THE WEIGHTED PLASTICITY INDEX IN ROAD DESIGN AND CONSTRUCTION

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1 THE WEIGHTED PLASTICITY INDEX IN ROAD DESIGN AND CONSTRUCTION B. G. Look Foundation Specialists Pty Ltd, Brisbane ABSTRACT The commonly used Plasticity Index (PI) test disposes the material retained on the 425 micron sieve, thus is not representative of the whole sample. In Australia, residual soils are very common, with a high granular content in clayey soils. Thus a significant portion of the sample is discarded for the PI (only) test. The weighted plasticity index (WPI) accounts for the portion used in the PI test and this is important for classification of residual soils. The background and historical development of the WPI is presented with its relationships, applications and limitations. Changes in testing Standards affects the correlations expected with the original WPI classification. 1 INTRODUCTION In situ rock weathering results in residual soil materials, which dominate the Queensland soil profile for road construction. Due to their in situ formation, residual soils generally are heterogeneous and progressively grade to the characteristics of their parent rock. A blur occurs between XW (extremely weathered) rock and residual soils with no clear layering, although an interpretative line is drawn on the borelogs. In comparison, alluvial soils have been sorted during transportation and likely to be more uniform in composition with distinct layers. Reactive clays may be derived from these residuals soils. The economics of using available local material provides a significant cost saving during construction where cut and fill is required. In south east Queensland, there is 84% residual and 16% transported soils on an area basis (Lacey, 2016). The shrink swell index (I ss ) is the key parameter for assessing movement of structures on reactive clays in Australia. Fityus et al. (2005) discuss its historical developments and application in Australia. The soil suction zones and the Thornthwaite Moisture Index (TMI) are used to derive many of the parameters (AS2870, 2011). However, the Atterberg Limits are more used internationally and for road works in Australia for assessment of the quality of fill. It is a descriptive parameter and lacks many site specific elements as compared to the I ss which is quantitative, and when applied considers climatic effects, depth of seasonal variation, the seasonal changes and the cracked zone within that active zone. The I ss is a primary design parameter, while the PI is used as both a secondary design (index) parameter and in construction quality control. A pavement is also a light structure and many of the concepts of AS2870 apply similarly. When a material is classified as a fine grained soil (AS 1726, 1993), the Plasticity Index (PI) has then been traditionally used as an indicator of the soil s volume change potential when exposed to or deprived from water. However, the PI value often provides false positives in the residual clays. This was found to be due to the removal of the fraction coarser than the 425 micron sieve as part of the test procedure. That portion can be significant in residual clays. Additionally road specifications include the requirement for a plasticity test even when the soil is classed as a coarse grained soil. This leads to a misunderstanding that plasticity tests are always required. A high plasticity index can therefore be misleading as an indicator of site movement potential, if some or most of the in situ material has been discarded to carry out the PI test. The weighted plasticity index (WPI) is the product of the plasticity index and the percentage passing the 425 micron sieve, and therefore accounts for both the PI and % used in the test. The removal of that fraction is part of the standard test and the WPI intent was to report simply the percentage used, although industry seems now to use a full gradation test in tandem with the PI test. The background, relationships, properties and application to the WPI classification boundaries (Table 1) are provided in this paper. It is instructive to also reflect on this classification boundary relevance, by examining additional data since that time. The discussion is for roadworks and may not be applicable to other types of construction. 2 HISTORICAL APPLICATION OF WPI IN QUEENSLAND In the late 80s and early 90s, Queensland Main Roads was experiencing a significant pavement maintenance budget associated with expansive clays. Look (1995) showed the total maintenance cost of roads on reactive clays is two and a half the cost for roads on other soil types in Queensland. Various research projects were carried out with some of the outcomes leading to greater emphasis in equilibrium moisture content (EMC), using an upper characteristic value for 21

2 material with a high potential for volume change, zonation strategies, and the WPI classification as a screening tool in the Earthworks specifications (QT, 1995; MRTS04, 1999). Despite the issues with the performance and costs associated with roadways built with expansive clays, there seemed to be a regular misclassification of the material using the standard Casagrande plasticity chart for residual soils. These residual soils covers over 80% of Queensland, therefore a more effective screening tool was required. This was derived by considering 1. Various approaches in the international literature 2. The probability of occurrence for the classification 3. Relationship with the soaked CBR test which is a key input parameter for pavement design. 2.1 VARIOUS APPROACHES While various authors have used various classifications based on Linear Shrinkage, Liquid Limit and Plasticity Index (PI), the latter seemed to provide the most consistent classification system. Holtz and Gibbs (1956) related expansion potential to index properties. Their samples were tested at moisture contents for a dry to saturated condition under a surcharge of 6.9 kpa (1 PSI). This resulted in a probable expansion of over 30% for the very high classification. Chen (1988) established relationships from natural moisture content to saturation with varying surcharges and compared the results with the approach of Holtz and Gibbs (1956) and classification schemes by Seed et al. (1962) who also tested remoulded samples. Chen (1988) classified the very high degree of expansion with 10% total volume change, which highlights the considerations of initial state of soils and overburden (surcharge) in evaluating expansion potential. Van der Merwe (1964) used an expansive clay chart to relate the activity, PI and the clay fraction to the swelling potential. Williams and Donaldson (1980) discuss modifications to that chart and suggested using the PI of the whole sample. Low and very expansive were classified as PI < 12, and PI > 32, respectively. Their values were similar to those of Holtz and Gibbs (1956) but accounted for the percentage used in the test as well as the PI of the sample. Their terminology of PI of the whole sample was meant to be distinct from the sample PI. However this had the potential to be easily confused in its application. Given that the sample PI was found to be providing false indications of the potential for volume change in residual soils, the approach of accounting for the fraction used seemed appropriate to avoid mis-classifications occurring at that time. The first part of the classification process should involve determining whether coarse grained or fine grained and then carrying out a plasticity description on the latter. However, at a site in Mt Walker, Queensland that was classified as highly expansive with a PI of 48% (Look, 1995), the percentage passing the 425 micron sieve was less than 23%. Firstly, the soil should have been classified as a coarse grained soil without the need for a plasticity test. Yet that is a common road practice, where specifications for a granular material also includes a plasticity criteria. Secondly, the plasticity test was on a minor portion of the soil, which was commonly occurring even for soils classified as fine grained. These combined considerations would change that site classification from a high to a low potential for volume change by including the percentage used in the test. The term weighted plasticity index (WPI) was therefore adapted to clearly differentiate from the PI and is the product of the plasticity index and the number percentage passing the 425 micron sieve (and not the fractional value used by Williams and Donaldson, 1980). The classification in Table 1 was adopted (Queensland Transport, 1995) with a technical note advice on procedures for constructing with expansive clay materials. Table 1 Weighted Plasticity Index Classification of volume change. CLASS WEIGHTED PLASTICITY INDEX (WPI) POTENTIAL FOR VOLUME CHANGE A < 1200 LOW+ B 1200 WPI < 2200 MEDIUM C 2200 WPI < 3200 HIGH D 3200 VERY HIGH + When applied to pavement a Very low value of < 200 is typically used 22

3 2.2 CLASSIFICATION At the time of the initial classification, the relative proportion of the material was unknown. Over time data was collected and Figure 1 shows the distribution of WPI expected in the various categories from 146 test samples for various Queensland sites with residual clays. Figure 1: Distribution of WPI. Table 2 shows an updated distribution (2015) with additional data from Queensland sites (double the initial data) with residual soils. Approximately half of all residual soil values are Class A material, with an approximately similar amount (14% to 18%) of Class B, C and D materials (Table 2), for the data. Using values based on PI only on residual material has the greater potential to classify as an expansive material. Table 2 Updated comparison of potential for volume change in residual material using WPI and PI criteria. PLASTICITY (PI) CRITERIA WEIGHTED PLASTICITY INDEX (WPI) POTENTIAL FOR VOLUME TYPICAL PI LIKELIHOOD WPI LIKELIHOOD CHANGE RANGE < 12% 20.2% < % LOW 12% PI < 22% 31.8% 1200 WPI < % MEDIUM 22% PI < 32% 21.6% 2200 WPI < % HIGH PI 32% 26.4% % VERY HIGH The PI criteria would have an approximately even distribution for the medium to very high categories. Figures 2 and 3 compare the distribution of data using the PI and the WPI, respectively. A lognormal and exponent Probability Distribution Function (PDF) applies to the PI and WPI data, respectively. 23

4 Figure 2: Plasticity Index Distribution. Figure 3: WPI Distribution. Figure 4 shows the Cumulative Distribution Function (CDF) for the % passing the 425 micron sieve used to carry out the PI tests. The number of test values do not exactly match as some samples had both PI and % used, while in some cases, one or the other was missing from the site test data. In the PI tests, typically only 60.4% of the samples was used in a residual soil profile. This accounts for the difference and over classification when using the PI only as an index of volume change. In one case only 4% of the sample was used in the PI test. This clearly illustrates why the use of the PI test may not be representative in residual soil profiles, due to the high percentage discarded in carrying out the test. For this residual soil data set, the percentage fines is typically passing 0.75 times the percentage passing the 425 micron sieve i.e. percentage of whole sample used in the test (Figure 5). The percentage passing the 19 mm sieve is also shown. This value represents the oversize associated with the compaction tests. Above 20% oversize (80% passing the 425 micron) represents increasing errors associated with using that test result in compaction or CBR tests. Figure 5 shows that over 50% passing the 425 micron sieve is required to remove having oversize corrections from consideration - a secondary but associated concern. 24

5 Figure 5 also suggests that if less than 67% of the sample is used in the PI test then the material is likely coarse grained according to AS1726 (1993) which specifies 50% fines as the classification change point. A classification which uses 35% fines as fine grained would have the corresponding PI applicability to 50% passing the 425 micron. Figure 4: Distribution of Fraction used in PI tests. Figure 5: Ratio of percentage fines to percentage used in PI test for typical residual soil. Thus the misclassifications associated with using only the PI in residual soils was overcome by using the WPI screening criteria. This simple approach can then be used to classify materials both during investigations and in quality control during construction. The classification boundaries also provided a reasonable allocation ratio of such materials. 25

6 3 RELATIONSHIP OF WPI WITH CBR TEST The WPI was also related to the CBR swell value using the 4 day soaked value. This was for the standard compaction and surcharge with samples compacted close to the Maximum Dry Density (MDD) and Optimum Moisture Content (OMC). There is testing variability in achieving the exact density and moisture ratio. In practice, the as compacted density does vary from that target compaction value, even in the laboratory. A value above 98% MDD would generally apply. The CBR swell value is not typically given a high consideration by many engineers, yet swell is very important in determining whether strength (as indicated by the CBR index value) may be a secondary consideration based on the swell value. Movement rather than strength would then be the governing design criteria. Porter (1950) found subgrades that provide a satisfactory performance usually show a swell of less than 3% during the 4 day soaked test. Good base and subbases shows less than 1% swell. The Asphalt Institute (1982) considered a swell significant when it exceeded 2% under a surcharge consistent with the pavement design. The WPI = 1200 corresponded to the 2% swell value (Figure 6) for the first few sites examined with a strong correlation (R 2 = 0.74). This added credence to the selected classification system. Figure 6: Comparison of CBR swell with WPI. Although this was adapted as a universal classification system, Figure 6 shows that the relationships varied for various sites. The initial major project sites where data was examined, were biased (unintentionally) to highly expansive materials. After implementation of the classification system and as data for more sites became available many years later, then this bias became evident in hindsight. This additional data also revealed that the correlation was not as strong as first believed (by this author). An additional observation for sites 5 and 6 was that a high WPI material could still have a low CBR swell (Figure 7). This would be an error in the classification system, but would also occur if the PI only was used. Therefore, while the WPI was developed as an improved screening tool from using the PI only, the additional data suggests that some overestimation in potential volume change may still occur. 26

7 Figure 7: Correlation comparisons for CBR swell vs WPI. Residual soils properties are affected by their primary origin. Kaolinite and Halloysite clay mineral profile occurs for high annual rainfall and temperatures as occurs in Queensland. The Kaolinite is associated with low activity as compared to the other clay minerals, but the Halloysite (a mineral of the Kaolin group) contains an inter-layer of water and as a result has high moisture contents and liquid limits and is sensitive to remoulding during construction (Reeves et al., 2006). Fast forward to 2015, over 20 years after the WPI research was implemented, and with more data than from the 6 major projects, then Figure 8 shows the correlation is now reduced from R 2 = 0.74 to R 2 = The WPI / CBR swell correlation line now represents the top 80% of sites and not an average site condition with approximately 50% above and below that line as first thought (Figure 8). Figure 8: Comparison of CBR swell vs As a footnote to this data set, Figure 5 shows that 22% of tested samples exceeded 20% retained on the 19 mm sieve. However the initial data would have been based on Queensland Main Roads Q110A (1978) and Q113A (1992) for compaction and California Bearing Ratio, respectively. At that time, such oversize corrections were not required in the test. Those tests have been replaced by Q142A (2012) for compaction and Q140A (2010) now accounts for up to 35% oversize in determining the relative compaction. 27

8 The oversize was not reported in the early data and would not have been known if a parallel grading had not been carried out. According to Australian Standards (AS 1289, 2014) the test is not meant to be applicable for greater than 20% discarded when using the standard mould. Additionally, a correction factor should be applied to the OMC and the MDD for any oversize. Additionally test sample preparation has also been changing due to the poor reproducibility and repeatability of results. Rallings (2014) highlights these and other issues with the CBR test. Despite these changes in test procedures, some of the recent tests did not have the corrections applied. This shows that neither corrections nor non-validity of the tests are being consistently used in industry practice even with the updated 2014 test standards. Some distortion in the earlier relationships is now occurring due to changing testing Standards. 4 DESIGN AND CONSTRUCTION IMPLICATIONS Figure 9 shows the relationship of the soaked CBR with WPI. Typically, a WPI of 1200 would have a CBR of 8%, while a WPI above 3200 would have a CBR 3%. Thus any CBR below 8% should consider movement potential and for CBR < 3%, the movement potential likely governs rather than strength considerations. This relationship is for 4 day soaked values. Austroads (2010) suggests in service pavement in fair to poor drainage requires a 7 and 10 day soaked test condition, for medium and high rainfall, respectively. Linking that to the type of material should also occur. For Type A/B materials, 4 days would likely produce the lowest CBR/ highest swell condition. However for Types C and D consideration should be given to 7 and 10 day soaked CBRs, respectively. Figure 9: Soaked CBR relationship with WPI. Class A material (WPI < 1200) would typically have less than 15% fines and/or less than 12% plasticity index. Such materials would have less sensitivity to moisture changes CBR reduction (50% to 100% of unsoaked value). The changes would occur within 1 day of soaking. Class C/D materials with higher WPI would have a greater reduction ratio of soaked to unsoaked strength. (Figure 10), but with increased time required for soaking to show that change. Type A material has the advantage of high strength and low swell as compacted to the other types. However if a low permeable material is required another specification with at least 15% fines would apply. The soaked CBR swell can be used as an indicator of swell in design to determine specification moisture content to minimize movement. However a 5-point Soaked CBR is required rather than the industry (low cost) practice of a one point CBR at the MDD/ OMC only. A low CBR is often, but not necessarily, an indicator of volume change behaviour. A good granular pavement material has a swell < 0.5% and WPI <

9 Figure 10: Impact of PI on strength loss in flexible (Sharp et al., 2001). Table 3 shows the governing design which would then apply. The CBR original estimated swell value is also shown. Note Austroads (2007) uses a different set of values their data source is unknown. CLASS POTENTIAL FOR VOLUME CHANGE Table 3: WPI Classification and CBR swell relationship with design considerations CBR SWELL (%) (FIGURE 6) DESIGN CONSIDERATIONS APPLICATION CBR SWELL (%) (AUSTROADS, 2007) VERY LOW < 0.5% SUITABLE PAVING MATERIAL DATA STRENGTH A LOW < 2% GOVERNS MAY BE USED FOR CAPPING < 0.5% MATERIAL B MEDIUM 2 3.5% C HIGH 3.5% - 5% D VERY HIGH 5% STRENGTH / MOVEMENT MOVEMENT GOVERNS STRENGTH GOVERNS BUT SOME MOVEMENT CONTROLS MAY APPLY FOR MAJOR ROADS UNSUITABLE DIRECTLY BELOW PAVEMENT. CONSIDER GEOTEXTILES % % REPLACE AND /OR STABILISE 5.0% Look (1995, 2005) provides details on the overall process requirements for both equilibrium density and moisture content. For High WPI (Class D), the EMC is wet and dry of OMC for high and low rainfall, respectively (Figure 11). The relatively low correlation for Class A (WPI< 1200) suggests low dependency on climate. The moisture ratio is generally dry of OMC. Look and Reeves (1992) and Look et al. (1994) provide the background on the design and specification development, respectively. Burman et al. (2008) present case studies which highlight that post construction re-testing of clay fill differed from the as constructed placed moisture and density. They did not discuss the causal factors. However this author believe that study highlights the issues associated with not constructing at the equilibrium conditions. Having established the classification of the material in terms of its WPI, then the resulting design and construction process is shown in Figure 12. This adopts some of the concepts associated with the shrink swell index approach (earlier versions of AS2870) by considering depth of seasonal variation (a zonal strategy) and TMI climatic effects (rainfall affecting equilibrium conditions). 29

10 Figure 11: Equilibrium Moisture Content for varying rainfall and WPI. Figure 12: Design and construction process based on WPI classification. 30

11 Figure 12 highlights the EMC should govern the target placement moisture content for high WPI materials and targeting the OMC is not desirable in wet or dry environments, but appropriate in climates with rainfalls of 500mm to 1,000mm. Importantly the OMC should not be a pass / fail condition, yet that is still common in many earthworks specifications. Forcing placement to the OMC is often counter-productive. This moisture content may (or may not) represent a reduced effort for placement, but EMC of the borrow material for that climatic material, is a more important criteria. The target density (a pass / fail condition) can also be achieved at moisture contents significantly away from the OMC and depends on the equipment (weight and type) available. Another key point is that a high density (at or over 100% MDD) in class C and D materials can be a failed compaction, for a wet climatic environment (rainfall > 1,000mm). Over compaction of these materials induces swell in the long term, although high compaction may temporarily (during construction) show a high strength (as inferred by density). Thus an upper characteristic value is used to avoid such over compaction and resulting increasing swell (Look et al., 1994). Many designs simply use a 4 day soaked CBR test and assume it is a conservative approach. Mulholland et al. (1986) show that 25% of test sites have an in situ CBR less than the soaked CBR and provide such correction factors to estimate the equilibrium in situ CBR. More recently the introduction of 7 and 10 days soak follow that philosophy, that the CBR may be less than the 4 day soaked value. This would likely apply to Class C and D material, respectively. However, a more rational approach is estimating the likely EMC and density, then compacting the sample to that equilibrium condition before carrying out a CBR test. More importantly, Table 3 shows that strength (as indicated by the CBR value) is often not the governing design consideration for Class C and D material. Figure 12 shows a zonation requirement which effectively reduces the active zone (for Class C/D) to limit movement of the overlying pavement. As desiccation cracks form on such slopes and may lead to shallow translational failures in such materials, then the outer zone criteria also extends to both over and to the sides of the Class C/ D materials. The upper and side zones are therefore for different purposes. Some of these aspects are highlighted further in the case study in the next section of this paper CASE STUDY For a 13 km major earthworks project in south east Queensland and in mainly residual soils, 1,385 WPI tests showed about 86% of the site derived material (Figure 13) was either Class A (WPI< 1200) or Class B ( ). The best fit distribution function (from a goodness of fit test) and the normal distribution is also shown. The use of appropriate distribution functions is discussed in Look (2015). Figure 13: Site derived material WPI. 31

12 If the PI of 12% had been used to classify the material (Figure 14), only 4.9% of the in situ material would have been Class A as compared with 34.5% using the WPI criteria. This shows the classification errors that would occur when material sampled is discarded as part of the procedure in carrying out the Atterberg tests. Figure 14: Site derived material PI. Figure 15 shows the wide range of passing density with moisture ratios from 5,649 compaction test results varying considerably, but predominantly between 75% and 103% OMC (Figure 16) i.e. dry of OMC for this mainly Class A/B material. This highlights OMC is not a criteria for achieving the correct density. Compaction theory suggests that the density target may not be achieved efficiently. But that does not consider the equilibrium moisture condition or the high energy equipment being used. Figure 15: Site derived material Density moisture ratio relationships. 32

13 Figure 16: Distribution of Moisture Ratios (mainly Class A and B). The WPI < 200 is also used in evaluating high quality materials. A moisture ratio of 43% to 90% of the OMC (Figure 17) applied for 10% to 90% of the 279 samples, respectively and for this same site. Thus in practice compaction is occurring at less than 100% of OMC for 95% of the samples tested. The passing compacted density ratio is independent of the moisture ratio. The concept of compacting to the Optimum Moisture Content is therefore not governing as the density can be achieved at different moisture contents in practice. That OMC is for a lab compaction energy which is not the field equipment energy. The heavy equipment used in the field shifts the optimum moisture value less than the Standard OMC. The material arriving at the site is also in a dry of OMC state. To wet up this material to OMC is therefore a sub optimal process, contrary to the Proctor theory, which suggests wetting up and compacting at Lab OMC. Irrespective of compacting at the OMC, the material would dry back to be less than OMC as its EMC condition. For this CBR 45 material with an actual placed moisture ratio of 43% to 90%, a specification target of 50% to 80% OMC for this paving material seems appropriate and would be in the range of 2/3 of these samples tested. The moisture ratio which is therefore not a pass/fail criteria. The moisture ratio is a useful guide to efficient compaction, but it is the density ratio is the pass/fail criteria. Figure 17: CBR45 Scatter plot of Density ratio and moisture ratio (WPI < 1200). 33

14 6 CONCLUSIONS Residual clays have a significant portion discarded in the Atterberg tests. High plasticity clays (CH) when classified in accordance with AS1726 for percentage fines and then using the standard Casagrande approach for soil plasticity, does not consider the percentage used in the test. Data presented shows material with a low potential for volume change can have a CH classification. The Weighted Plasticity Index overcomes that misclassification due to the percentage used. The considerations that affected the use of the WPI and its boundary classifications were presented. As test procedures are also evolving, then the WPI classification boundaries may not necessarily represent the original intent in terms of its relationship to CBR swell. The WPI is a screening tool which leads to design and construction procedures currently adopted in various Road Authority Specifications. The energy used in the laboratory derived optimum moisture content may not represent the field equipment energy. Materials that are in a very wet or very dry climatic environment and with a high potential for volume change, require the long term equilibrium condition to be part of the design and construction process. 7 ACKNOWLEDGMENTS Some of the data was obtained as part of a research project while the author was at the Queensland Main Roads Department in the 1990s. The views expressed do not necessarily reflect those of the Department. 8 REFERENCES Australian Standard (1993), Geotechnical Site Investigations, AS Australian Standard (2011), Residential Slabs and Footings, AS Australian Standard (2014), Methods of testing soils for engineering purposes. Method 6.1.1: Determination of the California Bearing Ratio of a Soil, AS Austroads (2009), Guide to the Pavement Technology Part 4I: Earthworks Materials, Publication No. AGPT04I/09. Burman, B C Mostyn, G and Piccolo D (2008), Experiences with post-construction retesting of engineered clay fills, Australian Geomechanics Vol 43, No 4, pp Chen F.H. (1988), Foundations on Expansive Soils, Elsevier Science Publishers. Fityus S G, Cameron D A, and Walsh P F (2005), The shrink swell test, Geotechnical Testing Journal, Vol 28, No 1. pp Holtz W.G. and Gibbs H.J., (1956), Engineering properties of expansive clays, ASCE Transactions, Vol. 121, pp Lacey D W (2016), Assessment of some engineering properties and testing methods of residual soil and highly weathered rock materials in Queensland, Australia, PhD thesis, The University of Queensland. Look, B G and Reeves, I N (1992), Development of design details for expansive clay embankments based on time domain reflectometry instrumentation, 7th Road Engineering Association of Asia and Australia, Singapore, June, Vol 2, pp Look, B G, Reeves I N and Williams D J, (1994), Development of a specification for expansive clay road embankments, 17th Australian Road Research Board Conference, August, Part 2, pp Look, B.G. (1995), The effects of volumetrically active clay embankments on roadway performance, PhD thesis, The University of Queensland. Look B G ( 2005), Equilibrium Moisture Content of volumetrically active clay earthworks in Queensland, Australian Geomechanics Journal, Vol 40, No. 3, pp Look B (2015). Appropriate distribution functions in determining characteristic values, 12th Australia New Zealand Conference in Geomechanics, Wellington, New Zealand, P014 Mulholland P.J., Schofield, G.S., and Armstrong P. (1986), Structural design criteria for residential street pavements: interim report based on Stage 1 of ARRB project 392, Australian Road Research Board. Porter O.J. (1950), Development of the original method for highway design, Symposium on the development of flexible pavement design methods for airfields, ASCE Transactions Vol 115, Paper No 2406, pp Queensland Department of Main Roads (1999), General Earthworks, MRS Standard Specifications. Queensland Department of Main Roads (1992/2014), Materials Testing Manual Queensland Transport (1995), Expansive Clay embankments Technical Note 10. Rallings R. (2014), The CBR test a case for change? Australian Geomechanics, Vol 49, No. 1, pp Reeves G M, Sims I and Cripps J C - Editors (2006), Clay Materials used in Construction, Geological Society Engineering Society Special Publication No. 21. Seed H.B., Woodward R.J, and Lundgren R. (1962), Prediction of swelling potential for compacted clays Journal of the Soil Mechanics and Foundations Division, ASCE, Vol. 88, No. SM3, pp Sharp K G, Vuong B T, Rollings R S, Baran E, Foley G D, Johnson-Clarke J R and Metcalf J B (2001), An Evaluation of the Field and Laboratory Properties of Lateritic Gravels, ARRB Transport Research Report ARR

15 The Asphalt Institute (1982), Research and Development of the Asphalt Institute s thickness design manual (MS-1), Ninth Edition, Research Report No Van der Merwe D.H. (1964), The prediction of heave from the plasticity index and percentage clay fraction of soils, The Civil Engineer in South Africa, Vol. 6, No. 6, pp Williams A.B. and Donaldson G.W. (1980), Building on expansive soils in South Africa: Proceedings of the 4th international Conference on expansive soils Vol II, Colorado, p