Photoshop CS6, ImageJ 1.50i and CSD Corrections 1.53. Resultaten av ovan nämnda analyser visar att det finns ett samband mellan bergmaterialets LA-värde och komplexiteten av dess korngränser, där mer sammanväxta och komplexa korngränser ser ut att ge materialet en ökad styrka. Ett liknande samband verkar finnas mellan bergmaterialets LA-värde och andelen omvandlad plagioklas, där högre andel omvandlad plagioklas också verkar ha en tendens att stärka materialet. AN-värdet å andra sidan verkar vara mer påverkat av bergartens mineralogi, där ökad mängd glimmer och minskad mängd fältspater tydligt försämrar materialets motstånd mot nötning. Inget samband hittades mellan materialets tekniska egenskaper och dess kornstorlek, kornstorleksfördelning eller perimeter, något som möjligtvis endast återspeglar det faktum att dessa utvalda prover var alltför lika varandra för att påvisa något samband. Vad gäller de olika litotektoniska enheterna så återfanns det bästa bergmaterialet (starkast och mest motståndskraftigt) som väntat i det Transskandinaviska bältet i öster, jämfört med det Västra och Östra Segmenten i väst. Nyckelord: Bergkvalitet, krossat bergmaterial, kvantitativ petrografisk analys, bildanalys, korngränser, kornstorlek, perimeter, Los Angeles, Kulkvarn Handledare: Johan Hogmalm, Göteborgs Universitet, och Thomas Eliasson, Sveriges Geologiska Undersökning

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1. Introduction

1.1 Background

As a result of a rapidly growing community with increasing demands for environmental quality, accessibility and quality of life, an extensive upgrading of Sweden's infrastructure is intended within the near future. This has led to the project ”Götalandsbanan”, a railway for high-speed trains between Gothenburg and Stockholm, passing through smaller cities as Borås, Jönköping and Linköping (Fig. 1). The objective of the project is not only to significantly shorten the travelling time between these cities, but also, for example, "blurring" the boundaries between urban and rural areas, increasing economic integration and achieve a better functioning labour market along the current route (Om Projektet – Götalandsbanan, n.d.). This would eventually promote efficiency and growth while giving people greater opportunities to find a balance between private life and work. The construction of Götalandsbanan does however place great demands on its surroundings. Considering that the track is mostly planned to be built as longitudinal concrete plate, called Slab track (i.e. ballast-free track), it is expected to require large amounts of gravel, in particular for concrete aggregate. Ideally, one would prefer to use natural gravel

for this purpose. However, the Swedish Parliament, in April 1999 (as well as in November 2005), decided to establish 16 so-called "environmental quality objectives", which form the basis of the national environmental policy. These objectives include a vision stating that we, by year 2020, should be able to hand over a Swedish society to the next generation in which the major environmental problems have been solved. One of these environmental objectives is to reduce the use of nature gravel, mainly due to its significant role in various groundwater-related issues, together with the fact that natural gravel is not an endless resource. As an alternative to natural gravel, it is possible to use larger quantities of crushed bedrock material (Fig. 2), preferably recovered in the vicinity of the locality where it will be used, in order to dramatically reduce transportation of such large volumes of material. In order for the crushed material to be used as concrete aggregates or in road- and railway constructions, it must however meet certain requirements, for example in terms of the bedrock's geological and technical characteristics. Information on these features is not only required prior to the processing and proportioning of concrete, but can also be used as a basis for the assessment of quarry localities. The Geological Survey of Sweden is currently working on updating and expanding the geological data in the vicinity of the planned track profile between Gothenburg and Jönköping, in order to create coherent information on bedrock, soil and groundwater in the area of concern (e.g. Bergström et al., 2015). The Geological Survey of Sweden has

Figure 1 Map of southern Sweden showing the approximate routes of planned railway for high-speed trains. The route called Götalandsbanan runs from the city of Gothenburg in the west, through smaller cities such as Borås, Jönköping and Linköping, to the city of Stockholm in east. The concern of this thesis lies in the vicinity of the route between Gothenburg and Jönköping. Figure from Stjärnered (2016).

Figure 2 Graph displaying the amount of aggregate deliveries in Sweden 1984-2014 according to type of material. Amounts are in million tonnes. The graph reveals that the amount of natural gravel being used as aggregates for construction has steadily been reducing during the last few decades. Figure modified after Sveriges geologiska undersökning (2015a).

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therefore kindly contributed with sampled material and geological data, enabling this thesis to be carried out. The geological data from the Geological Survey of Sweden consists, among other data, of Los Angeles-value (LA) and Studded tyre test values (AN) from samples collected along the track profile between Gothenburg and Jönköping, values that display the technical properties of the rock in terms of its resistance to fragmentation (LA) and abrasion (AN). These mechanical properties vary due to differences in rock texture (grain size, grain boundaries, grain shape), structure (foliation) and mineral composition.

1.2 Objectives

The aim and objective of this thesis is first and foremost to examine and evaluate a relatively large amount of bedrock samples of predominantly granitic composition in thin section, together with corresponding technical data, in order to find and emphasize any relations between different technical and petrological properties of a rock. Samples from rocks of granitic compositions are of interest not only because of their high occurrence in Sweden, but also because of the stability of their mineral composition, often regardless of deformation and/or metamorphism. Given that the concerned samples originate from different lithotectonic units and zones, they have supposedly been subject to various types and amounts of geological processes in conjunction with tectonic activity in the area. This is commonly evidenced by rocks displaying different technical and petrological properties depending on their lithotectonic origin, whereupon this thesis attempts to point out what one might be able to assume about a rocks quality simply by considering its tectonic history. In the construction industry today, you are constantly searching to find environmentally friendly as well as cost-effective methods regarding searching for, evaluating and extracting materials suitable for various types of construction. The challenge often lies in the well-known fact that cost-effective and environmentally friendly methods are rarely synonymous. With this in mind, this thesis includes an attempt to prove and emphasize a simple, “quick and dirty” way to estimate a

rocks technical quality by manually grading the amount of intergrowth between grains in each thin section and comparing this with the LA and AN value of the sample, given that a higher amount of interlocking grains has been shown to improve a rocks technical properties (e.g. Höbeda, 1971; Höbeda, 1995; Åkesson et al., 2003; Göransson et al., 2004). This thesis also includes image analysis of a number of samples, mainly in order to try out a tool in Adobe Photoshop CS6 which enables quick and precise selection of individual minerals in a thin section photo acquired from an optical microscope. The resulting image of highlighted minerals has in turn be used for analysis of mean grain size, grain size distribution (CSD), perimeter and to some extent fabric orientation, by using the image analysis software ImageJ 1.50i and CSD Corrections 1.53. By comparing the mentioned technical and microscopic characteristics of a rock, together with other given properties such as minerology, one can hope to find reasons for and evidence to why two macroscopically alike rocks may display different technical characteristics, as well as how two seemingly different rock types can exhibit equal technical characteristics.

2. Geological Setting

The geology of Sweden is mainly the result of a number of orogenic events between 3.5 and 1.5 Ga (Gaál & Gorbatschev, 1987), were the Fennoscandian (Baltic) Shield grew by accretion (Mohammad et al., 2011). The shield can be divided into three main domains; the Archean domain (north), the Svecofennian Province (east) and the Sveconorwegian Province (south) (Fig. 3), the two latter being of interest here. These domains have further been divided into different lithotectonic segments; the southernmost part of the Svecofennian Province consists of the Transscandinavian Igneous Belt (TIB), while the Sveconorwegian Province consists of, among other segments, the Eastern and Western Segments (Fig. 3). These three segments are separated by large deformation zones; the Mylonite zone separating the Western Segment from the Eastern Segment, and the Protogene Zone lying between the Eastern Segment and the TIB (Fig. 3).

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2.1 The Western Segment

Within the concerned area of the railway route, the Western Segment (also known as the Idefjorden Terrane) is dominated by the Gothenburg suite (1600 Ma), with mostly granodioritic and tonalitic rocks. These are often found to be gneissic and veined with characteristic features such as long, persistent bands of augen gneiss, revealing that the Gothenburg suite has been subject to generally high amounts of deformation (Bergström et al., 2015). The Western Segment also consists of the younger, generally less deformed Hisingen suite (ca 1560 Ma) and Kungsbacka suite (ca 1320 Ma).

2.2 The Mylonite zone

The Mylonite Zone (MZ), an arcuate, 400 km long, north-south trending ductile deformation zone, is situated between the Eastern and Western Segment. The deformation zone is, in summary, a result of Sveconorwegian transpressional deformation, evidenced in the northern and central parts of the zone (Mohammad et al., 2011). The bedrock within the zone is heavily deformed and contains rock types from both the Western and Eastern Segment.

2.3 The Eastern Segment

The Eastern Segment, within the concerned area of the railway route, primarily consists of

varying types of granitic and granodioritic gneiss (1700 Ma). The bedrock of the Eastern Segment has generally been subject to higher crustal depths and therefore higher temperatures and pressure compared to the Western Segment (Larson et al., 1986; Larson & Berglund, 1992). This is demonstrated by rock types with poor technical properties, with for example rounded grains and straight grain boundaries, together with high amounts of elongated minerals (Bergström et al., 2015). A problem when mapping the gneissic bedrock of the Eastern Segment, according to Bergström et al. (2015) is the heterogeneity of the bedrock, with a wide range of compositions even within the same outcrop. This results in an even more simplified bedrock map than usual, were only the most common composition is mapped. Bergström et al. (2015) also points out that the relatively flat dip (15-30 degrees towards the west) may result in a relatively thin superficial layer being mapped over a large area, where the bedrock in reality is very heterogeneous.

2.4 The Protogine zone

The Protogine Zone (PZ) is a ~25 km wide and steeply dipping deformation zone south of Lake Vättern that has been subject to high amounts of strain. The zone separates the high grade Eastern Segment from the only slightly metamorphosed TIB in a N-S trending direction (Larson et al., 1986; Johansson et al., 1993). In reality, the Protogine zone is a major shear-zone system, its easternmost part being the Sveconorwegian Frontal Deformation Zone (SFDZ) which in turn is the eastern border of the Sveconorwegian orogeny (Brander et al., 2012). The zone can therefore be considered to be a tectonometamorphic break, considering that the rocks to its west, in the Eastern Segment, is heavily deformed by Sveconorwegian and pre-Sveconorwegian orogenic events, while the bedrock east of the zone comprises the nearly unaffected 1920-1810 Ma rocks of the Svecokarelian orogen and 1810–1650 Ma plutons and volcanic rocks of the TIB (e.g. Andréasson & Dallmeyer, 1995; Söderlund et al., 2005; Möller et al., 2007).

2.5 The Transscandinavian Igneous Belt

The Transscandinavian Igneous Belt, or TIB, stretches ~1500 km in a roughly N-S direction across the Scandinavian Peninsula, reaching from the south-easternmost parts of Sweden all the way to north-western Norway (Högdahl et

Figure 3 Map showing the major geological units of southern Sweden, modified after Hegardt et al. (2005).

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al., 2004; Brander et al., 2012). It was formed mostly by the reworking of the juvenile (2100-1870 Ma) Svecofennian crust with addition of mantle material in a continental-arc setting (Högdahl et al., 2004), why it is dominated by granitoid, syenitoid and gabbroid plutons and associated volcanic rocks, all of whom are relatively undeformed (Brander et al., 2012).

3. Crushed Rock Material

The demands on the quality of rock material being used for road and railway construction may be completely different depending on the area of use. In order to build sustainable concrete constructions, roads and railways, the properties of the material must meet the demands of the construction being built. As mentioned, properties such as mineralogy, grain size and grain boundaries may indicate the mechanical behaviour of a rock material. In order to utilize the resources in a cost effective and environmentally friendly manner, the area of use of the rock material should be adapted according to the materials properties. Rock material with less desirable properties may be used in less sensitive constructions if possible, while the rock material with the highest quality can be used in the most sensitive constructions (Hellman, et al., 2011).

3.1 Crushed Rock Material in Road and Railway Ballast

The requirements placed on rock materials used for the construction of roads and railways varies somewhat, and largely depends on in which layer of the construction it is intended to be used. In general, rock materials used for road construction is recommended to have low Los Angeles (<25%) and studded tyre test (<4-14%) values, a low to intermediate amount of mica minerals (<30%) as well as a low flakiness of particles (Geological Society of Sweden, 2016). For use in railroad constructions, the rock material should preferably have a low Los Angeles value (≤20% measured on fraction 31.5-50 mm), a low to intermediate amount of mica minerals (<10-25%), a low water absorption (<0.5%) as well as a low flakiness of particles (Geological Society of Sweden, 2016).

3.2 Crushed Rock Material in Concrete The concrete industry has been attempting to replace natural gravel with crushed rock

material in their concrete with increasing effort in recent years. This does however place great demands on the knowledge of how the crushed material interacts with other elements of the concrete, such as flowing agents and cement, and how to adapt the selection and quarrying of bedrock and mixing of concrete agents in order to manufacture a final product with low maintenance needs and costs (Lagerblad et al., 2011). There are a number of differences between natural gravel and crushed bedrock material. The particles in the crushed rock material are more angular and have a rougher surface, while the natural gravel is more rounded and almost polished (Göransson, 2011), given that it has been processed and worn by forces of nature through time (Fig. 4-5). Furthermore, the weaker material of the natural gravel has been destroyed over time. Because of these differences, the amounts of concrete agents will have to be assembled differently in order for

Figure 4 Scanning electron microscope (SEM) image of thin sections with natural ballast (left) versus crushed rock material (right). The natural gravel displays the characteristically more rounded grains, while the crushed rock material is flakier. The fraction is 0,5 – 1,0 mm. The fragments are built up by several mineral grains. Images from Lagerblad et al. (2011).

Figure 5 Scanning electron microscope (SEM) image of thin sections with natural ballast (left) versus crushed rock material (right). The natural gravel displays the characteristically more rounded grains, while the crushed rock material is flakier. The fraction is 0,125-0,25 mm. The fragments are predominantly individual minerals. Light coloured flaky particles are most likely biotite. Images from Lagerblad et al. (2011)

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the concrete to meet certain requirements, both in liquid and solid form (Lagerblad et al., 2011). Concrete essentially consists of aggregates, cement and water. The proportion of these agents is usually determined depending on the area of application of the concrete, with respect to the needs of workability, consistency, stability, hardening, air content and strength, etc. (Lagerblad et al., 2011). Given that up to 70% of the concrete can be made up of aggregates, the quality of this material largely determines the properties of the concrete (Bergkross i betong - Krossat berg ersätter naturgrus, Cementa, n.d.). Aggregates for concrete can be made up of most kinds of solid materials, as long as the material does not react negatively with the cement. Granitic bedrock material is usually favoured in Sweden because of its low cost and local availability. The granitic composition seldom reacts negatively with the cement, and the strength and durability of granitic aggregates is rarely an issue as the weakest part of the concrete will be the cement phase (Lagerblad et al., 2011). Furthermore, the distribution of particle sizes from a crushed material is often more favourable than that of natural gravel. While a crushed rock material will contain a more even distribution from fine to large particles, certain fractions of natural gravel has often been washed away, leaving so called particle gaps in the distribution of particle sizes (Lagerblad et al., 2011). The more even and continuous the particle distribution is, the better the concrete will be compacted. A problem with aggregates from crushed bedrock is however the flakiness of particles, especially in the so called filler fraction (0-0,125mm). The shape of the larger particles can usually be improved enough by using the right type of crushing process, resulting in more cubic particle shapes. However, the shape of smaller particles is to a larger extent determined by the mineral type, which exhibits a predetermined crystal form (Lagerblad et al., 2011). Quist & Evertsson (2010) demonstrated that particles or fragments finer than 0,25mm are harder to make cubic, revealing that this might be the critical size at which mineral grains are becoming more common than rock fragments. Mica minerals are the most troubling grains in the filler fraction, given that they tend to form flaky minerals. A filler fraction with more flaky

aggregates will require more water and artificial floating agents when mixing concrete in order to maintain the workability of the liquid concrete, and therefore also more cement to maintain the consistency, stability and hardening properties of the final product (Lagerblad et al., 2011). Adding more cement and floating agents will not only be unsatisfying for the environment, but it is also a very expensive solution. An amount of no more than 7% mica minerals is therefore preferred in a rock material that is to be used as aggregates in concrete (Geological Survey of Sweden, 2016). The amount of mica in the crushed rock material from Swedish granites can vary from almost nothing at all, up to 20-30% in the filler fractions. The most cost-effective is therefore to choose a rock type with apparent low mica content if possible. If this is not an option, the amount of free mica grains in crushed rock material can be lowered by either washing or wind sieving the material (Lagerblad et al., 2011). This does however add yet another cost to the line of production. The conclusion of the above mentioned would be that if the input crushed rock material is of good quality with favourable properties, the output concrete will be cost effective and of equally good quality. Adequate information on the bedrock and its input characteristics is therefore required prior to any decisions regarding the construction of bedrock quarries, mixing of concrete or anything in between the two.

4. Technical properties of rocks The technical properties of a rock that are considered in this thesis are affected by a number of different factors. These factors are all a product of the rock’s origin and tectonic history. By investigating the microstructural properties of the rock, one can roughly estimate the rocks mechanical strength. Some of the most important and common factors to investigate for this purpose are the following:

Grain size and grain size distribution

Mineralogy

Grain boundaries

Perimeter

Foliation

Micro cracks

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Secondary alterations

Porosity Not all of these properties have been investigated here, but their principals and importance is still summarized below. The technical properties of rocks are in one way or another affected by these properties, were the weakest property will act as the most limiting factor.

4.1 Grain Size and Grain Size Distribution

For magmatic granitoid rocks, and their metamorphic equivalents, the grain size of the rock often has a large impact on its technical values. Rocks with a smaller mean grain size are known to correlate with better technical qualities than do rocks with a larger mean grain size (e.g. Åkesson et al, 2004; Göransson et al., 2004; Hellman et al., 2011). A larger grain size will enable cracks to propagate easier and thereby weaken the rock, while a larger number of smaller grains will make it more difficult for cracks to propagate. Another disadvantage with very coarse grains is that the cleavage planes of the minerals very well may affect the technical properties in a negative manner (Hellman et al., 2011). The grain size distribution is also of great importance, where rocks with a larger grain size range is favoured over rocks with more homogenous grain size distributions. Rocks that consist of both coarse and fine grains tend to have a positive effect on the resistance to fragmentation and wear (Hellman et al., 2011). The grain size and grain size distribution have both been analysed in this thesis. A common way to investigate these parameters has been by manually measuring the diameter of individual grains along traverse lines that have been traced on an image obtained from optical microscopy (e.g. Åkesson et al., 2003; Hellman et al., 2011; Lundgren, 2012). However, a new approach has been tried in this thesis, explained further in Materials and Methods (Section 6).

4.2 Mineralogy

The physical properties of minerals will affect the technical properties of the rock they build up. The most primary mineral properties are crystal shape, density, cleavage and hardness, the two latter having the most significant effect

on the rocks resistance to fragmentation and abrasion. Given that the major minerals of granitic rocks are feldspars, quartz, mica and amphibole minerals, their properties are important for estimating the technical behaviour of the rock they constitute. Feldspar crystals have very good cleavage, which will act to increase the brittleness and lower the resistance to fragmentation of the rock. The feldspar minerals are however relatively hard, increasing its resistance to abrasion. Quartz crystals are hard, have low cleavage and are often anhedral. The hardness of the quartz crystals contributes to better resistance to fragmentation, the low cleavage prevents the mineral from easily breaking by fragmentation, while the anhedral grains are able to fill the space between other grains, thereby lowering the porosity and strengthening the rock (Johansson, 2011). Quartz crystals have the ability to recrystallize relatively easy, and at the same time form complex grain boundaries that contributes to the strength of the rock (Hellman et al., 2011). Mica minerals (biotite, muscovite and to some extent chlorite being relevant here) preferably form flaky particles. Mica minerals are soft minerals, giving them good resistance to fragmentation but a not so good resistance to abrasion. In igneous rocks, the mica minerals are often evenly dispersed and the mica content therefore usually has less effect on the strength of the rock. In metamorphic rocks however, the mica minerals often form mica-rich bands were the particles are arranged parallel to each other, creating a rock with anisotropic strength properties (Johansson, 2011). The strength of the rock in the direction parallel to the foliation will be the lowest, while the strength perpendicular to this foliation will be the highest. Furthermore, the amount of mica is known to be enriched when the bedrock material is crushed (Miškovský, 2004; Loorents et al., 2007; Johansson et al., 2008), which may cause the material to be unsuitable for road and/or rail way constructions due to its impaired ability to manage freeze-thaw conditions (Arvidsson & Loorents, 2008) and the reduced ability to pack the material (Höbeda & Bünsow, 1974).

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The mafic mineral amphibole commonly exhibits a long, prismatic and sometimes even fibrous mineral form. Fibrous minerals as such are known to increase the rocks flakiness and brittleness, in turn decreasing the strength of the rock (e.g. Brattli, 1992). High contents of amphibole are therefore expected to lower the strength of the rock.

4.3 Grain Boundaries The grain shape and grade of intergrowth between adjacent grains in a rock will have a large impact on the rocks technical properties. Rocks with more irregular grain shapes and a higher grade of intergrowth between grains will be more resistant to stress than rocks with less complex grain shapes and grain boundaries (e.g. Höbeda, 1971; Höbeda, 1995; Åkesson et al., 2003; Göransson et al., 2004). Straight grain boundaries will allow cracks to easily propagate along the boundaries, instead of them having to propagate through minerals, resulting in a more brittle rock with less resistance to fragmentation (Hellman et al., 2011). The grain boundaries of the samples in this thesis have been manually graded depending on their amount of intergrowth. This grading has then been compared with other properties of the sample to find any possible correlations.

4.4 Perimeter By measuring the total perimeter of a sample, one attains the total circumference of all objects in the sample (Åkesson et al., 2003). This perimeter value is affected by grain size and grain boundary complexity, were smaller grain sizes and more complex grain boundaries results in higher perimeter values, and vice versa. A higher perimeter may indirectly correlate with more preferable technical properties in rocks, as particularly the LA-value is often greatly improved by smaller grain size and interfingering or sutured grain boundaries (Åkesson et al., 2003).

4.5 Foliation Foliation is a repetitive layering recognized in metamorphic rocks, caused by shearing forces or differential pressure, typical for rocks that have been subject to orogenic events such as mountain formation. The foliation may be represented by chemical or compositional

banding, realignment of micas and clays via physical rotation of minerals, growth of platy minerals, or alignment of tabular minerals, amongst other foliating mechanisms. The foliated planes of rocks will most likely give rise to planes of weakness in the rock, causing it to easier fracture in this direction and resulting in poor mechanical properties. This is not true for all foliated rocks however, as certain metamorphic rocks have developed complex mineral grain boundaries with the ability to strengthen the rock instead (e.g., Persson & Göransson, 2005). Foliated rocks with poor mechanical properties, on the other hand, are often found to inhibit straight grain boundaries with 120° triple joints, a product of recrystallization during metamorphism (Hellman et al., 2011). The degree of foliation in a rock is commonly measured using the Foliation index, FIX (e.g. Åkesson et al., 2003; Hellman et al., 2011). This has not been carried out in this thesis, but consideration has been taken to whether or not gneissic texture has been observed in the field when sampling was carried out, as gneissic texture is a type of foliation. Crystal alignment results retrieved from the image analysis have also been considered.

4.6 Micro Cracks Micro cracks are developed in a rock when it is deformed in brittle state, the deformation being caused by blasting, crushing, pressure discharge, thermal contraction and/or tectonic movements in the crust (Kowallis & Wang, 1983; Hellman et al., 2011). These cracks may have a large impact on the technical properties of rocks, were especially the resistance to fragmentation is known to be lowered by higher amounts of micro cracks (Hellman et al., 2011). Micro cracks are generally intragranular or transgranular. Intragranular cracks are found inside the crystals, while transgranular cracks propagate straight through crystals, affecting the entire lattice (Hellman et al., 2011). Micro cracks may also occur along grain boundaries, given that cracks rather propagate along straight surfaces, such as straight grain boundaries or cleavage planes, than bending its way through the lattice (Hellman et al., 2011). In feldspars and quartz, by far the most frequent minerals in the samples investigated in this thesis, the cracks are almost

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predominantly intragranular. (Janssen et al., 2001). Micro cracks are not investigated in this thesis, but are still partly considered when evaluating the results.

4.7 Secondary Alterations Certain minerals may be subject to secondary alterations by chemical and/or physical processes, were the contemporary minerals are altered into new minerals by hydrothermal solutions in the Earth’s crust (Hellman et al., 2011). Plagioclase that has been exposed to alteration, e.g. sericite or saussurite alteration, is believed to make its host rock more resistant to brittle deformation (e.g. Åkesson et al., 2004; Hellman et al., 2011). Åkesson et al. (2004) demonstrates that feldspars with sericite alteration have less crack abundance, the reason being that sericitized feldspar grains exhibit more flexible properties than do an unaltered feldspar grain. It has however also been indicated that an overly aggressive sericite alteration will weaken the rock (Göransson et al., 2004). Weathering of rocks may be considered secondary alterations as well, a process that weakens the rocks strength properties. Weathering is however a very slow process, and most parts of the Swedish granitic bedrock has only been weathered a few millimetres since the latest ice sheet eroded and polished the landforms throughout northern Europe. The amount of weathered bedrock in crushed bedrock material is therefore often negligible (Lagerblad et al., 2008). Problems with weathering may however appear more prevalent on a local scale, for example in the vicinity of road cuts, tunnels and larger deformation zones, why caution must be taken in such areas. The degree of alteration was determined for each thin section through a microscope by simply grading the amount and extent of altered plagioclase grains from 1 to 5, were a grade 1 corresponds to no or very small amounts of alteration, and a grade 5 corresponds to very high amounts of alteration. The degree of alteration has then been compared with the technical properties of the corresponding rock samples.

4.8 Porosity The porosity refers to the void spaces in a material, in this case in rocks. A higher porosity in a rock will lower its mechanical quality. The porosity of crystalline bedrock in Sweden is however usually lower than 0,5% of the volume of the rock, why it is commonly not considered a serious issue when investigating technical properties of these rocks (Höbeda, 1995; Mazurek et al., 1996). Neither is it investigated or considered in this thesis.

5. Earlier Work Due to the growing relevance of crushed bedrock material in road and railway constructions, there is a relatively solid foundation of information on the topic regarding comparisons between bedrocks technical characteristics and its texture, structure, mineral composition, and so on. Lindqvist & Åkesson (2001) carried out a brief literature review regarding the field of image analysis applied to the technical analysis of geological materials, with the intention to give an introduction to the available literature in that area of scientific research. Michael Denis Higgins recognized that there was an entire ocean of widespread information on image analysis available, and decided to compile it all in an entire book on the topic of quantitative textural measurements in petrology (Higgins, 2006). The book not only discusses aspects of petrological theory, it also develops the methodological basis of quantitative textural measurements, evaluates available software for analysis, and indicates were mistakes and errors may occur during the analysis process. Johansson (2011) carried out an extensive literature review with regards to the correlation between the petrographic and mechanical properties of bedrock, where the most important conclusions of the various properties of bedrock have been summarized.

6. Materials and Methods

The material used and analysed in this thesis has exclusively been received from the Geological Survey of Sweden, as mentioned. The material consists of thin sections from sampling sites along the potential track profile

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of the Götalandsbanan, including a number of additional samples east-northeast of Jönköping (Fig. 6). Furthermore, the Geological Society of Sweden provided technical and mineralogical data for each site of sampling as well.

The analysis of thin sections and image capturing from the same thin sections has mostly been carried out at the Geological Society of Sweden in Gothenburg, but partly also at the Department of Earth Sciences, University of Gothenburg.

All thin sections were reviewed briefly in a microscope, whereupon the grain boundaries were graded depending on the degree of intergrowth. A number of samples were chosen for further analysis of grain size, grain size distribution, perimeter and grain orientation. The results were then compared with corresponding technical properties and mineralogical composition of each sample, and furthermore compared more accurately with single samples that exhibit similar properties.

In a statistical analysis, expected results largely depend on the choice and number of input samples. The samples that were analysed in this project are of granitic or closely granitic composition, given that this is the most common composition in the concerned area. The precision of the results partly depends on how homogeneous/heterogeneous the bedrock is in the sampling area, and in part also depends on how homogeneous/heterogeneous the thin section from the sampling area is, given that only a small window if the thin section was analysed in terms of grain size, grain size distribution, perimeter and grain orientation.

Merely a petrographic analysis is not sufficient enough to conclusively evaluate a rocks suitability for use in constructional purposes. Petrographic results should rather be seen as basic information from which you decide if and what kind of further analysis, tests and controls should be carried out.

6.1 Technical Analysis The technical properties of a rock may initially decide whether it is suitable for use as aggregates for road and railway construction, in terms of quality and sustainability. These properties are routinely investigated by standardized methods in order to quantitatively evaluate the characteristics of different materials. The methods considered in this thesis are described below. All values from the technical analysis described below and used in this thesis were, as mentioned, provided by the Geological Society of Sweden.

6.1.1 Los Angeles Test

The Los Angeles value (LA) indicates a rocks resistance to fragmentation. The test is carried out according to the Swedish standard method SS-EN 1097-2 (Svensk Standard, 1997a). In brief words, a bedrock sample is crushed, sieved and weighed, after which 5000 g of a fixed fraction of the sample together with 11 steel balls (diameter 45-49 mm) is added to a Los Angeles mill. The mill rotates the material 30 min (500 rotations) and the resulting material is sieved for 10 min and weighed again. On this basis the Los Angeles vale for the sample can be determined. Higher values correspond to poor technical values, and vice versa.

Figure 6 Map showing the location of all 116 samples of granitoid compositions. The city of Gothenburg is located in the west and the city of Jönköping in the east.

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6.1.2 Studded Tyre Test

The studded tyre test value (AN) reveals a rocks resistance to wear by abrasion from studded tyres. The test is carried out according to the Swedish standard method SS-EN 1097-9 (Svensk Standard, 2004a). In brief words, a bedrock sample is crushed in two steps by a rotational jaw crusher followed by a smaller jaw breaker, after which the sample is sieved and the different fractions are weighed. The sample is then placed in a ball mill, together with steel balls (7000 g) and two litres of water, and the ball mill spins the material 5400 revolutions (c. 1 hour). The steel balls are magnetically removed, the sample is sieved, dried in an oven and finally weighed in different fractions, after which the studded tyre test value can be calculated. Higher values correspond to poor technical values, and vice versa.

6.1.3 MicroDeval Test

The MicroDeval (MDe) test measures a rocks resistance to abrasion. The test is carried out according to the Swedish standard method SS-EN 1097-1 (Svensk Standard, 1997b). In brief words, the process of crushing, sieving and weighing is carried out as for the studded tyre test. The sample (500 g) is placed in a mill together with steel balls (5000 g) and water (c. 2,5 l) and rotated 12 000 revolutions (c. two hours), after which the steel balls are removed and the sample is sieved, dried and weighed in different fractions. The microDeval test value can then be established. Higher values correspond to poor technical values, and vice versa.

6.2 Micro Analysis As mentioned, all thin sections used for micro analysis were sampled and provided by the Geological Society of Sweden. A total of 116 thin sections have been analysed, all of whom were sampled in the vicinity of the proposed railway corridor of the Götalandsbanan (Fig. 6) at different periods of time during the past few decades. In order to avoid bias, the technical properties of the samples where not known nor at hand while analysing the grain boundaries.

6.2.1 Mineralogy

The mineralogical composition of all but three samples was determined by the Geological

Society of Sweden, by point counting a minimum of 500 minerals of every sample. The results are presented in percent of each mineral type of the sample. This modal composition has been used to determine the rock type based on the Streckeisen (1967) classification for igneous rocks.

6.2.2 Grading of Grain Boundaries

All 116 thin sections were analysed through microscope in order to determine the general amount of intergrowth between individual grains in every sample. The observations were based on the amount of intergrowth and interlocking between the grains, how apparent the grain boundaries were and if the grain boundaries were straight/smooth or irregular. Depending on the character of these features, each sample was assigned a grade from 1-5, with joints every 0.5 grade, according to Figure 7.

6.2.3 Secondary Alterations

All 116 thin sections were analysed through microscope in order to determine the general amount of altered plagioclase in each rock sample. Each sample was assigned a grade of 1-

Figure 7 Scale for determining the grade of intergrowth of grain boundaries. Figure modified from Hellman et al. (2011).

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5, were a grade 1 corresponds to no or very small amounts of alteration, and a grade 5 corresponds to very high amounts of alteration. The grade was decided with regards to the amount of altered plagioclase, according to both total volume of the whole sample and the extent of the alteration in individual plagioclase grains.

6.3 Image Analysis

Image analysis is a technique used in many fields of science in order to obtain quantitative information by measuring the features in an image (Mainwaring & Petruk, 1989). Applications are known from scientific fields such as medicine, biology, physical metallurgy, remote sensing, military detection, food sciences and earth sciences. In all these fields, it has been recognised that it is possible to obtain information on several different parameters by measuring a computerised image, such as size, shape, number of objects or grey-scale value. In this study, the analysed images are obtained from optical microscopy, which despite its long history still is considered a cost-effective method for quantitative textural analysis, and is probably one of the most commonly used methods as well. This is partly due to the wide range of optical properties displayed by minerals in thin section investigated through optical microscopes. This makes it relatively easy to distinguish individual crystals and different mineral types, unlike for example the also commonly used images from a scanning electron microscope with backscattered detector (SEM/BSE), were the thin section is investigated in grey scale depending on the minerals atomic weight. This makes it difficult to separate mineral grains of the same composition when positioned next to each other (Fig. 8). The quality and range of data possible to obtain depends largely on the resolution of the image. This is however not considered a serious issue today, as there are a large number of cheap alternatives of cameras and camera lenses for obtaining images of sufficient resolution. Manual image analysis methods generally yield high-quality data directly, but on the other hand requires a large amount of time and judgement. There is also a higher risk of operator bias when collecting and interpreting

data manually. Automatic image analysis will not be subject to the same amount of operator bias, but on the other hand often requires a larger amount of time to set up. The analysis itself will however be much less time consuming, especially if there is a large number of similar samples being analysed. A general problem with data being automatically processed is that the quality of the results will be lowered the more times a computer accounts for the interpretations.

6.3.1 Mineral Grain Size and Grain Size Distribution

The mineral grain size and grain size distribution has been determined for 25 samples. Most of these samples were selected for specific comparative purposes, but a number of samples were chosen on a more random basis in order to attain a larger volume of data for general comparison. Samples with very complex grain boundaries were not able to be chosen for this purpose, nor were samples with porphyritic texture, due to the nature of the method presented below.

Figure 8 SEM/BSE (a and c) and binary (b and d) images used for perimeter analysis by Åkesson et al. (2003). Image a and c have been treated with a number of image enhancing operations in order to detect the biotite (image a and b) and K-feldspar (image c and d) phases for measuring their perimeter value. Note that the boundaries between single mineral grains cannot be detected if adjacent grains are of the same phase. Images from Åkesson et al. (2003)

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For each of the 25 samples, a representative part of the thin section was photographed through an optical microscope. Images were taken of the sample in crossed polarized (Fig. 9A) and plane polarized (Fig. 9B) light. For a number of samples, an image was taken with a first order retardation plate inserted under crossed polarized light (Fig. 9C) as well. These images were then stacked on top of each other, but in different layers, in the image processing software Adobe Photoshop CS6. Minor adjustments were made to the images, e.g. regarding contrast and colour, in order to increase the probability to detect and highlight the grain boundaries correctly. Working with a copy of the crossed polarized image as base, the mineral grains were selected one by one using the Adobe Photoshop tool Quick Selection Tool. By using this tool, the boundaries of a mineral grain are automatically traced simply by clicking in the middle of the grain. Minor adjustments of the selection made by the tool are required for more complex grains, adjustments that are easily made by simply decreasing or increasing the selection with the same tool. By stacking the different types of images of the sample taken from the microscope on top of each other in different layers, one can use them alternatively in order to more accurately identify the grain boundaries when they are somewhat unclear. Here, every traced grain was highlighted in red and a black border was assigned to the outline

of the selection. Minerals that intersect the border of the image were not considered, while broken and altered grains were traced according to their original form, as far as possible. When all visible grains have been highlighted, the resulting image will look like in Figures 9D and 9E. An image like Figure 9E can be used to analyse the size, distribution, perimeter and orientation of the grains in an image processing program. The open source program ImageJ 1.50i was used for this purpose. Given the nature of the image used for analysis, the image does not require any major adjustments in order for the software to analyse it. The procedures carried out after adding the images to the ImageJ program are summarized in Table 1. In order to obtain 3D data from the 2D data provided from ImageJ calculations, the program CSD Corrections 1.53 (Higgins, 2000) has been used to perform the necessary stereological conversion. In order for the calculations to work out properly, the general crystal shape has to be estimated for each sample. This has been done by plotting the 2D data into the spreadsheet CSDSlice (version 5), created and explained in detail by Morgan & Jerram (2006), and was kindly provided by the authors. The CSDSlice spreadsheet contains a large amount of crystal shape measurements. Hence, by entering our 2D data to the spreadsheet, it will be compared to the data in

Figure 9 Microscopic images from sample TEN140014. A) Crossed polarized image. B) Plane polarized image. C) Crossed polarized image with a first order retardation plate inserted. D) All grains have been traced and highlighted. E) All grains intersecting the boundary of the image have been removed.

A C

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the CSDSlice database, and a best-fit overall crystal shape will be proposed. The 2D data from ImageJ, together with the best-fit crystal shape from CSDSlice, is then entered into the 3D conversion program CSD Corrections. When the calculations have been made, a macro connecting ImageJ to CSD Corrections will alert if any crystals are too small to be measured reliably. The crystal sizes are divided into different bins, the number of bins chosen prior to calculations. The number of crystals in each bin should not be less than three (Higgins, 2006) for statistical matters.

6.3.2 Perimeter Analysis The perimeter was measured in the same

samples as the grain size distribution was

measured, given that the software ImageJ

(section 6.3.1) is able to attain perimeter values

from the image as well. The total perimeter

value attained directly from the measurement

was normalized against the total area of

measured grains, as the total area of grains

differ somewhat from sample to sample.

7. Results

7.1 Technical Analysis

All data from technical analysis has been compiled in Appendix A. As expected, due to the similar nature of both test types, the values from the microDeval and studded tyre tests show an evident correlation (Fig. 10). This correlation between technical methods can be applicable when only one value is known for a certain material. As stated by Göransson et al. (2008), the unknown value can then be estimated if necessary. The correlation of the 77 samples in this study, where MDE = 0.71 x AN – 0.93 (R2 = 0.87), can successfully be compared with correlations made by Stenlid (2000; MDE = 0.86 × AN – 2.71 (R2 = 0.95)), Göransson et al. (2008; MDE = 0.89 × AN – 2.50 (R2 = 0.89)), Bergström et al. (2008; MDE = 0.77 × AN – 1.51 (R2 = 0.97) and Lundgren (2012; MDE = 0.62 × AN + 0.55 (R2 = 0.96), all showing a distinct positive correlation. The comparison between Los Angeles and Studded Tire Test values for a total of 112 samples are shown in Figure 11. The samples

Table 1 Procedures carried out prior to analysing images in ImageJ 1.50i. Binary image and threshold

The program requires an image that contains fields with given values in order to recognize and measure different features of the image. This was obtained here by adjusting the image type to an 8-bit binary image and then using the threshold tool to obtain an image with only two colours (e.g. black and white), or more accurately two values.

Set measurements

The following features were measured: Area, Centroid, Perimeter and Fit Ellipse.

Set scale The scale of the image must be set correctly.

Analyse particles

Prior to analysing the particles of the image, a number of conditions can be set. The following are of interest here: Size of particles: determines the minimum and maximum area of the particles wished to be measured (here: 0,0001-Infinity). Exclude on edges: in order to not measure the “empty” area along the edge of the image. Show Outlines: the program will produce an image showing the outlines of every measured grain.

y = 0,7116x - 0,9298R² = 0,8674

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Figure 10 MicroDeval value (%) compared to Studded Tire Test value (%) of 77 samples. As expected, the values show a good positive correlation.

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are categorized in different colors and symbols depending on which lithotectonic unit or zone they belong to, given the diverse properties observed between these geological areas. The concerned units and zones are the Protogene Zone (PZ), the Transscandinavian Igneous Belt (TIB), the Eastern Segment (ES), the Western Segment (WS) and the Mylonite Zone (MZ), all present on the geological map in Figure 3. The Los Angeles and Studded Tyre Test values show a good positive correlation overall, with lower values being equal to more favourable technical properties, enabling use of this material in more demanding concrete-,

road- and railway constructions. Figure 11 demonstrates that the rocks in the TIB are more likely to have more favourable (low values) technical properties. The two samples from the Mylonite zone seem to have a tendency towards the lower values as well. Samples from the Protogine zone together with the Eastern and Western segments rarely give LA-values below 20, and only a few samples have AN values lower than 10. Above these values, the samples seem to be more or less widespread. A more detailed, but still simplified, map of the geology in the concerned area can be seen in Figure 12, with the evident boundaries of the

Figure 11 Los Angeles values (%) compared to Studded Tire Test values (%) of 112 samples. The symbols representing the samples are given different shapes and colors depending on which lithotectonic unit or zone they belong to. The concerned units and zones are the Protogene Zone (PZ), the Transscandinavian Igneous Belt (TIB), the Eastern Segment (ES), the Western Segment (WS) and the Mylonite Zone (MZ).

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Figure 12 Geologic map of the area surrounding the planned railway corridor between the cities of Gothenburg (west) and Jönköping (east) with locations of 169 sampling sites. The samples are of different rock types, in contrary to the exclusively granitoid rock samples analysed in more detail in this study, in order to obtain an overview of the technical properties of all rocks in the area.

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different lithotectonic units (Fig. 3). When comparing this geological map with the maps in Figures 13 and 14, where the Los Angeles and Studded Tyre Test values from the analysed rock samples have been interpolated respectively, it is made obvious once again that the technical properties of the bedrock are more favourable in and near the TIB compared to the rocks in the Eastern and Western Segments. One should however bear in mind that these maps are greatly simplified, given the scattered distribution of samples and variation of rock types in the area. The values interpolated farther away from the sampling sites are subject to significant uncertainties with increased distance to sampling sites. The maps do however provide a general picture of the technical properties of the concerned corridor and its surroundings.

In Figure 15, the samples have been divided into rock types, according to Streckeisen (1967). To make it easier to evaluate the technical values displayed by each rock type, the rock types from Figure 15 have all been divided into separate figures (Figs. 16 – 25).

The 31 rock samples of monzogranitic composition (Fig. 16) are widely dispersed, with both high and low values of LA and AN. The correlation between the two values is very evident here as well. The 23 gneissic monzogranitic rock samples (Fig. 17) are confined to LA values higher than 20% and AN values higher than 10%. The values appear almost to be divided into two clusters, one group with higher values and one group with lower values when compared to each other.

Figure 14 Map with interpolated (IDW) Studded Tyre Test values from a total of 169 rock samples in the vicinity of the planned railway corridor.

Figure 13 Map with interpolated (IDW) Los Angeles values from a total of 169 rock samples in the vicinity of the planned railway corridor.

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The 11 syenogranitic rock samples (Fig. 18) are relatively spread out, displaying intermediate values of both LA and AN, with the characteristic correlation between the two values being fairly evident. One rock sample differs from the others with its very high values of both LA (46.2%) and AN (26.5%). The six syenogranitic gneiss samples (Fig. 19) are not very unlike the non-gneissic syenogranites, but do in comparison have overall slightly higher values. The 13 granodioritic rock samples (Fig. 20) display varying technical properties, ranging from low to high values of both LA and AN. The correlation between the two values is evident here. The four rock samples of granodioritic gneiss (Fig. 21) however, show higher and more consistent properties, with LA values of 26.8-35.9%, and AN values of 13.2-19.85%.

Four rock samples were of tonalitic composition (Fig. 22). The AN values of these rocks were found to be generally high, with the lowest value at 15.4% and the highest at 25%. The LA values on the other hand are low to intermediate values (15.5 – 29.4%). Two additional rock samples of tonalitic gneiss (Fig. 23) showed generally higher values of both LA and AN values (28.2-35.5%; 18.5-21.8%). Three rock samples are of quartz dioritic or quartz gabbroic compositions (Fig. 24). All three samples have similar LA values (14.9-21.4%), and their AN values range from 11.6% to 18.7%. Another three rock samples have quartz monzodioritic compositions (Fig. 24), and have fairly low and similar LA and AN values (13.8-18.9%; 8.6-13.1%). Eight quartz monzonite rock samples where recognized (Fig. 25), and they all plot in a more or less straight line when comparing LA and AN values. The LA values range from 15.9% all the way up to 37.5%, and the AN values lie between 7.2% and 20.4%

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Figure 15 Los Angeles values (%) compared to Studded Tyre Test values (%) of 108 samples. The samples have been divided into corresponding rock type, according to Streckeisen (1967).

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Figure 16 Los Angeles values (%) compared to Studded Tyre Test values (%) for 31 samples of monzogranitic composition.

Figure 17 Los Angeles values (%) compared to Studded Tyre Test values (%) for 23 samples of monzogranitic gneiss.

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Figure 18 Los Angeles values (%) compared to Studded Tyre Test values (%) for 11 samples of syenogranitic composition.

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Figure 20 Los Angeles values (%) compared to Studded Tyre Test values (%) for 13 granodioritic samples.

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Figure 22 Los Angeles values (%) compared to Studded Tyre Test values (%) for 4 samples of tonalitic composition.

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Figure 23 Los Angeles values (%) compared to Studded Tyre Test values (%) for 2 samples of tonalitic gneiss.

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Granitoid rock types with gneissic texture are common in south-western Sweden, and the texture is likely to impact on the technical properties of the rocks associated with it. Therefore, the Geological Society of Sweden have indicated whenever the sampled rock type is of gneissic origin. Using these field observations, the rock samples were divided into non-gneissic and gneissic, and their technical properties were compared (Fig. 26). The result shows a clear trend with most of the non-gneissic rock samples inheriting low or intermediate technical values, while the gneissic rock samples almost exclusively have intermediate and high technical values. When considering the LA value, only two non-gneissic rock samples plot above 30%, the rest above this value being gneissic rock samples.

Regarding the AN values, only two gneissic samples plot below 10%, the rest being non-gneissic samples, and on the other side of the spectra only two non-gneissic samples plot above 20% while the rest are gneissic rock samples. Certain properties of minerals can sometimes be estimated simply by considering if they are light or heavy, why the technical values of a rock may be demonstrated by comparing the ratio of LA and AN values with the density of the rock (Fig. 27). Light minerals, such as quartz and feldspars, tend to have lower fracture energies which enables them to crack more easily (Lindqvist et al., 2007; Tavares & das Neves, 2008). A rock containing large amounts of light minerals will therefore behave

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1,5

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2,5

3

3,5

2,6 2,65 2,7 2,75 2,8 2,85

LA

/A

N

Density (g/cm3)

Figure 27 The LA/AN ratio of 112 samples compared to their density. High contents of heavy minerals seem to lower the LA/AN ratio, and vice versa.

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Non-Gneissic Gneissic

Figure 26 Los Angeles values (%) compared to Studded Tyre Test values (%) of 112 samples. The samples have been divided into non-gneissic and gneissic rocks, based on observations made by the Geological Survey of Sweden during sampling.

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AN (%)

Quartz Monzonite

Figure 25 Los Angeles values (%) compared to Studded Tyre Test values (%) for 8 samples of quartz monzonitic composition.

Figure 24 Los Angeles values (%) compared to Studded Tyre Test values (%) for 3 samples of quartz dioritic/gabbroic composition and 3 samples of quartz monzodioritic composition.

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Quartz Diorite / Quartz Gabbro Quartz Monzodiorite

24

in a more brittle manner, hence increasing the LA value of the rock. The same minerals are however known to have a high resistance to abrasion because of their hardness, and will therefore show low AN values. This combination will result in a higher LA/AN ratio. High-density minerals such as biotite and amphiboles on the other hand have high fracture energies, and are therefore more likely to have lower LA values (Lindqvist et al., 2007; Tavares & das Neves, 2008). The same minerals are however less resistant to abrasion, and will therefore cause the rock to have higher AN values. The above mentioned correlations between high and low-density minerals and their rocks technical values are evident in Figure 27, where all the rock samples LA and AN ratio are compared with the density of the sampled rock, resulting in a negative correlation.

7.2 Mineralogy The mineralogy of a rock may have a large impact on its technical values. The influence of single mineral types on the LA and AN values of the rock samples in this study are evidenced in Figures 28-41. All mineralogical data is compiled in Appendix C. The influence of quartz on the technical values (Figs. 28-29) is not obvious in the samples of this study. A weak positive correlation can however be seen between the quartz content and LA value (Fig. 28). Plagioclase does not seem to have an evident effect on its own on the rocks technical values (Figs. 30-31). The amount of alkali feldspar in a rock shows no correlation with the LA values in the samples of this study (Fig. 32), but does however show a weak negative correlation with the AN values (Fig. 33). When combining the amounts of plagioclase and alkali feldspar and comparing this value with the technical values (Figs. 34-35), there is still no correlation with the LA

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0 20 40 60

LA

(%

)

Quartz (%)

Figure 28 The Los Angeles value (%) of 110 rock samples compared to their amount of quartz (%).

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0 20 40 60

AN

(%)

Quartz (%)

Figure 29 The Studded Tyre Test value (%) of 113 rock samples compared to their amount of quartz (%).

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0 20 40 60

LA

(%

)

Plagioclase Total (%)

Figure 30 The Los Angeles value (%) of 110 rock samples compared to their amount of plagioclase (%).

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AN

(%)

Plagioclase Total (%)

Figure 31 The Studded Tyre Test value (%) of 113 rock samples compared to their amount of plagioclase (%).

25

value (Fig. 34) but there is on the other hand an obvious negative correlation with the AN value (Fig. 35). When combining the amount of quartz, plagioclase and alkali feldspar, no correlation can be seen with the LA value (Fig. 36), but a negative correlation can be seen with the AN values of the samples (Fig. 37).

The total amount of mica (biotite, muscovite and chlorite) in the rock samples does not affect the LA value in an evident way (Fig. 38), but it does however have an obvious impact on the AN value (Fig. 39), which is lowered with lowered amount of mica in the sample. Amphibole content does not seem to affect the technical properties of the rock samples of this study (Figs. 40-41).

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LA

(%

)

Alkali Feldspar (%)

Figure 32 The Los Angeles value (%) of 106 rock samples compared to their amount of alkali feldspar (%).

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AN

(%)

Alkali Feldspar (%)

Figure 33 The Studded Tyre Test value (%) of 110 rock samples compared to their amount of alkali feldspar (%).

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0 20 40 60 80 100

LA

(%

)

Feldspar Total (%)

Figure 34 The Los Angeles value (%) of 110 rock samples compared to their total amount of feldspar (%).

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AN

(%)

Feldspar Total (%)

Figure 35 The Studded Tyre Test value (%) of 113 rock samples compared to their total amount of feldspar (%).

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LA

(%

)

QAP (%)

Figure 36 The Los Angeles value (%) of 110 rock samples compared to their amount of quartz (Q), alkali feldspar (A) and plagioclase (P; %).

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AN

(%)

QAP (%)

Figure 37 The Studded Tyre Test value (%) of 113 rock samples compared to their amount of quartz (Q), alkali feldspar (A) and plagioclase (P; %).

26

7.3 Micro Analysis

7.3.1 Grading of Grain Boundaries

All data from grading of grain boundaries is compiled in Appendix B. The grade of intergrowth between adjacent grains has been determined for 116 rock samples. The observations were predominantly built on the amount of intergrowth and interlocking between the grains, how apparent the grain boundaries were and if the grain boundaries were straight/smooth or irregular. Figure 42 illustrates the type of grain boundaries that can be assigned certain grades of intergrowth, with examples from the current study. The difference between i.e. grade 1.5 and 2.0 may not be obvious at this scale, but the difference between i.e. grade 1.5 and grades higher than 3.5 is be more evident. The results are summarized in Figures 43 and 44. In Figure 43, the grade of intergrowth is

compared to the LA values of the rock samples. There is an obvious negative correlation between the two properties, where a high amount of intergrowth between the grains seems to correlate with lower LA values, and vice versa. A correlation can also be observed between the grade of intergrowth and the AN value of the rock samples (Fig. 44), but this is not as evident. When comparing the grade of intergrowth between gneissic and non-gneissic rock samples, it is evident that the gneissic rocks have a higher tendency to have less interlocking grain boundaries (Fig. 45), with no samples graded higher than 3.0. The grain boundaries of the non-gneissic rocks show more variation, with both high and low grades of intergrowth, although they seem to trend towards the intermediate grades. This trend reflects the recrystallization of minerals that occurs during metamorphic processes, therefore causing gneissic rocks to have lower resistance to fragmentation (LA).

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LA

(%

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Mica Total (%)

Figure 38 The Los Angeles value (%) of 108 rock samples compared to their total amount of biotite, muscovite and chlorite (%).

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AN

(%)

Mica Total (%)

Figure 39 The Studded Tyre Test value (%) of 111 rock samples compared to their total amount of biotite, muscovite and chlorite (%).

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LA

(%

)

Amphibole (%)

Figure 40 The Los Angeles value (%) of 30 rock samples compared to their amount of amphibole (%).

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AN

(%)

Amphibole (%)

Figure 41 The Studded Tyre Test value (%) of 31 rock samples compared to their amount of amphibole (%).

27

Figure 42 Examples of grain boundaries and their grade of intergrowth from samples analysed in this thesis. Los Angeles value and Studded Tyre Test values are also included for each sample.

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(%

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Grade of Intergrowth

Figure 43 The Los Angeles value (%) compared to the grade of intergrowth between grains of 112 rock samples.

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1,0 2,0 3,0 4,0 5,0

AN

(%)

Grade of Intergrowth

Figure 44 The Studded Tyre Test value (%) compared to the grade of intergrowth between grains of 116 rock samples.

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1,5 2 2,5 3 3,5 4 4,5

Nu

mb

er o

f S

amp

les

Grade of Intergrowth

Gneissic Non-Gneissic

Figure 45 Comparison between the grade of intergrowth between grains in gneissic versus non-gneissic rock types for 116 rock samples.

28

7.3.2 Altered Plagioclase

All data from grading the amount of altered plagioclase is compiled in Appendix B. Figures 46A-D show examples of rock samples with a grade 1 (Fig. 46A-B) and grade 5 (Fig. 46C-D) amount of altered plagioclase. The comparison between the amount of altered plagioclase compared to the LA value of 112 rock samples is shown in Figure 47. The results indicate that low amounts of altered plagioclase correlate with higher LA values, and vice versa. The trend is somewhat more evident when comparing samples with grade 1, 4 and 5 regarding amounts of altered plagioclase, apart from two grade 1 samples with lower LA values than expected and one grade 4 sample with higher LA value than expected. Grade 2 and 3 samples are somewhat more continuously spread out with regards to their LA values. When comparing the amount of altered plagioclase compared to the AN value of the rock samples (Fig. 48) a correlation does exist, but is far less evident. The grade 1 samples show intermediate AN values, while grade 2, 3 and 4 samples show larger variation in terms of the AN value. Grade 5 samples inherit low and more similar AN values.

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LA

(%

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Altered Plagioclase

Figure 47 The amount of altered plagioclase (grade 1-5) compared to the Los Angeles value (%) of 112 rock samples. Grade 1 corresponds to no or low amounts of altered plagioclase, while grade 5 corresponds to high amounts of altered plagioclase.

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(%)

Altered Plagioclase

Figure 48 The amount of altered plagioclase (grade 1-5) compared to the Studded Tyre Test value (%) of 112 rock samples. Grade 1 corresponds to no or low amounts of altered plagioclase, while grade 5 corresponds to high amounts of altered plagioclase.

Figure 46 Microscope images from thin sections with widely different amounts of altered plagioclase. A and B) Plane polarized (A) and crossed polarized (B) images from rock sample TEN140014. Plagioclase crystals are nearly unaffected by alteration. The amount of plagioclase alteration equals grade 1. The LA value of the sample is 46.29% and the AN value is 14.77%. C and D) Plane polarized (C) and crossed polarized (D) images from rock sample TEN082013. Plagioclase crystals are abundant and severely affected by alteration. The amount of plagioclase alteration equals grade 5. The LA value of the sample is 13.8% and the AN value is 8.6%.

A B

C D

29

7.4 Image Analysis

All data from the image analysis is compiled in Appendix D and E. Figure 49 contains a map showing the position of the 25 samples that were subject to image analysis in this study. The map also displays the different lithotectonic units present in the area. Examples of images used in and created from analysis in ImageJ 1.50i are shown in Figure 50 (from sample MGO035025A). Examples of the graphic results obtained from the measurements of the mentioned images in CSD Corrections 1.53 are shown in Figures 51-53. Figure 51 is the basis for the grain size and grain size distribution results, were a linear

regression is calculated from the ln (population density) (mm-4) versus size (mm). The mean grain size is retrieved from this graph as well. Figure 52 shows a histogram displaying the population density (number of grains) versus size (mm), a complementary grain size distribution result. This has not been used in the analysis, but can be used to get a better understanding of the grain size distribution. In Figure 53, a rose diagram displays the alignment of the grains in the measured sample, i.e. the foliation of the sample. The scale to the left in the figure indicates number of grains. Table 2 demonstrates the results obtained for the measurements of sample MGO035025A that gave rise to the graphs and the rose diagram in Figures 51-53

Figure 49 Map showing the lithotectonic units of the area surrounding the planned railway corridor, and the positions of all samples that have been subject to image analysis.

30

Regression Slope -2,84

Mean Grain Size 0,4266

Alignment Factor 0,36

Figure 51 A linear regression curve with ln(population density) (mm-4) versus grain size (mm). The grain size and grain size distribution results are retrieved from this graph.

Figure 52 A histogram displaying the population density (number of grains) versus grain size (mm).

Figure 53 Rose diagram displaying the alignment of grains in the sample measured. Scale indicates number of grains.

Table 2 Results obtained from CSD Corrections 1.53 for sample MGO035025A.

Figure 50 Microscopic images of thin section MGO035025A. A common scale for figures A-C is found in the bottom right corner of Figure A. A) Crossed polarized image. B) Resulting image from digitalization of grains. C) Binary image created in ImageJ 1.50i prior to measurement. D) Resulting image from measurements of grain size in ImageJ 1.50i.

A

B

C

D

31

7.4.1 Grain Size and Grain Size Distribution

The LA value versus the grade of intergrowth

between grains in the 25 rock samples subject

to image analysis are shown in Figure 54,

demonstrating that the complexity of grain

boundaries does not have a definitive effect on

the LA value in this case.

The measured grain size distributions of the

25 rock samples are displayed in Figures 55

and 56, where the results have been compared

to the LA and AN values, respectively. The

more negative the regression slope value is, the

smaller the grain size distribution. The rock

samples have been divided into granitic gneiss

and granites. There seems to be no obvious

correlation between the grain size distribution

and the technical properties of the samples

however. Figures 57 and 58 display the mean

grain size values obtained from the

measurements, compared to the technical

properties of the rock samples as well. The

same division into granitic gneiss and granites

as mentioned has been made here as well. No

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(%

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Regression Slope

Granitic gneiss (ES) Granite

Figure 55 The Los Angeles value (%) versus the grain size distribution (regression slope) of 24 rock samples. The more negative the regression slope value, the smaller the grain size distribution.

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Regression Slope

Granitic Gneiss Granite

Figure 56 The Los Angeles value (%) versus the grain size distribution (regression slope) of 25 rock samples. The more negative the regression slope value, the smaller the grain size distribution.

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Mean Grain Size (mm)

Granitic Gneiss Granite

Figure 57 The Los Angeles value (%) versus the grain size distribution (regression slope) of 24 rock samples. The more negative the regression slope value, the smaller the grain size distribution.

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Mean Grain Size (mm)

Granitic Gneiss Granite

Figure 58 The Los Angeles value (%) versus the grain size distribution (regression slope) of 25 rock samples. The more negative the regression slope value, the smaller the grain size distribution.

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Grade of Intergrowth

Figure 54 The Los Angeles value (%) versus the grade of intergrowth between grains in the 25 rock samples that have been subject to image analysis.

32

evident correlation seems to exist between the

mean grain size and the technical properties

either. The mean grain size of the rock samples

is however quite similar, with all but three

samples within the range of 0.2-0.5 mm.

A correlation of grain size and technical

properties that does seem to exist however can

be seen in Figure 59, where the LA and AN

values are compared to each other and the rock

samples have been categorised according to the

absence or presence of porphyritic texture. The

texture has been observed in the field by the

Geological Survey of Sweden in the process of

collecting the rock samples, or during the

study of thin sections through microscope. The

results show that the rock samples that are

porphyritic, or even weakly so, seem to be more

likely to obtain lower technical values, while

technical values of the non-porphyritic rocks

are more widespread.

7.4.2 Perimeter

The results from the perimeter analysis in

comparison with the technical values of the

rock samples are displayed in Figures 60 and

61. The rock samples have been divided into

granitic gneiss and granites. All samples plot

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(%

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AN (%)

Non-Porphyritic Weakly Porphyritic Porphyritic

Figure 59 The Los Angeles value (%) compared to the Studded Tyre Test value (%) of 112 rock samples, divided into non-porphyritic, weakly porphyritic and porphyritic.

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Perimeter (mm/mm2)

Granitic Gneiss Granite

Figure 60 The Los Angeles value (%) compared to the perimeter (mm/mm2) of 24 rock samples. A higher perimeter value equals a smaller grain size and/or more complex grain boundaries.

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Perimeter (mm/mm2)

Granitic Gneiss Granite

Figure 61 The Studded Tyre Test value (%) compared to the perimeter (mm/mm2) of 25 rock samples. A higher perimeter value equals a smaller grain size and/or more complex grain boundaries.

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Alignment Factor

Gneiss (ES) Granite

Figure 62 The Los Angeles value (%) compared to the alignment factor of the grains in 24 rock samples. A higher alignment factor corresponds to a more foliated fabric.

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Alignment Factor

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Figure 63 The Studded Tyre Test value (%) compared to the alignment factor of the grains in 25 rock samples. A higher alignment factor corresponds to a more foliated fabric.

33

within a perimeter value of c. 12-20 mm/mm2.

The perimeter of the samples does not seem to

correlate with the technical values.

7.4.3 Crystal Alignment

The alignment of crystals in a rock reveals the

amount of foliation present in the rock. The

results from measurements of the crystal

alignment in 25 rock samples are shown in

Figures 62 and 63, where it is compared to the

technical values of corresponding rock

samples. The rock samples have been divided

into granitic gneiss and granites. Alignment

factor 0 corresponds to a massive texture, while

a value of 1 corresponds to a perfectly foliated

rock. All but one sample have an alignment

factor less than 0.4. No evident correlation was

found between the degree of crystal alignment

and the technical properties of the rock

samples.

8. Discussion

8.1 Technical Analysis

When comparing the technical properties of the rock samples in this study with their original location (Figs 13, 14 and 15), it is evident that the more favourable properties are found in the rocks belonging to the TIB. The properties of the rocks derived from the Eastern and Western segments, as well as the Protogine and Mylonite zones, tend to be more unpredictable, and their resistance to fragmentation and abrasion is more likely to be lower than for the TIB rocks. This is most likely due to the fact that the rocks in the TIB are more or less undeformed, while the rocks of the Eastern and Western segments, the Mylonite zone and the Protogine zone were all subject to high amounts of stress and strain during a number of orogenic events. A large amount of the rocks in the deformed areas exhibit gneissic texture, which evidently lowers the rocks resistance to both fragmentation and abrasion (Fig. 26). This is most likely due the fact that deformation will cause minerals to recrystallize and increase the frequency of micro cracks in the rock, causing it to break more easily.

When comparing the technical properties between different rock types, according to

Streckeisen (1967), there seems to be no clear correlations between solely the rock type and the technical properties. The granites (Figs. 16-19) and granodiorites (Figs. 20-21) all plot in a widespread manner. One might argue that the quartz diorites/gabbros (Fig. 24), quartz monzodiorites (Fig. 24) and quartz monzonites (Fig. 25) all display exclusively low technical values compared to the rest of the rock types, while the tonalities (Figs. 22-23) tend to show higher technical values, but there are however very few samples of these rock types in this study, making their results somewhat uncertain. As mentioned, it is however clear that the gneissic equivalents of the granites (Figs. 17 and 19), granodiorites (Fig. 21) and tonalities (Fig. 23) all seem to exhibit less favourable technical values.

With the above-mentioned differences between the lithotectonic units and gneissic vs non-gneissic rock types, it is made clear that the rocks of the TIB are far more likely to be suitable for use as concrete aggregates and/or in road- and railway constructions, given their stronger and more predictable technical values.

8.2 Mineralogy

In general, the results reveal that the mineralogy of a rock first and foremost affects the rocks resistance to abrasion rather than the resistance to fragmentation. The only mineral that shows the opposite relation in this study is the amount of quartz (Figs. 28-29). Although weak, there is a correlation between the amount of quartz and the LA value, where a higher amount of quartz correlates to reduced resistance to fragmentation. Quartz, given its hardness and low cleavage, is normally known to have a good resistance to fragmentation, but the crystals may in this case be affected by the stress and strain that acted upon the rocks during the orogenic events in the area. This may have led to an increased amount of intragranular cracks and sub-grain formation, features that will weaken the crystal lattice. No correlation was found between the amount of quartz and the rocks resistance to fragmentation however, indicating that the hardness of the quartz crystals still applies. The effect of feldspars on a rocks technical properties is more evident when combining the amount of plagioclase and alkali feldspar (Figs. 34-35), rather than comparing them separately (Figs. 30-31 and 32-33). The rocks resistance

34

to fragmentation shows no correlation with the amount of feldspars (Fig. 34), while the rocks resistance to abrasion on the other hand shows a very strong correlation (Fig. 35). A higher amount of feldspar correlates with better resistance to abrasion, and vice versa. These results coincide well with the fact that the feldspar crystals are relatively hard, and are therefore expected to have a high resistance to abrasion. Feldspar crystals are however likely to have a highly developed cleavage and may exhibit high amounts of intragranular cracks, properties that will lower the resistance to fragmentation. This seems not to be the case for the rock samples of this study however, given that the resistance to fragmentation is not lowered with increased amounts of feldspar. When combining the amounts of quartz and feldspar in the rock samples, it is evident that the rocks resistance to abrasion favours a higher total amount of these minerals (Fig. 37). As mentioned, quartz and feldspars are hard minerals, and will naturally resist abrasion more easily. The amount of mica (biotite, muscovite and chlorite) has an evident effect on the rocks resistance to abrasion in the samples studied here, with lower amounts of mica correlating with a better resistance to abrasion (Fig. 39). The softness of the mica minerals is most likely the cause for this. The content of mica minerals seems not to have an evident effect on the rocks’ resistance to fragmentation however. The amount of mica in the rock samples varies throughout the investigated area. Around 40 rock samples (out of a total 112) have a mica-content lower than 7%, as required for use as concrete aggregate. Regarding the amount of amphibole in the rock samples, it does not seem to affect the properties of the rock in this case (Figs. 40-41), most likely due to the low amounts present in the considered rock samples.

8.3 Micro Analysis

8.3.1 Grading of Grain Boundaries

The grade of intergrowth between adjacent grains in a rock shows a clear correlation with the Los Angeles value of the same rock (Fig. 43), where rocks with more complex grain boundaries are likely to have better resistance

to fragmentation compared with rocks that exhibit less complex grain boundaries. It is also made clear that rocks with observed gneissic texture will have predominantly less complex grain boundaries (Fig. 45), which could partly explain why gneissic rocks exhibit less favourable technical properties (Fig. 26). Only one gneissic rock sample was obtained from within the TIB, were gneissic rocks are uncommon given the TIBs low grade of deformation. This explains to a great extent why the technical properties of the rock types in the TIB are profoundly more favourable than those of the Eastern and Western Segments (Fig. 11), reinforcing the fact that the tectonic history of a rock may reveal its technical properties prior to analysis.

8.3.2 Evaluation of Method

The results obtained in this study from the grading of grain boundaries shows that it is possible to obtain quantitative data reflecting this property, which can then be successfully used to evaluate a rocks technical properties, in this case especially the rocks resistance to fragmentation and in a lesser degree the rocks resistance to abrasion. The results also show that the method may be an effective way to distinguish between rocks that have i.e. a grade 1.5 and 4 of intergrowth, but will likely not be useful when comparing rocks with i.e. a grade 2 and 2.5 of intergrowth. As seen in Figure 43, rock samples with a 2 or 2.5 grade of intergrowth show great variation when it comes to LA values. Perhaps other properties of the rock are more dominating here, such as microcracks, making it hard to draw any conclusions from these parts of the results. This coincides with the fact that this method should be used as a tool for obtaining “the big picture” of the concerned area and perhaps not for detailed studies.

There is however an uncertainty of how compatible the method is when it comes to comparing the results with other studies. The grade of intergrowth in the rock samples was decided based on the model from Hellman et al. (2006) in Figure 7, but is to some extent also based on individual interpretations, why the results may turn out somewhat different if carried out by another individual. Furthermore, the grading is most likely a comparison between the samples concluded in this study, and hence the results are not adapted to any global scale of grain boundary

35

intergrowth. The results from this study may therefore not be compatible with a similar study carried out in a completely different area.

Considering the positive outcome of the results, the method should be considered when searching for simple and cost-effective methods for analysing and evaluating rock properties in a general manner, as results can be obtained from a large amount of rock samples in a short period of time.

8.3.3 Altered Plagioclase

Altered plagioclase crystals are known to affect the strength of rocks, either weakening them because of overly aggressive alteration (Göransson et al., 2004), or strengthening them due to the more flexible properties obtained from alteration (e.g. Åkesson et al., 2004; Hellman et al., 2011). Particularly the resistance to fragmentation has been found to be affected by this. The results obtained in this study, considering the amount of altered plagioclase in a total of 112 samples, complies with the results obtained by Åkesson et al. (2004) and Hellman et al. (2011), indicating that an increased amount of altered plagioclase does in fact seem to increase the rocks resistance to fragmentation (Fig. 47). A weak correlation can also be seen between increased amount of altered plagioclase and increased resistance to abrasion (Fig. 48). These results indicate that this method for briefly determining the amount of altered plagioclase in a large number of rock samples may be successfully used as a cost-effective way to estimate the rocks’ resistance to fragmentation at an initial stage of investigation. There are however disadvantages of this method as well, and they are nearly identical to those found for the method of grading the amount of intergrowth between adjacent grains. The method is based on individual interpretations, why the results may turn out somewhat different if carried out by another individual. Furthermore, the grading is most likely a comparison between the samples concluded in this study, and hence the results are not adapted to any global scale regarding the amount of altered plagioclase. The results from this study may therefore not be compatible with a similar study carried out in a completely different area.

8.4 Image Analysis

8.4.1 Grain Size and Grain Size Analysis

The grain size and grain size distribution of the samples were initially expected to show a correlation with the technical properties of the rock samples. This correlation does not appear to exist, however. The samples do however show very similar results in terms of grain size distribution (Figs. 55-56) and mean grain size (Figs. 57-58), which might prevent a possible correlation from being made visible. It is also possible that the influence of the grain size and grain size distribution is insignificant in this area, and that other properties affect the technical properties to a much greater extent. This is however less likely true, given that rocks with porphyritic texture in the area tends to increase the resistance to fragmentation and abrasion (Fig. 59), while rocks with non-porphyritic texture are more unpredictable. This has however not been determined numerically in this study, and is only based on observations done during sampling in the field and during microscope analysis.

8.4.2 Perimeter

No correlation was found between the perimeter of the rock samples and their technical properties (Figs. 60-61). The perimeter results were however also very similar for all samples, with values roughly between 12 and 20 mm/mm2. Åkesson et al. (2001) found a correlation between the perimeter and technical properties, but did on the other hand have perimeter values of up to 50 mm/mm2. The lower perimeter values (<20 mm/mm2) of Åkesson et al. (2001) did not show an equally strong, or even absent, correlation, why there is reason to believe that the perimeter of the samples in this present study are simply too similar to reveal a possible correlation.

8.4.3 Crystal Alignment The results from the crystal alignment analysis shows that all samples but one plot below 0.4 on the 0-1 scale, revealing that they are considered more massive than foliated. It is therefore not surprising that there is no obvious correlation between the foliation of the samples and their technical properties (Figs. 62-63).

36

8.4.4 Evaluation of Method

The method used in this thesis for digitalisation of mineral grains in a microscope image from a thin section comes with both advantages and disadvantages, the same as for many other methods concerning image analysis. First and foremost, the method is very simple as well as relatively time efficient and cost-effective, considering that analysis of one single image with digitalised grains can produce a large amount of data concerning many different properties of the rock sample. The method requires no more than a basic background in geology, mineralogy or similar in order carry out the simplest analysis steps like identification of mineral grain boundaries. The steps carried out in the image processing software’s (Photoshop CS6, ImageJ 1.50i and CSD Correction 1.53) are simple as well, considering that the image with digitalized grains require a very low amount of processing prior to analysis (Table 1). This in turn yields better regulated results, given that the more the data is interpreted or manipulated by a computer, the less reliable the results may be considered.

Microscopy is not only an established and well-known method for analysing rock samples, but it is also one of the most cost-effective methods. By using images from a microscope, the pictures will have a very good resolution as well, increasing the possibility to distinguish the grain boundaries and very small grain sizes. In combination with the “Quick selection” tool used in Photoshop CS6 for tracing minerals both precisely and efficiently, the accuracy of the results is considered very high. For samples with larger grain sizes, several images can be taken at the highest magnification, and then be stitched together in order to analyse a larger area but still contain the high resolution. In order to enhance the precision of the method even more, SEM/BSE images may be used in combination with microscope images, as the SEM/BSE images may yield very clear grain boundaries. The downside with these images are however the fact that the grain boundaries of minerals within the same phase that lay adjacent each other are not visible. The method will also be more time consuming and costly if SEM/BSE images are to be used.

Another advantage with the method is that the mineralogical composition of the sample can be

determined at the same time, which has not been done in this thesis however. Different mineral types can be assigned different colours when digitalising the grains in the image, after which the image processing program ImageJ 1.50i can distinguish between the different phases. The results can then additionally be used for analysing the impact of the specific area of each mineral phase, their nucleation rates, the amount of monominerallic aggregates, or simply for comparing the volume of each mineral phase instead of the modal amounts used frequently today.

There are however a number of downsides to the method as well. A disadvantage of the method when used in this thesis was the fact that the digitalisation of mineral grains was only applicable on images from rock samples with less complex grain boundaries and low amounts of alteration. Complex grain boundaries and alteration makes it difficult, if not impossible, to distinguish grain boundaries and the results would therefore be far more uncertain. Unfortunately, only rocks from the Eastern and Western Segments where able to be analysed in this study because of this, as the rocks from the TIB were too complex in their texture to be able to be analysed. Neither were porphyritic rock types possible to analyse, given that they consist of large amounts of very small grains gathered in aggregate bands, making it impossible, or at the least extremely time consuming, to distinguish between every single grain. The problem with complex rock textures are however not exclusive for this method, but is rather an issue for nearly all types of image analysis methods.

Another issue when digitalising mineral grains was to distinguish between grain boundaries and cleavage plains in certain grains, mostly in mica minerals. Quartz grains displayed subgrain formation in many samples as well, adding additional uncertainty to the analysis.

The precision of the “Quick Selection” tool in Photoshop CS6 for tracing mineral grains is as mentioned relatively high in comparison with other methods, but a problem does arise when it comes to tracing the outlines of very small or very flaky minerals. If these minerals were to be traced as accurately as the larger, less complex minerals, the method would be far more time consuming, as the size of the tool range would have to be repeatedly adjusted according to mineral size during the

37

digitalisation process. This can perhaps be considered negligible regarding the very small grains, but in terms of flaky minerals being traced and digitalised as more rounded, this could have a considerable effect on for example the perimeter results depending on the amount of flaky minerals in the sample. Furthermore, the conversion of 2D data obtained from measurements in ImageJ 1.50i into 3D data in CSD Corrections 1.53 involves the mathematical assumptions of stereology. Stereological conversions are of complex nature, and a large number of equations have been used through time for the same purpose. In this case, Higgins (2000) has used and modified the Saltikov method in the program CSD Corrections 1.53. This conversion adds a computerized interpretation into the result, why the results may vary to a certain degree depending on the input variables selected in the program prior to conversion of data.

9. Conclusions

The most obvious conclusions made from this thesis are summarized below.

As expected, the technical properties of the TIB rocks are more favourable for use in concrete-, road- and railway constructions than rocks of the Eastern and Western Segments. This can be attributed to the apparent difference in tectonic history of the areas, were the rocks of TIB are relatively undeformed, while the rocks of the Eastern and Western Segments have been subject to large amounts of stress and strain due to a number of orogenic events.

The above mentioned tectonic history of the area has led to an increased amount of gneissic rocks in the Eastern and Wstern Segment. By observations made in the field during sampling regarding gneissic or non-gneissic texture, it has been made visible that rocks with gneissic texture are more likely to correspond with less favourable technical properties. By grading the amount of intergrowth between adjacent grains, it has also been determined that gneissic rocks display less complex grain boundaries, most likely due to the

recrystallization of minerals that takes place during metamorphic events.

Quantitative results of mean grain size and grain size distribution, as well as the alignment of mineral grains, show no correlation with the technical properties of the rock samples analysed. This is however suspected to be caused by the similarity of the analysed samples, preventing any correlation from being made visible. Although not quantitatively determined, observations made in the field and during microscope analysis regarding the presence of porphyritic texture reveal that porphyritic rocks tend to exhibit more favourable technical properties than non-porphyritic rocks are likely to do.

Considering the non-existing correlation between grain size, grain size distribution and alignment of grains compared to the technical properties, there is also a suspicion that micro-cracks could have a strong influence on the material on this scale.

The mineralogy of the rock samples in this study corresponds to the technical properties as expected, the most evident being that increased amounts of feldspars correlates with better resistance to abrasion, while increased amounts of mica lowers the rocks resistance to abrasion. Quartz was shown to be the only mineral affecting the resistance to fragmentation, as there is a very weak but evident correlation between high amounts of quartz and better resistance to fragmentation.

The grade of intergrowth between adjacent grains showed a strong correlation to the technical properties of the rock samples in this study, particularly with their resistance to fragmentation. More complex grain boundaries will give more favourable technical properties.

The results from the grading of grain boundaries imply that the method can be used in an early stage of analysis, and is a cost-effective way to retrieve basic information on a large number of rock

38

samples. The method is preferably used to distinguish between and estimate the technical properties of rock samples with significantly low or high grade of intergrowth, i.e. 1.5 and 4 on the scale of 1-5, and not between samples with a more similar grade of intergrowth (i.e. 2 and 2.5).

The image analysis method used in this thesis, were mineral grains were digitalised with the “Quick Selection” tool in Photoshop CS6 and analysed in ImageJ 1.50i and CSD Corrections 1.53, is considered to be both cost-effective and time-efficient. The method can produce a large amount of data on several different parameters from one single measurement. A disadvantage of the method is that too complex, heavily altered or porphyritic rock samples are not able to be analysed as time efficiently, nor with good enough precision.

Higher amounts of altered plagioclase correlates with a higher LA value, which coincides with results obtained by Åkesson et al. (2004) and Hellman et al. (2011). Plagioclase that has been exposed to alteration, e.g. sericite alteration, is believed to make its host

rock more resistant to brittle deformation (e.g. Åkesson et al., 2004; Hellman et al., 2011). Åkesson et al. (2004) demonstrates that feldspars with sericite alteration have less crack abundance, the reason being that sericitized feldspar grains exhibit more flexible properties than do an unaltered feldspar grain.

10. Acknowledgments I would like to thank my supervisor Johan

Hogmalm for contributing with ideas and

knowledge in order to design this project, as

well as for the help and input along the way and

for critically reviewing the results of the thesis.

Thanks also to my co-supervisor Thomas

Eliasson at the Geological Society of Sweden,

for contributing with the initial idea of the

project, as well as extensive supervision, advise

and input throughout the whole project. Many

thanks also to the Geological Society of

Sweden for providing the material on which

this thesis was based on, and for providing a

workspace for the microscopic analysis. Last

but not least, thanks the Department of Earth

Sciences at the University of Gothenburg for

making this project possible.

39

11. References

Andréasson, P. G., & Dallmeyer, R. D. (1995).

Tectonothermal evolution of high‐alumina rocks within the Protogine Zone, southern Sweden. Journal of Metamorphic Geology, 13(4), 461-474.

Arvidsson, H., & Loorents, K. J. (2008). Inverkan av köld och vatten på glimmerhaltiga bärlager. VTI, Statens väg- och transportforskningsinstitut, 2-2008.

Bergkross i betong - Krossat berg ersätter naturgrus, Cementa (n.d.). Retrieved from http://www.cementa.se/sv/system/files_force/assets/document/d7/bf/bergkrossibetong.pdf?download=1, 2016-07-05.

Bergström, U., Eliasson, T., Engdahl, M., Jelinek, C., Lindh, Å., Lundqvist, L., Lång, L-O., Persson, L., Persson, T., & Pile, O. (2015). Geologiska data mellan Göteborg och Jönköping del I: Göteborg-Borås. The Geological Survey of Sweden, SGU-rapport 2015:18.

Bergström, U., Göransson, M. & Shomali, H. (2008) Beskrivning till bergkvalitetskartan Partille och Lerums kommuner. Sveriges geologiska under-sökning K 94, 33 pp.

Brander, L., Appelquist, K., Cornell, D., & Andersson, U. B. (2012). Igneous and metamorphic geochronologic evolution of granitoids in the central Eastern Segment, southern Sweden. International Geology Review, 54(5), 509-546.

Brattli, B. (1992). The influence of geological factors on the mechanical properties of basic igneous rocks used as road surface aggregates. Engineering Geology, 33(1), 31-44.

Gaál, G., & Gorbatschev, R. (1987). An outline of the Precambrian evolution of the Baltic Shield. Precambrian Research, 35, 15-52.

Geological Survey of Sweden (2016). Produkt: Ballast (Visningstjänst). Retrieved 2016-11-08, from http://resource.sgu.se/data/service /wms/130/ballast.

Göransson, M. (2011). Ersättningsmaterial för naturgrus. Sveriges geologiska undersökning, 10, 32.

Göransson, M., Bergström, U., Shomali, H., Claeson D. & Hellström, F., 2008: Beskrivning till berg-kvalitetskartan delar av Kungsbacka och Varbergs kommuner. Sveriges geologiska undersökning K 96, 37 pp.

Göransson, M., Persson, L., & Wahlgren, C. H. (2004). The variation of bedrock quality with increasing ductile deformation. Bulletin of Engineering Geology and the Environment, 63(4), 337-344.

Hegardt, E. A., Cornell, D., Claesson, L., Simakov, S., Stein, H., & Hannah, J. (2005). Eclogites in the central part of the Sveconorwegian Eastern Segment of the Baltic Shield: support for an extensive eclogite terrane. GFF, 127(3), 221-232.

Hellman, F., Åkesson, U., & Eliasson, T. (2011). Kvantitativ petrografisk analys av bergmaterial: en metodbeskrivning. VTI rapport 714.

Higgins, M. D. (2000). Measurement of crystal size distributions. American Mineralogist, 85(9), 1105-1116.

Higgins, M. D. (2006). Quantitative textural measurements in igneous and metamorphic petrology. Cambridge University Press.

Höbeda, P. (1971). Bergmaterial till vägbyggnad. Statens väginstitut, Stockholm. Specialrapport 84. 1–126.

Höbeda P. (1995). FAS Asfaltsbok. Kapitel:

Stenmaterial. Published by Föreningen för Asfaltsbeläggningar i Sverige. 85–110.

Höbeda, P. & Bünsow, L. (1974). Inverkan av

glimmer på packnings- och bärighetsegenskaperna hos berggrus. VTI rapport 55. Statens väg och trafikinstitut, Stockholm. 1–29.

Högdahl, K., Andersson, U. B., & Eklund, O.

(Eds.). (2004). The Transscandinavian Igneous Belt (TIB) in Sweden: a review of its character and evolution (Vol. 37). Geological survey of Finland.

40

Janssen, C., Wagner, F., Zang, A., & Dresen, G. (2001). Fracture process zone in granite: a microstructural analysis. International Journal of Earth Sciences, 90(1), 46-59.

Johansson, Å., Meier, M., Oberli, F., &

Wikman, H. (1993). The early evolution of the Southwest Swedish Gneiss Province: geochronological and isotopic evidence from southernmost Sweden. Precambrian Research, 64(1), 361-388.

Johansson, E. (2011). Technological properties of rock aggregates. Luleå: Luleå Tekniska Universitet. (Doctoral Thesis / Luleå University of Technology).

Johansson, E., Miskovsky, K., Loorents, K. J., & Löfgren, O. (2008). A method for estimation of free mica particles in aggregate fine fraction by image analysis of grain mounts. Journal of Materials Engineering and Performance, 17(2), 250-253.

Kowallis, B. J., & Wang, H. F. (1983). Microcrack study of granitic cores from Illinois deep borehole UPH-3. Journal of Geophysical Research 88, 7373 – 7380.

Lagerblad, B., Westerholm. M., Fjällberg, L., & Gram, H. E. (2011). Bergkrossmaterial som ballast i betong. CBI report 1:2008. CBI Betonginstitutet, Stockholm. ISBN 978- 91-976070-1-8.

Larson, S. Å., & Berglund, J. (1992). A

chronological subdivision of the Transscandinavian Igneous Belt—three magmatic episodes?

Larson, S. Å., Stigh, J., & Tullborg, E. L.

(1986). The deformation history of the eastern part of the southwest Swedish gneiss belt. Precambrian Research, 31(3), 237-257.

Lindqvist, J., & Åkesson, U. (2001). Image

analysis applied to engineering geology, a literature review. Bulletin of Engineering Geology and the Environment, 60(2), 117-122.

Lindqvist, J. E., Åkesson, U., & Malaga, K.

(2007). Microstructure and functional properties of rock materials. Materials characterization, 58(11), 1183-1188.

Loorents, K. J., Johansson, E., & Arvidsson, H. (2007). Free mica grains in crushed rock aggregates. Bulletin of Engineering Geology and the Environment, 66(4), 441-447.

Lundgren, L. (2012). Variation in rock quality

between metamorphic domains in the lower levels of the Eastern Segment, Sveconorwegian Province. Dissertations in Geology at Lund University, no 324.

Mainwaring, P. R., & Petruk, W. (1989).

Introduction to image analysis in the Earth and mineral science. Image Analysis in Earth Sciences. Mineralogical Association of Canada, Short Course Handbook, 16, 1-5.

Mazurek, M., Alexander, W. R., & MacKenzie,

A. B. (1996). Contaminant retardation in fractured shales: matrix diffusion and redox front entrapment. Journal of Contaminant Hydrology, 21(1), 71-84.

Miskovsky, K. (2004). Enrichment of fine mica

originating from rock aggregate production and its influence on the mechanical properties of bituminous mixtures. Journal of materials Engineering and performance, 13(5), 607-611.

Mohammad, Y. O., Cornell, D. H., Danielsson,

E., Hegardt, E. A., & Anczkiewicz, R. (2011). Mg-rich staurolite and kyanite inclusions in metabasic garnet amphibolite from the Swedish Eastern Segment: evidence for a Mesoproterozoic subduction event. European Journal of Mineralogy, 23(4), 609-631.

Morgan, D. J., & Jerram, D. A. (2006). On

estimating crystal shape for crystal size distribution analysis. Journal of Volcanology and Geothermal Research, 154 (1), 1-7.

Möller, C., Andersson, J., Lundqvist, I., &

Hellström, F. (2007). Linking deformation, migmatite formation and zircon U–Pb geochronology in polymetamorphic orthogneisses, Sveconorwegian Province, Sweden. Journal of Metamorphic Geology, 25(7), 727-750.

Nesse, W. D. (2012). Introduction to

mineralogy (No. 549 NES).

41

Om Projektet - Götalandsbanan. (n.d.). Retrieved October 03, 2016, from http://xn--gtalandsbanan-imb.nu/omprojektet.4.252ae5fb14a14b7e8bbe5c1.html.

Persson, L., & Göransson, M. (2005).

Mechanical quality of bedrock with increasing ductile deformation. Engineering geology, 81(1), 42-53.

Quist, J., & Evertsson, C. M. (2010).

Application of discrete element method for simulating feeding conditions and size reduction in cone crushers. In XXV International Mineral Processing Congress (IMPC) 2010 Proceedings/Brisbane, QLD, Australia/6-10 September 2010 (pp. 3337-3347).

Stenlid, L., 2000: Utvärdering av micro-

Devalmetoden. Slutrapport SBUF projekt nr 5002. Skanska Sverige AB, Vägtekniskt Centrum Nord, Bålsta, 16 pp.

Stjärnered, P-O. (2016). Här ska tågen stanna.

SVT Nyheter Väst, retrieved 2016-09-12 from http://www.svt.se/nyheter/lokalt/vast/har-stannar-de-nya-tagen.

Streckeisen, A. (1967). Classification and

nomenclature of igneous rocks. Neues Jahrbuch für Mineralogie, Abhandlungen 107, 144-240.

Sveriges geologiska undersökning, 2015a: Grus, sand och krossberg 2014. Periodiska publikationer 2015:2, 31 s.

Svensk Standard (1997a): SS-EN 1097-2.

Ballast – Mekaniska och fysikaliska egenskaper – Del 2: Bestämning av motstånd mot sönderdelning. Swedish Standards Institute, Sweden.

Svensk Standard (1997b): SS-EN 1097-1.

Ballast – Mekaniska och fysikaliska egenskaper – Del 1: Bestämning av nötningsmotstånd (Micro-Deval). Swedish Standards Institute, Sweden.

Svensk Standard (2004a): SS-EN 1097-9. Ballast – Mekaniska och fysikaliska egenskaper – Del 9: Bestämning av motstånd mot nötning av dubb-däck (Nordiska kulkvarnsmetoden). Swedish Standards Institute, Sweden.

Söderlund, U., Isachsen, C. E., Bylund, G.,

Heaman, L. M., Patchett, P. J., Vervoort, J. D., & Andersson, U. B. (2005). U–Pb baddeleyite ages and Hf, Nd isotope chemistry constraining repeated mafic magmatism in the Fennoscandian Shield from 1.6 to 0.9 Ga. Contributions to Mineralogy and Petrology, 150(2), 174-194.

Tavares, L. M., & das Neves, P. B. (2008).

Microstructure of quarry rocks and relationships to particle breakage and crushing. International Journal of Mineral Processing, 87(1), 28-41.

Åkesson, U., Hansson, J., & Stigh, J. (2004).

Characterisation of microcracks in the Bohus granite, western Sweden, caused by uniaxial cyclic loading. Engineering Geology, 72(1), 131-142.

Åkesson, U., Lindqvist, J., Göransson, M., &

Stigh, J. (2001). Relationship between texture and mechanical properties of granites, central Sweden, by use of image-analysing techniques. Bulletin of Engineering Geology and the Environment, 60(4), 277-284.

Åkesson, U., Stigh, J., Lindqvist, J. E., &

Göransson, M. (2003). The influence of foliation on the fragility of granitic rocks, image analysis and quantitative microscopy. Engineering Geology, 68(3), 275-288.

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12. Appendix

12.1 Appendix A: General and technical data

Sample ID N-S E-W Rock Type (QAP) Gneissic Tectonic

Unit * Porphyritic

MGO035002B 6389234 325179 Quartz Diorite WS

MGO035004A 6395487 326491 Monzogranite WS Yes

MGO035005A 6393555 328550 Syenogranite WS

MGO035006A 6392540 327682 Tonalite WS Yes

MGO035013A 6394752 350308 Tonalite WS

MGO035014A 6396010 335631 Monzogranite WS

MGO035016A 6396295 342178 Monzogranite WS Yes

MGO035018A 6391557 333629 Monzogranite WS

MGO035025A 6401488 342003 Monzogranite WS

MGO035031A 6389494 339883 Granodiorite WS

MGO035036A 6397892 334366 Monzogranite WS Yes

MGO035037A 6396108 352450 Monzogranite ES

MGO035039A 6396605 361771 Granodiorite ES

MGO035044A 6397587 358420 Quartz Monzonite ES

MGO035046A 6390050 356492 Granodiorite ES

MGO035050A 6391007 331586 Granodiorite WS

MGO035070A 6400582 344891 Granodiorite WS

MGO035071A 6390465 345516 Granodiorite WS

MGO035071B 6390465 345516 Granodiorite WS

MGO035072A 6394756 339224 Granodiorite WS

MGO035073A 6402360 333671 WS

MGO035080A 6397438 366059 Monzogranite WS

MGO035081A 6400444 373256 Monzogranite Yes WS

MGO035082A 6404464 387787 Syenogranite WS

MGO045025A 6398925 333293 Syenogranite WS

MGO045026A 6390590 350606 Syenogranite MZ Yes

MGO045031A 6404351 393563 Syenogranite Yes ES

MGO045033A 6391491 384232 Syenogranite Yes ES

MGO045034A 6392494 383161 Monzogranite Yes ES

MGO045038A 6394508 358236 Monzogranite ES

MGO045041A 6391649 359824 Granodiorite Yes ES

MGO045046A 6389794 323168 WS

MGO045048A 6399531 352307 Tonalite ES

MGO045051A 6393684 382249 Monzogranite Yes ES

MGO045055A 6390103 379356 Syenogranite Yes ES

MGO045056A 6405441 387946 Monzogranite Yes ES Yes

MGO045057A 6400350 382457 Syenogranite Yes ES

MGO045059A 6401444 380419 Monzogranite Yes ES

MGO045060A 6402642 378162 Syenogranite Yes ES

43

Sample ID N-S E-W Rock Type (QAP) Gneissic Tectonic

Unit * Porphyritic

MGO045064A 6407515 376359 Syenogranite Yes ES

MGO045065A 6403260 373237 Monzogranite Yes ES

MGO045069A 6405270 379986 Monzogranite Yes ES

MGO045070A 6398782 375290 Tonalite Yes ES

MGO045071A 6395414 368809 Tonalite Yes ES

MGO045082A 6395250 353219 Syenogranite ES

MGO045083A 6398201 360280 Granodiorite ES

MGO045084A 6400892 365350 Monzogranite Yes ES

MGO045085A 6402486 365272 Quartz Syenite Yes ES

MGO045085B 6402486 365272 Granodiorite Yes ES

MGO045087A 6393214 368116 Granodiorite Yes ES

MGO045092A 6394291 348479 Monzogranite WS Yes

MGO045094A 6393220 348336 Tonalite MZ Yes

MGO055025A 6389515 352837 Monzogranite ES

MGO055026A 6390028 352607 Syenogranite ES

MGO055074A 6393796 348014 MZ Weakly

MGO082001 6394234 460070 Monzogranite TIB

TEN062001 6409163 444006 Monzogranite PZ Yes

TEN062002 6399176 445339 Monzogranite PZ Weakly

TEN062003 6406058 436615 Monzogranite PZ Weakly

TEN062004 6402027 435860 Monzogranite PZ

TEN062005 6409227 447621 Quartz Monzodiorite TIB Yes

TEN062006 6408897 444138 Syenogranite PZ Yes

TEN062007 6404132 448201 Syenogranite PZ Yes

TEN062008 6404018 448233 Syenogranite PZ Yes

TEN062010 6407154 464592 Quartz Monzonite TIB

TEN062011 6401870 466056 Monzogranite TIB

TEN062012 6402164 433914 Monzogranite PZ Weakly

TEN072001 6401061 429018 Monzogranite Yes WS

TEN072002 6398954 429591 Monzogranite Yes WS

TEN072003 6401511 447122 Monzogranite PZ Yes

TEN072004 6405529 467933 Monzogranite TIB

TEN072005 6404126 472773 Monzogranite TIB Yes

TEN072007 6407963 472046 Monzogranite TIB

TEN072009 6406544 460674 Granodiorite TIB

TEN072010 6403328 456563 Monzogranite TIB

TEN072011 6398540 455422 Quartz Monzonite PZ Yes

TEN072012 6389580 456237 Monzogranite PZ Yes

TEN072013 6402772 463459 Monzogranite TIB Weakly

TEN072014 6406148 462634 Quartz Monzonite TIB

TEN072018 6394775 452718 Monzogranite PZ

TEN072021 6397521 466246 Granodiorite TIB Weakly

TEN072025 6410510 460905 Quartz Gabbro TIB

44

Sample ID N-S E-W Rock Type (QAP) Gneissic Tectonic

Unit * Porphyritic

TEN072026 6413594 449331 Granodiorite TIB

TEN072027 6411243 466436 Quartz Monzonite TIB

TEN072028 6397539 461371 Quartz Monzodiorite TIB

TEN082002 6415987 470463 Quartz Monzonite TIB Yes

TEN082004 6392295 449383 Monzogranite TIB Yes

TEN082007 6412550 457859 Quartz Diorite TIB Weakly

TEN082008 6413693 463096 Quartz Monzonite TIB Yes

TEN082009 6416857 463309 Quartz Monzonite TIB

TEN082010 6415718 468721 Monzonite TIB

TEN082011 6415008 468104 Monzonite TIB

TEN082012 6412800 472747 Monzogranite TIB

TEN082013 6393341 467506 Quartz Monzodiorite TIB

TEN082016 6384836 455288 Syenogranite PZ Yes

TEN082017 6383153 457666 Monzogranite TIB Yes

TEN082018 6386820 451295 Monzogranite Yes PZ

TEN082020 6396164 439396 Monzogranite Yes PZ

TEN082021 6393435 434304 Monzogranite Yes PZ Yes

TEN082024 6425942 463544 Granodiorite TIB

TEN082025 6425983 463492 Monzogranite TIB

TEN082026 6421612 460340 Monzogranite TIB Yes

TEN082029 6421526 466381 Monzogranite TIB

TEN082030 6425482 469947 Syenogranite TIB Yes

TEN140001 6402481 404551 Monzogranite Yes ES

TEN140002 6410678 416386 Monzogranite Yes ES

TEN140004 6402867 402299 Monzogranite Yes ES

TEN140005 6397378 398583 Quartz Monzonite Yes ES

TEN140006 6400769 393880 Monzogranite Yes ES

TEN140007 6396265 394112 Monzogranite Yes ES

TEN140009 6403374 399540 Monzogranite Yes ES

TEN140010 6402764 406254 Quartz Monzodiorite Yes ES

TEN140011 6409901 399122 Granodiorite Yes ES

TEN140012 6409607 397173 Monzogranite Yes ES

TEN140013 6409587 397088 Monzogranite Yes ES

TEN140014 6408816 404122 Monzogranite Yes ES

* ES = Eastern Segment; WS = Western Segment; PZ = Protogine Zone; MZ = Mylonite

Zone; TIB = Transscandinavian Igneous Belt

45

Sample ID Density (g/cm3)

Studded Tyre Test (%)

Los Angeles (%)

MicroDeval (%)

MGO035002B 2,66 12,7 21,4

MGO035004A 2,69 18,5 29,4

MGO035005A 2,64 12,6 31,7 9

MGO035006A 2,81 25 29,4

MGO035013A 2,81 15,4 19,8

MGO035014A 2,71 17,6 28,3

MGO035016A 2,67 13,4 26,2 10

MGO035018A 2,76 21,8 26,7

MGO035025A 2,64 14,2 28,7 8

MGO035031A 2,74 22,6 33,5

MGO035036A 2,69 18,5 29,8

MGO035037A 2,68 17,5 30,7

MGO035039A 2,66 16,3 44,1

MGO035044A 2,76 20,4 37,5

MGO035046A 2,69 22,3 42,1

MGO035050A 2,74 13,6 22,3

MGO035070A 2,71 16,6 24,9

MGO035071A 2,75 20,8 28,5 17

MGO035071B 2,73 18,9 28 13

MGO035072A 2,78 22,2 37,6

MGO035073A 2,67 18,1 33,5

MGO035080A 2,69 20,3 44,8 14

MGO035081A 2,63 12,2 28,2 7

MGO035082A 2,64 16,7 39

MGO045025A 2,71 26,5 46,2

MGO045026A 2,68 11,2 22 7,3

MGO045031A 2,63 11,4 30,4 7,6

MGO045033A 2,64 16,3 37,1 8,6

MGO045034A 2,72 20,8 40,2 13,2

MGO045038A 2,71 19,2 31,5 12,4

MGO045041A 2,72 17,3 29,6 11,7

MGO045046A 2,63 13,4 25,9 7

MGO045048A 2,76 15,8 25,1

MGO045051A 2,69 16,4 35 10,2

MGO045055A 2,63 15,1 36,9 15,1

MGO045056A 2,7 12,8 23,3

MGO045057A 2,69 13,3 25,6

MGO045059A 2,74 13,1 21,3

MGO045060A 2,64 11,7 23

MGO045064A 2,65 13,9 33,4

MGO045065A 2,74 21,1 31,5

MGO045069A 2,63 10,6 26,7

MGO045070A 2,83 21,8 35,5

46

Sample ID Density (g/cm3)

Studded Tyre Test (%)

Los Angeles (%)

MicroDeval (%)

MGO045071A 2,76 18,5 28,2

MGO045082A 2,63 10,9 27,6

MGO045083A 2,7 17,8 40,4

MGO045084A 2,64 14 36,4

MGO045085A 2,74 20,2 32,6

MGO045085B 2,65 13,2 35,9

MGO045087A 2,77 18,1 26,8

MGO045092A 2,71 16,9 20,7

MGO045094A 2,82 16,5 15,5

MGO055025A 2,69 20,9

MGO055026A 2,62 10,8 33,5

MGO055074A 2,82 16,2

MGO082001 2,64 8,3 18,8 5,2

TEN062001 2,72 16,7 11,3

TEN062002 2,64 10 24,2 5,6

TEN062003 2,63 18,7 38,5 11,6

TEN062004 2,62 12,5 40 7,9

TEN062005 2,77 9,9 18,9 6,3

TEN062006 2,62 11,6 25,6 6,4

TEN062007 2,62 8,3 23 5,1

TEN062008 2,67 10,8 20,7 6,7

TEN062010 2,72 13,9 28,9 7,4

TEN062011 2,65 7,6 17,1 5

TEN062012 2,64 15 29,7 8,9

TEN072001 2,76 16,5 39,5 10,7

TEN072002 2,65 17,7 43,3 11,2

TEN072003 2,69 14 22 8,9

TEN072004 2,63 11,1 26 6,3

TEN072005 2,75 12,4 21,4 7,7

TEN072007 2,65 13,9 25,9 8,4

TEN072009 2,68 6,6 15,5 4,1

TEN072010 2,73 9,9 17,8 7,3

TEN072011 2,73 11,5 20,1 7,4

TEN072012 2,7 11,7 21,2 7,4

TEN072013 2,74 9,5 18,1 5,5

TEN072014 2,68 12,2 25,8 6,6

TEN072018 2,65 8,9 22 4,9

TEN072021 2,7 10,1 21 5,8

TEN072025 2,81 17,5 18,7 13,9

TEN072026 2,66 8,2 19,4 5,5

TEN072027 2,67 7,9 17 5,6

TEN072028 2,81 13,1 18,8 8,1

TEN082002 2,68 7,2 18 4,2

47

Sample ID Density (g/cm3)

Studded Tyre Test (%)

Los Angeles (%)

MicroDeval (%)

TEN082004 2,65 10,4 25,8 6,8

TEN082007 2,81 11,6 14,9 9,2

TEN082008 2,68 7,8 15,9 4,6

TEN082009 2,66 9 20,3 5

TEN082010 2,7 9,8 21,9 5,7

TEN082011 2,69 12,7 7

TEN082012 2,65 8,8 22,5 4,7

TEN082013 2,8 8,6 13,8 6

TEN082016 2,64 15,7 22,1 9,7

TEN082017 2,7 6,9 16,7 4,5

TEN082018 2,64 10,2 28,4 6,9

TEN082020 2,71 16,8 36,7 11,2

TEN082021 2,67 16,8 32,3 12,2

TEN082024 2,71 7,9 16,9 4,9

TEN082025 2,66 6,1 14,8 3,5

TEN082026 2,67 10,8 26,4 6,4

TEN082029 2,66 8,3 18,1 5,3

TEN082030 2,64 7,3 19,3 4,4

TEN140001 2,63 11,11 31,73 6,63

TEN140002 2,61 13,57 44,54 8,67

TEN140004 2,62 13,5 39 8,12

TEN140005 2,63 12,38 34,46 7,56

TEN140006 2,68 10,49 24,42 6,31

TEN140007 2,67 15,37 42,41 10,02

TEN140009 2,64 14,22 45,69 9,11

TEN140010 2,68 14,34 32,28 9,91

TEN140011 2,74 19,85 31,92 13,2

TEN140012 2,7 18,39 35,89 10,7

TEN140013 2,63 15,25 34,97 8,34

TEN140014 2,62 14,77 46,29 8

48

12.2 Appendix B: Grade of Intergrowth and Grade of Altered Plagioclase

Sample ID Grade of

Intergrowth (1-5) Grade of Altered Plagioclase (1-5)

MGO035002B 3,0 2

MGO035004A 3,0 2

MGO035005A 2,5 1

MGO035006A 3,0 2

MGO035013A 3,5 1

MGO035014A 3,0 2

MGO035016A 2,0 2

MGO035018A 3,0 3

MGO035025A 2,0 3

MGO035031A 2,5 2

MGO035036A 2,5 3

MGO035037A 2,5 2

MGO035039A 1,5 2

MGO035044A 2,0 3

MGO035046A 2,0 2

MGO035050A 3,0 3

MGO035070A 3,0 3

MGO035071A 2,5 3

MGO035071B 2,5 3

MGO035072A 2,5 2

MGO035073A 2,0 2

MGO035080A 2,5 2

MGO035081A 3,0 3

MGO035082A 2,5 1

MGO045025A 2,5 2

MGO045026A 2,5 3

MGO045031A 2,0 2

MGO045033A 2,0 2

MGO045034A 2,5 3

MGO045038A 3,0 4

MGO045041A 2,0 2

MGO045046A 2,5 2

MGO045048A 3,0 2

MGO045051A 2,0 2

MGO045055A 1,5 2

MGO045056A 3,0 3

MGO045057A 2,0 2

MGO045059A 2,0 2

MGO045060A 2,0 1

MGO045064A 2,5 2

MGO045065A 3,0 3

49

Sample ID Grade of

Intergrowth (1-5) Grade of Altered Plagioclase (1-5)

MGO045069A 3,0 2

MGO045070A 2,0 3

MGO045071A 2,5 3

MGO045082A 3,0 2

MGO045083A 1,5 2

MGO045084A 1,5 1

MGO045085A 2,0 2

MGO045085B 1,5 2

MGO045087A 2,5 2

MGO045092A 3,0 3

MGO045094A 4,0 3

MGO055025A 1,5 3

MGO055026A 2,5 3

MGO055074A 3,0 4

MGO082001 4,0 5

TEN062001 2,5 4

TEN062002 2,5 3

TEN062003 2,0 2

TEN062004 2,5 1

TEN062005 3,5 4

TEN062006 3,0 4

TEN062007 3,0 3

TEN062008 4,0 4

TEN062010 3,0 3

TEN062011 3,0 4

TEN062012 2,0 2

TEN072001 1,5 1

TEN072002 2,0 1

TEN072003 3,0 2

TEN072004 3,5 3

TEN072005 3,5 4

TEN072007 3,5 4

TEN072009 4,5 5

TEN072010 3,0 5

TEN072011 4,0 5

TEN072012 3,5 5

TEN072013 3,5 5

TEN072014 3,0 3

TEN072018 3,5 5

TEN072021 3,0 4

TEN072025 3,5 4

TEN072026 3,0 5

TEN072027 4,0 4

50

Sample ID Grade of

Intergrowth (1-5) Grade of Altered Plagioclase (1-5)

TEN072028 3,0 4

TEN082002 3,5 4

TEN082004 2,5 2

TEN082007 4,0 4

TEN082008 3,0 4

TEN082009 3,5 5

TEN082010 3,5 4

TEN082011 3,0 2

TEN082012 4,0 4

TEN082013 4,0 5

TEN082016 2,5 2

TEN082017 3,0 5

TEN082018 2,0 2

TEN082020 2,0 2

TEN082021 2,0 2

TEN082024 4,0 3

TEN082025 4,0 4

TEN082026 2,0 3

TEN082029 3,0 4

TEN082030 2,5 2

TEN140001 2,0 1

TEN140002 2,0 1

TEN140004 2,0 1

TEN140005 2,5 2

TEN140006 2,5 3

TEN140007 2,5 1

TEN140009 1,5 1

TEN140010 3,0 2

TEN140011 2,5 2

TEN140012 2,5 2

TEN140013 2,5 2

TEN140014 1,5 1

51

12.3 Appendix C: Mineralogy

Note: Includes only the minerals that are of interest for this thesis, other minerals have been left out from this Appendix.

Sample ID Quartz

(%) Alkali Feldspar

(%) Plagioclase

(%) Biotite

(%) Chlorite

(%) Muscovite

(%) Amphibole

(%)

MGO035002B 8,2 33,1 7,6 45,8

MGO035004A 31,2 28,8 30,8 6,2 1,6

MGO035005A 36,7 36 17 9,3

MGO035006A 36 33,6 22 0,6 2,2

MGO035013A 23 40 19,1 0,6 9,1

MGO035014A 28,7 20 33,9 14,6 0,2 0,6

MGO035016A 34,5 26 28,8 6,5 0,2

MGO035018A 27,3 18,7 30,9 15,1 4,8 0,2

MGO035025A 20,8 33,6 34,4 2,2 8

MGO035031A 30 17 35,2 12,6 0,4

MGO035036A 31,6 25,8 32,6 6,4 1,4 2

MGO035037A 29,8 20,8 41,4 5,6 0,2

MGO035039A 27 15,8 47,4 9,4

MGO035044A 8,2 24,4 45,8 8,2 0,4 0,2 12,2

MGO035046A 32 13 42 7,8 0,2 4

MGO035050A 28,2 9 39,6 10,8 1,6 4,6

MGO035070A 30,5 12,8 37,4 16,1 0,4

MGO035071A 27,4 10,6 35,8 17,6

MGO035071B 28,6 13,6 39,4 12

MGO035072A 29,6 7,7 39,4 16,3 3

MGO035073A

MGO035080A 26,1 24,7 42,2 5,8 0,4

MGO035081A 39,8 21,2 33,1 4,2

MGO035082A 34,2 41,1 19,2 1,4 s

MGO045025A 32,3 32,7 10,8 16,8 1,3

MGO045026A 39,9 27,7 14,2 8,6 2,4

MGO045031A 48,5 29,4 14,8 6 0,3

MGO045033A 33,6 41,5 15,5 6 0,8

MGO045034A 26,2 33 20,2 16,3 2,2 s

MGO045038A 18,5 25,9 29,7 1,8 13,5 8

MGO045041A 34,3 4,1 26,9 14,9 2,9 15,4

MGO045046A

MGO045048A 29,2 3,8 36 14,9 2,8 10,8

MGO045051A 41,9 18,4 22,5 12,2 0,3 3,4

MGO045055A 36,8 42,3 13,1 4,6 s

MGO045056A 29,3 27,8 15,5 11,5 3,9 8,4

MGO045057A 39,8 26,7 13,2 10,5 1,8 5,1

MGO045059A 38,7 16,1 21,2 13,9 0,6 6,4

MGO045060A 38,3 36,6 16,9 5,9 0,3

52

Sample ID Quartz

(%) Alkali Feldspar

(%) Plagioclase

(%) Biotite

(%) Chlorite

(%) Muscovite

(%) Amphibole

(%)

MGO045064A 23,1 42 16,1 10,9 1,7 1

MGO045065A 29,9 17,5 22,1 23,4 0,6 4,9

MGO045069A 40,3 23 22,4 7,4 1,8 0,4

MGO045070A 11,6 1,4 37,2 7,2 7,2 30,1

MGO045071A 20,3 1,5 41 17,5 6,2 11,8

MGO045082A 39,5 37,2 10,9 4,4 0,6

MGO045083A 25,5 9,9 37,8 7,3 5,6 11,3

MGO045084A 33,7 28,1 27,9 8,9

MGO045085A 15,4 54,8 16 7,3 2,3

MGO045085B 34,4 7 30 13 7,2 6,8

MGO045087A 31,9 6,4 32 17,3 2,4 8,6

MGO045092A 38,3 13,9 16,4 17,6 9,4

MGO045094A 25,9 4,2 23,5 13,4 8 15,1

MGO055025A 25,4 25 33,8 10 0,8 3

MGO055026A 42 40,2 16,8 0,4

MGO055074A

MGO082001 32,6 30,8 33 1,4 0,6

TEN062001 19,6 25,8 29,4 8,1 9,7

TEN062002 34,7 32,1 25,6 3,7 1,4

TEN062003 35,7 33,2 21,2 4,3 1,9

TEN062004 34,2 36,6 25,3 1 1,6

TEN062005 6,2 22,9 55 12,2 1,1

TEN062006 32,4 50,5 13 2,8

TEN062007 38,5 36,6 17,4 0,2 s 5

TEN062008 27,9 38,8 20,6 5,2 4,1

TEN062010 10,2 35,4 32,1 3,6 17,5

TEN062011 24,5 41,3 28 4,8

TEN062012 42 30,7 22 2,8 0,3 0,1

TEN072001 26,5 27,2 34,6 8,2 1

TEN072002 22,8 34,2 33,8 2,6 4,4

TEN072003 28,4 24,2 24 6,2 11,6

TEN072004 34,8 40 21,2 3,2 x

TEN072005 19,8 30,6 32,6 11,8 x 2,4

TEN072007 25,6 29,6 34,6 7,8 x

TEN072009 23,2 12,4 55,8 7,4 x

TEN072010 27 21,4 32,4 14,2 x 2

TEN072011 15,8 25,2 47 7 1,6 2,2

TEN072012 18,8 24 48,2 5,4 0,4

TEN072013 23,8 26,2 31,2 8 x 8,2

TEN072014 8 49,8 33,8 2,2 5,2

TEN072018 32 22,4 36 5,2 x 2,2

TEN072021 25,8 14,2 48,6 9 x

TEN072025 12,2 54,6 x 7,6 7,2 7,4

53

Sample ID Quartz

(%) Alkali Feldspar

(%) Plagioclase

(%) Biotite

(%) Chlorite

(%) Muscovite

(%) Amphibole

(%)

TEN072026 37,8 18,8 37,6 4,4 0,4

TEN072027 8,6 42,8 35,2 9,8 x x

TEN072028 14,8 14,4 50,2 8,4 10,6

TEN082002 12,14 33,14 39,14 5 + 8,57

TEN082004 30 42,71 23,14 3

TEN082007 12,01 2,95 51,7 15,35 9,28

TEN082008 14,42 46,42 24,28 5,42 4,85

TEN082009 16,28 37,28 39,57 4,57 + 0,57

TEN082010 3,42 32,85 51,71 7,28 0,42 1,57

TEN082011 0,2 39,8 50,2 6,6 1,6

TEN082012 27,4 36,6 30,8 1,6 + 0,2

TEN082013 12,4 13,6 48,8 6,2 + 0,2 15,2

TEN082016 36,4 28,4 15,4 1,4 13

TEN082017 26,2 22,4 41,4 5,6 1,2

TEN082018 26,4 31,6 33,6 5,6 0,4

TEN082020 17,2 21,4 36,8 15,2 5

TEN082021 37,6 26,4 24 7,6 0,4

TEN082024 19,54 18,95 45,04 7,62 0,23 3,09

TEN082025 26,78 28,1 37,46 4,74

TEN082026 26,62 35,76 27,8 3,31 0,13 2,78 0,13

TEN082029 22,63 43,02 26,44 0,78 0,26 3,42

TEN082030 34,3 45,8 13,7

TEN140001 19,1 30,6 42,5 3,1 s 1,3

TEN140002 27,4 26,3 43,9 0,8 0,2

TEN140004 21,8 29,9 42,9 3,8 s

TEN140005 11 37 44,6 4,8 0,3 s

TEN140006 23,4 24,1 43,3 5,7 0,6 0,5

TEN140007 25,3 32,5 31,7 5,6 s 0,1 2,9

TEN140009 23,7 24,8 40,5 7,9 0,2

TEN140010 10,8 21,5 47,9 9,6 0,1 7,4

TEN140011 16,3 12,7 44,9 9 s 15,4

TEN140012 19,9 27,2 38,8 6,6 0,4 0,2 4,4

TEN140013 30,5 32,1 31,6 5,5 0,1

TEN140014 33,6 33,5 29,6 0,4 s

54

12.4 Appendix D: Area and Perimeter

Sample ID Total Area

(mm2) Perimeter

(mm) Perimeter/Area

(mm/mm2) Number of Grains

Measured

MGO035005A 28,3477904 505,865 17,84495345 698

MGO035014A 26,0773902 442,475 16,9677639 518

MGO035025A 29,1716268 561,958 19,26385538 646

MGO035037A 28,3739688 431,302 15,2006229 463

MGO035039A 29,0437459 441,033 15,18512803 484

MGO035073A 28,78194 525,438 18,25582292 646

MGO035080A 29,2143332 461,002 15,77999391 488

MGO035082A 31,8846661 523,014 16,40330805 625

MGO045025A 29,3106853 569,11 19,41646857 725

MGO045034A 27,4020931 402,466 14,68741817 385

MGO045046A 30,4588297 428,857 14,07989093 424

MGO045055A 27,907023 354,822 12,71443393 310

MGO045059A 29,5770569 492,854 16,66338884 639

MGO045060A 28,5308982 442,009 15,49229179 485

MGO055025A 29,7030977 448,493 15,09919957 485

MGO055026A 28,4445408 390,855 13,74094955 365

TEN140001 28,8295588 500,798 17,37099078 594

TEN140002 28,8427404 403,29 13,98237457 426

TEN140004 27,2458083 395,803 14,52711535 393

TEN140006 30,0549182 376,903 12,54047665 353

TEN140007 29,0559077 508,16 17,48904234 549

TEN140009 28,1592101 470,135 16,69560326 509

TEN140012 27,8759639 342,278 12,2786068 314

TEN140013 27,2148216 381,187 14,00659558 384

TEN140014 28,4637767 372,162 13,07493394 337

55

12.5 Appendix E: Texture and Grain Size

Sample ID Alignment Factor Regression

Slope Mean Grain Size

(mm)

MGO035005A 0,25 -2,46 0,4235

MGO035014A 0,33 -0,986 0,801

MGO035025A 0,36 -2,84 0,4266

MGO035037A 0,61 -1,02 0,8324

MGO035039A 0,32 -3,52 0,26

MGO035073A 0,32 -3,44 0,3311

MGO035080A 0,39 -2,01 0,5505

MGO035082A 0,15 -4,48 0,211

MGO045025A 0,22 -4,06 0,296

MGO045034A 0,3 -3,06 0,3844

MGO045046A 0,23 -3,49 0,3251

MGO045055A 0,11 -3,43 0,2691

MGO045059A 0,19 -4,29 0,2523

MGO045060A 0,21 -2,22 0,479

MGO055025A 0,17 -2,81 0,3278

MGO055026A 0,26 -3,64 0,2768

TEN140001 0,14 -3,97 0,2667

TEN140002 0,28 -2,66 0,363

TEN140004 0,16 -4,11 0,1905

TEN140006 0,36 -4,62 0,2318

TEN140007 0,14 -3,33 0,333

TEN140009 0,27 -4,72 0,2425

TEN140012 0,2 -3,27 0,2614

TEN140013 0,22 -3,36 0,2979

TEN140014 0,06 -3,02 0,3621

0=massive; 1=perfectly foliated