the skid resistance performance of different new...

Skid Resistance Performance of NZ Aggregates Dookeeram, Nataadmadja, Wilson and Black

IPENZ Transportation Group Conference, Shed 6, Wellington – 23 – 26 March, 2014

THE SKID RESISTANCE PERFORMANCE OF DIFFERENT NEW ZEALAND AGGREGATE TYPES

AUTHORS Vinay Dookeeram, MEngSt (Transportation), BSc (Civil) Department of Civil and Environmental Engineering, The University of Auckland E-mail: [email protected] Adelia D. Nataadmadja, BE(Hons)(Civil), BCom PhD Candidate Department of Civil and Environmental Engineering, The University of Auckland E-mail: [email protected] Douglas J. Wilson, PhD, BE(Civil), NZCE(Civil), MIPENZ, PIARC Senior Lecturer Department of Civil and Environmental Engineering, The University of Auckland E-mail: [email protected] Philippa M. Black, PhD, MSc(Geology), BSc Professor School of Environment, The University of Auckland E-mail: [email protected] ABSTRACT Road safety is a major concern for road engineers. Skid resistance, which is one of the surface pavement characteristics, is a major component of road safety and should be managed such that it is adequately present at most times. There are a number of factors that affect skid resistance and one of them is the geological properties of surfacing aggregates and how resistant they are to polishing and abrasion. For example, aggregates which have poor resistance to abrasion will usually worn out very quickly even though some of these aggregates provide good skid resistance during their lifespan. Ideally, it is desirable to be able to predict the long term skid resistance performance in terms of equivalent standard axle loadings. This research project forms part of a PhD research programme that aims to assess alternative laboratory based methods to better reflect in-field skid resistance performance. The skid resistance performance of pavement surfaces heavily depends upon the aggregate source and to a lesser extent the aggregate production processes. These aggregates are quarried from varying geological processes and source properties that affect their engineering performance. This paper is an analysis of the infield skid resistance performance of five natural aggregates (two Greywacke, two Basalt and one Andesite) which will be compared to their geological source properties. The aggregates were obtained from various locations around the North Island of New Zealand.

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INTRODUCTION The skid resistance of road surface, which is intrinsically related to the pavement surface’s microtexture and macrotexture, is an important functional component of pavement surface that contributes to road safety on both wet and dry roads. Microtexture, the small-scale texture of pavement aggregate, contributes to skid resistance for vehicles at low speed. Macrotexture, the coarse texture of pavement surface aggregates, helps in reducing the potential for aquaplaning and provides skid resistance at high speeds through the effect of hysteresis. New road surfaces are generally constructed with aggregates that have the characteristics of both good microtexture and macrotexture, in order to provide high skid resistance to vehicles travelling on the road. However, with the effect of traffic loadings, this microtexture is reduced over time with the polishing action of vehicles tyres. Macrotexture is also lost due to the wearing of the surface aggregates by repeated traffic loading and the rise of the bitumen around the chipseals. Insufficient skid resistance will increase the probability of vehicles to skid on the smooth road surface and this hazard is amplified during wet weather. Road Controlling Authorities are concerned about this problem and roads are monitored frequently to ensure that a minimum level of skid resistance is always achieved for the safety of road users. The Polished Stone Value (PSV) test is the most widely accepted laboratory test method to evaluate skid resistance of pavement surface and is commonly used in New Zealand (NZ). A number of studies have acknowledged that this test method has certain limitations, such as:

• it primarily measures only the influence of microtexture of aggregates, while both microtexture and macrotexture are needed to accurately calculate the skid resistance of road surface (Fu, 2000, Won and Fu, 1996, ASTM, Cenek and Jamieson, 2005).;

• the polishing action imposed by the Accelerated Polishing Machine used to polish the samples is not representative of the current traffic volume and composition because the device was developed more than 50 years ago and it was designed based on the typical traffic volume and composition at that time, which have already changed significantly (Wilson, 2006, Woodward et al., 2004);

• the standard PSV test polishes the test samples for six hours and it may not be long enough for the test samples to reach the equilibrium level (Wilson and Black, 2008).

Cenek and Henderson (2012) did a study which suggests that pavement aggregate source is a better predictor of how an aggregate will perform infield rather than the PSV ranking. Following up that finding, this paper assesses the mineralogy of five different natural New Zealand (NZ) aggregates and correlates the mineral components of each aggregate to its infield skid resistance performance. Objective and Scope This project aims to correlate the infield performance of NZ aggregates to their geological source properties. Five aggregates sourced different quarries around North Island of NZ were used in this research project (Table 1). The infield skid resistance data was obtained from roads (local and state highways) around Auckland and Waikato regions that were constructed by using the aggregates used in this research project. Only road surfaces constructed with single coat seal were included in the analysis.

Table 1: List of aggregates used and their quantity and notations

Aggregate Type Quantity Notation Greywacke 3 G1, G2 and G3

Basalt 2 B1 and B2 Andesite 1 D1



MINERALOGY OF NZ AGGREGATES It is important to have a good understanding of the mineralogy of aggregates as aggregates are non-renewable resources and their usage should be managed in a sustainable way, as prescribed in the Resource Management Act 1991. Most aggregates in NZ have their source mainly from Greywacke and Volcanic rocks. The strength and durability of these rocks are important properties which determine their function use. These two properties are closely related to the matrix properties of the rock, which includes its mineral compositions and the arrangement of these minerals within each rock. A mineral is defined as a naturally occurring homogeneous solid with a definite chemical composition and a highly ordered atomic arrangement which is usually formed by inorganic process (Klein et al., 1993). The mineral compositions of each type of rock and the arrangement of their constituents play a fundamental role in determining the aggregate’s durability and performance in the field. Each mineral has unique crystalline lattice structure planes which are held together by various forces of attraction which include strong covalent bonds, ionic bonds and weak intermolecular forces of attraction. The arrangement of the molecular lattice structure of the mineral determines its toughness and hardness properties. Some key minerals like quartz, which has a regular lattice structure and whereby all the silicon dioxide molecules are strongly bonded to each other by strong covalent bond, exhibits relatively harder and tougher property in comparison with other minerals like albite. It is also important to note that other components that influence the strength of aggregates include the size, shape and angularity of the mineral grains, the binding cementing content in the rock that holds all the grains together, and the porosity of the rock. Mineral grains with greater surface areas and closely related to each other are usually exhibited in harder rocks. A stronger binding cementing mineral like feldspar will hold the constituents of the aggregates with stronger bond and thus increases the hardness of the rocks. On the other hands, rocks with higher porosity encourage easy passage of water molecules through the rocks and can lead to failure due to dissolved minerals or change in the state of water to ice during falling temperatures. Greywacke In NZ, some greywacke is the result of more than 300 million years of intense induration and these rocks can be found in many mountain ranges, rivers and beaches in both the North and South Islands. According to the NZTA Report 295 (Henderson et al., 2006), 75% of sealing chips produced in NZ are Greywacke aggregates. These rocks are classified as a form of sedimentary rock and have distinctive colours like dull, grey, brown, yellow and black. The formation of greywacke occurs through the process of intense induration of greywacke sandstone which reduces the porosity of the rock and recrystallizes its clayey matrix. New minerals like Quartz and Feldspar which were formed during the process are cemented together to materialise the hardening of the rock. Greywacke rocks can be differentiated through the vein lines that run through the rock. These vein lines are the location quartz minerals that contribute to the hardness and toughness of the rock. Studies conducted by Mackechnie (2006) has pointed out that Greywacke rocks in NZ generally have high moisture absorption values and intrinsic shrinkage compared to the Greywacke found in other parts of the world. These characteristics could be attributed to the fact that Greywacke in NZ is sourced from a younger active geological environment (Mackechnie, 2006). Greywacke aggregates in NZ come from six difference terranes which have been identified by geologists. These terranes include Murihiku, Caples, Rakia and Pahau, which are located in the North Island. The rest of the terranes are located in the South Island and are located at Waipapa and Waipa. The following map depicts the location of these six terranes in New Zealand. Most of the greywacke aggregates produced in New Zealand are quarried from these six terranes and are then sold to the different parts of the country for the construction and maintenance of roads.



Figure 1: Map showing the six greywacke basement terranes

A coarse assessment of the different greywacke aggregates samples using microscopic investigation have revealed the following characteristics of the three samples used in this project. Table 2: List of the differences between the three Greywacke aggregates

G1 G2 G3

Grain Size

Coarse grained

Medium sized grains Fine grained Clast-supported and volcaniclastic

sandstone

Prehnite Veins None Run throughout the rock Run throughout the rock

Matrix

Mediocre and consists of angular sand

grains, dominated by volcanic lithic with

some igneous detrital heavy minerals.

Traces of secondary chlorite and pumpellyite are

also noticed in the rock matrix.

More quartz-rich and have a lower lithic

content in comparison with the previous two

samples.

The feldspar grains present in the rock are all albitised and have euhedral shape which

suggests to a near source rock.

Basalt Basalt is the most common type of igneous rock that can be found everywhere and represents 90% of all volcanic rocks. It contains low quartz content and low feldspars by volume and is characterized by its dark grey to black colour. In NZ, it is generally available in regions like the Northland, Auckland, Banks Peninsula, Dunedin and the surrounding islands (Campbell & Chatham Islands).



Microscopic investigation of the two basalt aggregates used in this experiment has revealed the following characteristics of the two aggregates samples. Table 3: List of the differences between the Basalt aggregates

B1 B2 Grain Size Fine grained Coarse grained

Grain Minerals The quantity of augite supersedes that of olivine in the rock matrix.

Presence of large phenocrysts or olivine can also be seen in most of

the chips.

Augites of smaller sized phenocrysts are identified in the samples.

Matrix

Traces of augite and black iron-oxide crystals, commonly known as

magnetite, are scattered throughout the matrix.

Groundmass of the rock is composed mainly of feldspars that comprise of granules of augite, iron oxides (Magnetite), and some brown

volcanic glass. Needle-like shape crystals of feldspars are observed in the rock matrix.

Andesite Andesite is a volcanic rock in intermediate mineral composition between basalt and rhyolite. It can be distinguished by its bluish-grey or grey colour and it has a porphyritic texture. The quantity of hard minerals like quartz in the rock is moderately low and its groundmass is made up of pyroxene, plagioclase, hornblende and phenocrysts. Andesite aggregates are mostly used for the construction of roads and can be found in NZ regions like the Waitakere Range, Coromandel Peninsula, Western Bay of Plenty, Taranaki and the Central North Island. Microscopic investigation of the Andesite aggregates (D1) used in this project has revealed that these aggregates are made up of large crystals, which are feldspars and are mostly colourless and the presence of pyroxene is profound. The matrix of the rock is composed of plagioclase (small needle), granules of pyroxene, iron oxides, and glassy material that seems to have devitrified to cristobalite or tridymite. METHODOLOGY An attempt to describe the skid resistance performance of aggregates, with respects to their different mineralogy, is complex exercise which requires a multi-disciplinary approach from various departments like Transportation, Geology and Chemistry. The difficulty of the project lies within the rock matrix which is the sitting place of myriads of minerals that dominate the strength and durability of the aggregates. The six selected aggregates were subjected to different methodologies in order to screen for potential stronger minerals and cementing materials that influence the hardness and toughness of the aggregates. Microscopic investigation of the thin-sections of the samples aggregates has proven to be a good technique for a preliminary assessment of the likely minerals to be present in each sample aggregate. Advanced technology like the X-ray Diffraction method has been used to characterize the predominating minerals and determine their respective percentage weightage. The Rietveld method (Young, 1993), together with the Siroquant Software Database (a software package used for the quantification of crystalline compounds), are good methods to check and count the identity of the minerals through the different X-ray diffraction peaks.

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X-Ray Diffraction (XRD) The uniqueness of the crystalline lattice structure of every mineral found in rocks has given preference to X-ray diffraction as a suited method to characterize and identify the minerals found in the selected aggregates. The X-ray diffractometer works with the underlying principle that every crystalline substance produces a unique diffraction pattern when subjected to incident X-Ray beams. The unique diffraction pattern for each particular substance can be used for its identification through peaks comparison using a software database. Since the minerals in the aggregates are pure substances and consist of pure crystalline lattice structure, the X-ray diffraction method was used to identify these key minerals and determine their percentage weightage with respect to each other. The X-ray diffraction method involves the use of an X-ray diffractometer which is showed in Figure 2 below. The diffractometer comprises of an X-ray emitter which is situated near the tested sample and two arms counter which rotates around the sample at an angle to collect reflected X-ray beams. The number of X-ray counts recorded by the arm-sensors of the diffractometer is then stored on a computer database as shown in Figure 3.

Sample aggregates were first crushed into fine powder and then loaded into different aluminium holders for X-ray diffraction analysis. Each sample aggregate was then mounted on the XRD Diffractometer whereby it was subjected to incident X-ray beams coming out from the XRD equipment. The powdered samples consist of fine aggregates particles of diameter in the range 0.002 to 0.005 mm and enables polycrystalline diffraction to take place without any hindrance. As the X-ray beams penetrate the particles of the fine aggregates particles, most of them pass through it unaffected as the structure of atoms consists mainly of empty spaces. However, some percentage of X-ray beams bounce back due to collisions with the crystalline reflecting planes of the minerals. The angle of the reflected beam is influenced by the structure and interplanar spacing between the molecules. The uniqueness of the crystalline lattice structure of each mineral would lead to a specific value of the angle of the reflected X-ray beam. This relationship between the angle of the reflected X-ray beam and the interplanar spacing is expressed mathematically as Bragg’s law and this is shown in Equation 1 below:

θλ sin2dn = Equation 1 where: n = an integer, λ = the wavelength of the incident wave, d = the interplanar spacing, θ = the angle between the incident ray and the scattering plane. The bulk mineralogy of the sample aggregate was determined by scanning the powdered sample with X-ray beam over an angular range of 30o to 50o.

Figure 2: The Bragg Bretona Diffractometer

Figure 3: The schematic diagram of the Bragg Bretona Diffractometer



All XRD data was first analysed via the Rietveld methods, using the Bruker AXS Topas package. Refinements via the Rietveld methods were conducted in order to reveal the presence of minerals whose peaks are in phase with those recorded in the database. The image in Figure 4 represents the diffractogram of a sample aggregate. The different colours on the diffractogram represent the fit for the likely minerals to be found in the sample.

Figure 4: An example of Rietveld refinement results

The data obtained from the X-ray diffraction were also analysed using the SIROQUANT Software, for the identification and quantification of the main minerals in each aggregate sample. The software operates by calculating the XRD profile of each mineral and further refinements are conducted before the profile is fit with the recorded patterns stored in the SIROQUANT database.

Figure 5: An example of the SIROQUANToutput

Infield Skid Resistance The prominent minerals present in each type of aggregate and their percentage composition computed by the X-ray Diffractometer was used to compare with the infield skid resistance performance of the aggregates. Data Collection Infield skid resistance data of roads (both locals and state highways), which were constructed using the same aggregates used in this project, was extracted from Road Asset and Maintenance Management (RAMM) database. The analysis was limited on the skid resistance data collected on the left lane of the road with the inclusion of both right and left wheel paths (RWP and LWP). The skid resistance data was collected by using the SCRIM device and each data point is an average of ten meter road section. The infield skid resistance data for G1 was not available in the RAMM database and thus its involvement in this part of the project was excluded. The number of road sections analysed for the different types of aggregates are listed in Table 4.

Table 4: Number of Road Sections

Aggregates Number of Road Sections G2 1 G3 2 B1 1 B2 3



D1 1 Data Analysis The measured skid resistance data was assessed with respect to the age of the surface pavement which had to be evaluated from the data obtained from the RAMM database. In order to accurately calculate the surface age, it was necessary to treat a resealed surface as a new surface, i.e. year zero. The surface age was calculated by using Equation 2 if the road has not been resealed and by using Equation 3 has been resealed.

Surface Age (year) = Reading Date − Surface Date Equation 2 where: Reading Date = the date when the SCRIM reading was taken, Surface Date = the date when the road was constructed for the first time.

Surface Age (year) = Reading Date − Reseal Date Equation 3 where: Reading Date = the date when the SCRIM reading was taken, Reseal Date = the date when the road was resealed. The statistical software R was used to plot boxplots of the infield skid resistance data for each type of aggregate over time. RESULTS AND DISCUSSION Key Assumptions Some key assumptions have been made during the course of this project and considerations were provided to ensure that these assumptions have minimal or no impact on the results of the experiment. Certain methodological parts of have been modified in order to minimize the effects of these assumptions and ensure that the results obtained are non-biased. In total, five key assumptions were made and these are listed as follows. Similar Aggregates sourced at the same quarry are homogeneous

One of the key assumptions of this project is that similar aggregates sourced from the same quarry have been assumed to have the same mineralogy, irrespective of the time when they have been sourced. In practice, slight variation in the mineral composition of the aggregates does exist as these aggregates come from different depths of the ground and thus could have been exposed to different geological conditions.

Accuracy of SCRIM data

The measured skid resistance of the pavement might have some slight variation as it is practically impossible to measure skid resistance on the same exact point of the pavement when the subsequent readings are measure taken by the SCRIM device. Accuracy of the readings depends on how precise the measurements were taken from the same spot onwards.

Seasonal Variation

Seasonal variation is usually applied to the SCRIM data collected to acknowledge the influence of varying climatic conditions on the road surface texture. For simple data handling, only SCRIM data were used for the infield skid resistance assessment of the road sections.

Traffic Loading



The effects of traffic loading have some influence on the pavement skid resistance performance. Pavement subjected to higher traffic loadings, especially with a higher percentage of heavy commercial vehicles will usually be subjected to greater pavement deterioration and hence the decrease in skid resistance performance over time will be higher. However, since most of the road sections selected for this project are located on similar kind of rural areas, the effects of traffic loading were assumed to be similar on all the sections.

Road Curvature

Road sections which include steep or sharp curvatures will also experience sharp decrease in skip resistance due to greater lateral forces acting at the left sides of the wheel of the vehicles. The NZTA RAMM database was screened properly so that only road sections with straight alignments were considered for this project.

X-ray Diffraction Results X-ray Diffraction results of the three greywacke samples indicate that they have similar minerals characteristics, with Quartz, Albite and Chlorite dominating the rock matrix as listed in Table 5. The ratio of these minerals is similar for both G1 and G2. However, a significant higher percentage of Quartz was observed in the G3 although its feldspars quantity was found to be similar with the other two greywacke aggregates.

Table 5: X-ray Diffraction results for Greywacke Samples G1, G2 & G3

Percentage Weightage for each aggregates Minerals G1 G2 G3 Quartz Weight % 28 28.4 37.2 Albite Weight % 61.5 59.4 52.8 Chlorite Weight % 10.5 12.2 10

Some variations were observed in the minerals content for the two basalt sample aggregates. The prominence of minerals like Albite, Labradorite, Nepheline and Pyroxene were detected in B1. Other typical basaltic minerals (mostly feldspars) like Diopside, Forsterite, Nepheline and Albite were also found in the B2. The ratios for the percentage weighing of these minerals can be found in Table 6. The D1 was found to consist mainly of Albite and Tridymite as the major minerals present in the aggregate. The Albite weightage was much more pronounced (89.1%) when compare with Tridymite which showed a weighing percentage of only 10.9% in the sample aggregates.

Table 6: X-ray Diffraction results for Basalt Samples B1, B2 and Andesite D1

Percentage Weightage for each aggregates

Minerals B1 B2 D1

Albite Weight % 29.9 16.6 89.1 Labradorite Weight % 23.5 Nepheline Weight % 19.3 17.7 Pyroxene Weight % 27.2 Diopside Weight % 53.8 Forsterite Weight % 11.9 Tridymite Weight % 10.9



Infield Skid Resistance Performance of the Aggregates The infield skid resistance performance of aggregates is expected to decrease over time, although depending on the aggregate type, the deterioration rate can be different for different aggregates.

Figure 6: Skid resistance data for G2 aggregates (LWP)

Figure 7: Skid resistance data for G2 aggregates (RWP)

Figure 6 and 7 show that the infield skid resistance of G2 aggregates fluctuate over time. The average of skid resistance values range between 0.45 and 0.55 for both LWP and RWP. The G2 aggregate has a high composition of hard minerals like quartz, and hence, it shows more resistance to polishing compare to other aggregates. The fluctuation that happened throughout the year may due to other factors, e.g. seasonal variation.

Figure 8: Skid resistance data for G3 aggregates (LWP)

Figure 9: Skid resistance data for G3 aggregates (RWP)

The mineral composition of G3 shows that it consists of a higher composition of quartz in comparison with G2 aggregate. Thus, its skid resistance performance was expected to be higher than the G2. However, this was not reflected in the infield skid resistance data as there is a clear decrease in skid resistance values. The average skid resistance values started off at about 0.5 and decreased to about 0.4 after seven years of polishing. This could be the result of a poorer



cementing material inside the rock matrix of this greywacke. There is an increase in skid resistance value at year 8 and this may be due to unrecorded resealing work.

Figure 10: Skid resistance data for B1 aggregates (LWP)

Figure 11: Skid resistance data for B1 aggregates (RWP)

Figure 10 and 11 show the infield skid resistance performance for B1 aggregates, which is observed to be stable over time and the average skid resistance is approximately 0.5. This could be attributed to its high composition of albite and other strong feldspars minerals that strengthen the aggregate and boost up its abrasive resistance.

Figure 12: Skid resistance data for B2 aggregates (LWP)

Figure 13: Skid resistance data for B2 aggregates (RWP)

The infield skid resistance of the basalt sample depicts a clear decreasing trend over time and has a value within the range of 0.4 to 0.6. The mineralogy of this aggregate indicates that it consists of less stronger minerals like albite and its rock matrix is made up of different cementing materials when compare with the first basalt sample. A weaker cementing materials and lower albite content might be responsible for the decrease in the infield skid resistance performance over time.

Figure 14: Skid resistance data for D1 aggregates (LWP)

Figure 15: Skid resistance data for D1 aggregates (RWP)



The D1 aggregates show a reasonable stable skid resistance performance over time and the slight fluctuation on the boxplot might be due to seasonal variation. The high quantity of albite indicates that it possess enough strong minerals and thus have higher abrasive resistance. CONCLUSION This paper has described the relationship between the skid resistance performance of aggregates and the differences in their respective geological properties. However, it should be noted that this is rather a complex exercise which involves a multi-disciplinary approach from different departments such as Transportation, Geology and Chemistry. Various methodologies like microscopic investigation of the aggregates’ thin section, X-ray diffraction method and analysis of infield skid resistance data have been applied to reach the objective of this project. During the course of this experiment, some important results were produced and these have been listed as follows: Microscopic investigation of the thin sections of the sample aggregates has provided clear

magnified images of the minerals grains present in the crushed sample and these images can be used to conduct a preliminary assessment of the likely minerals to be present in these aggregates.

The X-ray diffractometer has been successful in characterizing the predominating minerals inherent in the sample aggregates and computing the ratio of their respective percentage weightage.

Infield skid resistance data plots have revealed that some types of greywacke tend to perform better compare to other greywacke types. Statistical data analysis reveals that even though there tends to be a decrease in skid resistance performance over time, the trend is somehow slower in some greywacke types which constitute a higher content of some hard minerals like quartz.

Basalt aggregates also tend to show a slight variation against each other for their infield skid resistance performance even though a decreasing trend is observed for both aggregates. The slight variation may be attributed to the difference in the minerals composition of their rock matrix. Some basalt aggregate types which are rich in albite content, will generally exhibit greater skid resistance performance due to its hard cementing materials.

All these results confirm that the superiority in strength of the aggregates’ minerals and cementing materials plays a fundamental part in determining aggregates’ hardness and toughness properties. Fundamental knowledge of the geological properties of rocks can be used to screen potential aggregates for their high skid resistance performance and thus be applied on the surfacing layers of roads with high traffic loading. Pavement surface, constructed with high abrasive resistance aggregates, offer higher skid resistance performance over time and this can help in reducing the number of crashes on the roads. Screening aggregates for their functional use is an effective strategy to maximize pavement condition over time and thus help in making considerable savings in the life cycle of road assets. This also complements the Resource Management Act 1991 which prescribed that the use of non-renewable resources and their usage should be managed in a sustainable way. FUTURE RESEARCH The X-ray diffraction method and its associated minerals stored database have been successful in providing the ratio of the main minerals in the sample crushed aggregates through common peaks identification process. However, this method excludes the ability to quantify the percentage of dominant minerals with respect to the volume of the sample aggregate. The quantification of important hard minerals in the sample aggregates can be useful information in describing the skid resistance performance of the aggregates with respects to its mineralogy. Research work is currently in progress to determine the possibility of finding an alternative method to quantify the minerals in the sample aggregates. Microscopic investigation of the thin-sections of the aggregates does provide clear images of inherent grain-size minerals in the crushed sample aggregates. These images can be further processed using available image processing technology like the



Matlab Image Processing Toolbox to determine the percentage of these minerals in the sample aggregates. It is also important to note that the objective of this project has been focused mainly on the impact of mineralogy on the skid resistance performance of aggregates. Other components such as the size, shape and angularity of the aggregates, the groundmass of the rock, and the porosity of the rock should not be excluded as they also influence the skid resistance performance of aggregates. Further research is required to understand how these factors operate and impact the skid resistance performance of aggregates. REFERENCES ASTM D3319 – 11: Standard Practice for the Accelerated Polishing of Aggregates Using the British

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FU, C. N. 2000. Predicting Surface Friction - What Do We Want and What Do We Know? International Center for Aggregates Research (ICAR) 8th Annual Symposium. Aggregates Foundation for Technology, Research and Education.

HENDERSON, R., COOK, G., CENEK, P., PATRICK, J. & POTTER, S. 2006. The effect of crushing on the skid resistance of chipseal roads. Wellington: Land Transport New Zealand.

KLEIN, C., HURLBUT, C. S. & DANA, J. D. 1993. Manual of mineralogy, Wiley. MACKECHNIE 2006. Shrinkage of concrete containing greywacke sandstone aggregate.: ACI

Materials Journal. WILSON, D. J. 2006. An Analysis of the Seasonal and Short-Term Variation of Road Pavement

Skid Resistance. Doctor of Philosophy in Engineering Unpublished Thesis, The University of Auckland.

WILSON, D. J. & BLACK, P. M. 2008. Comparison of Skid Resistance Performance between Greywacke and Melter Slag Aggregates in New Zealand. International Safer Roads Conference. Cheltenham.

WON, M. & FU, C. 1996. Evaluation of Laboratory Procedures for Aggregate Polish Test. Transportation Research Record: Journal of the Transportation Research Board, 1547, 23-28.

WOODWARD, D., WOODSIDE, A. & JELLIE, J. 2004. Improved Prediction of Aggregate Skid Resistance Using Modified PSV Tests. 8th Internation Conference Applications of Advanced Technologies in Transportation Engineering. Beijing: American Society of Civil Engineers.

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