prediction of soaked cbr for coarse grained soil mixtures condensed to 10 pages

A METHOD FOR THE PREDICTION OF SOAKED CBR OF REMOULDED SAMPLES FROM STANDARD CLASSIFICATION TESTS.

Peter G McGough

Director - PGM Geotechnical Pty Ltd, Perth, Australia [email protected]

ABSTRACT

Many authors in the past have investigated the ability of standard classification tests to provide an estimate of soaked CBR for remoulded samples across a wide range of materials with most classifications limited to fine grained soils with soaked CBR’s < 20.

A method of estimating the soaked CBR of a remoulded soil from a PSD test and an Atterberg Limits test has been developed and tested against field data. It is valid for coarse grained or fine grained soils, or mixtures of both, and is not limited by the CBR value. The method also accounts for modified compaction of the material after soaking as the relative compaction prior to soaking did not influence the correlation. The method is based on more than 400 soaked CBR tests in wide variety of soils from around Western Australia, South Australian and Northern Territory. The method has been compared against the results of soaked CBR test from several locations around the world and found to be comparable.

The method is based on the Fine Material Factor (FMF) of the soil which is the product of the raw Plasticity Index and the proportion of the soil passing the 0.425m sieve. This method confirms the long held anecdotal evidence that materials with a FMF < 450 are typically suitable for sub-base use and in some cases suitable as base course. A series of correlations have been developed linking FMF and Soaked CBR for a range of MMDD Ratio’s.

1. BACKGROUND

Whilst undertaking a materials search investigation for coarse grained engineered fill for construction of the infrastructure for new mine development in an arid environment, the author was faced with the prospect that the preliminary specifications (as detailed in Table 1) for evaluating the suitability of materials for road, rail, and embankment construction may potentially discount many suitable materials due to their stringent plasticity constraints.

Table 1 - Preliminary specifications for sub-base and basecourse: Sub-Base Basecourse

Sieve Size (mm) Percent Passing Percent Passing

75.0 100 100

37.50 85-100 85-100

19.00 62-90 70-100

4.75 28-62 35-60

0.075 3-20 4-15

The material passing the 4.75mm sieve shall be additionally sieved through a 0.425mm sieve with the material passing having the following properties

Liquid Limit Maximum 35% Maximum 35%

Plastic Limit Maximum 20% Maximum 20%

Plasticity Index Maximum 15% Maximum 15%

Linear Shrinkage Maximum 6% Maximum 6%

CBR (4 day soaked) Minimum 40% @ 96% MMDD

Minimum 60% @ 98% MMDD

The above specification is one of many which are based on raw plasticity test results, which only consider the plasticity of the material <0.425m, and are not weighted for the proportion of test material (<0.425m) in the overall soil sample. Thus the specifications are inadequate as they show no consideration of the overall plasticity characteristics of the material being used in construction.

Dumbleton and West (1966) demonstrated that the plastic properties {LL, PL, and PI} of clayey soils exhibit a linear relationship which is dependent on the proportion of coarse fines in the sample. They also demonstrated that where clays are mixed with silts, or other clays, different plasticity characteristics can be obtained as demonstrated in Figure 1. As such, the raw Atterberg Limits values obtained in a test are not unique, and should not be used in specifications

without being normalised to a standard level. Morin and Todor (1970) best describe the author’s thoughts about the limitations of PSD and Atterberg Limits based specifications with this poignant quote:

“More and more engineers are realizing that the simplicity has a price. Simplification through elimination of a strength (CBR) requirement has necessitated restricting the use of materials to those which exhibit physical properties that fit into well defined limits. As these ideal materials become harder to find in temperate regions, it may become necessary to modify the AASHO specifications. This is even more true in the tropics where soils similar to temperate soils are almost impossible to find”.

(a) (b)

Figure 1 – (a) Example of Relationship between Plasticity Index and Clay Content for Mixtures of Clay Minerals with Different Coarse Fractions; and (b) Grading Curves for the materials involved. (After Dumbleton and West, 1966)

As a result of this author’s scepticism of standard specifications, a method of screening potential basecourse and sub-base material samples based on the Fine Material Factor (FMF) was introduced during the materials search. The FMF is the product of the raw Plasticity Index and the proportion of the soil passing the 0.425m sieve. The FMF screening method was initiated to save on CBR testing of unsuitable materials, and to justify soaked CBR testing of materials that were outside of standard specifications.

The Unsealed Roads Manual (ARRB, 1993) indicates that materials with a FMF < 300 to 400 are usually suitable as sub-base, basecourse, and wearing course materials. Thus a FMF of <650 was adopted as an initial guide for assessment of potentially suitable borrow materials, and the majority of samples with a FMF of < 650 were then tested for their 4 day soaked CBR when compacted at a nominal 95% Modified Maximum Dry Density (MMDD). Modified Compaction was undertaken in accordance with AS1289.5.2.1 (1998) and all samples were prepared at 100% of OMC. 95% MMDD was chosen as it was a preliminary specification for the majority of the proposed engineered fill. Some occasional outlier values were also tested to illustrate their unsuitability for borrow, and also to demonstrate the potential soaked CBR value of the subgrade within the area.

The resultant soaked CBR test results from four borrow areas, and the initial Site Investigations at Mesa A and Mesa B near Pannawonica, in Western Australia (WA) are shown in Figure 2. It was apparent from this plot that there was a potential relationship between FMF and soaked CBR which could be exploited. On this basis it was recommended that materials from the borrow pits that met grading requirements, and had a FMF of <450 could be used as borrow material for engineered fill and road construction, irrespective of their Atterberg limits and/or marginal grading, as they would produce a material with a soaked CBR of >40% when compacted to at least 95% MMDD. Statistical analyses confirmed the specification of FMF<450, with 90% of soaked CBR tests resulting in a values >= 40% as illustrated in Figure 3.

A small selection of samples were also compacted at 95% MMDD and 98% MMDD. The soaked CBR’s for samples compacted at 98% MMDD averaged 1.7 * Soaked CBR when compacted at 95% MMDD, thus confirming the selection criteria of FMF < 450 was also applicable to basecourse material selection, with the material meeting this specification likely to achieve a CBR >60% when compacted to a minimum of 98% MMDD.

Given that the use of the FMF on this site had been successfully used as a simple guide for suitability of borrow materials, it was then decided to research whether the FMF could be used over a wider range of material types to predict the minimum, design, or average soaked CBR for any soil without the need for comprehensive lab testing.

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Mesa A SI (26)

Mesa A Railway SI (4)

Mesa A, BA1 (16)

Mesa A, BA2 (34)

Mesa A, BA3 (21)

Mesa B, BP12 (12)

Upper and Lower Limits of Mesa A & B Raw Data (113 data points)

Figure 2 – Fine Material Factor (FMF) versus 4 day Soaked CBR value for all Mesa A and Mesa B Data

Figure 3 – Cumulative Frequency Distribution of Soaked CBR’s of samples at 95% MMDD with a FMF < 450 within

Mesa A and Mesa B Borrow Pits

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2. DEVELOPMENT OF A CLASSIFICATION FOR PREDICTION OF SOAKED CBR OF REMOULDED SOILS

A search of historic reports in the author’s library revealed 466 soaked CBR test results that had accompanying PSD and Atterberg Limits results. The authors own database of soil and CBR tests is predominantly from Western Australia however given the size of Western Australia [2,525,500 sq. km, or 1,021,478 sq mi], it encompasses a extremely wide variety of terrain including the pisolitic iron ore deposits near Pannawonica, the massive residual elluvium, alluvial and colluvial soils of the granite pluton south of Port Hedland, the basaltic ridges and plains of the Pilbara, to the wheat belt soils of the Yilgarn Craton, to the Sands and Clayey Sands of the Perth plain.

Despite the wide variety of spatial and geologic origins, it was confirmed that there is a consistent trend in the relationship between FMF and Soaked CBR, with the majority values from the author’s data set falling within the same well defined range determined from the Mesa A & B investigations illustrated in Figure 4.

Figure 4– Relationship between Soaked CBR and FMF for Complete Australian Dataset

A search for additional results was mostly unproductive with most soaked CBR results within the geotechnical reports discovered, since they were found to have no accompanying classification tests. Four small sets of data (57 test results) were obtained from South Africa, Thailand, Indonesia and Queensland. 12 results from South Africa were obtained from Paige-Green and Netterberg (2004) who provided data on weathered dolerites and norites, Siswosoebrotho et.al (2005) provided 13 examples of a trial of clean crushed rock mixed with 4-16% of high and low plasticity fines from Bandung in Indonesia {which were only soaked for 9 hrs}, Archwichai et.al (1993) presented the results of 28 tests from a lateritic soil region in Thailand, and Sharp et.al (2001) presented 4 results from Queensland. Despite the varied locations and soil types the soaked results again followed the trend identified at Mesa A and also fell with the identified limits as illustrated in Figure 5(a). Two large data sets of tests were presented by Morin and Todor (1970), and Brytenbach (2009). Morin and Todor presented the results of 136 tests from across Africa and another 163 from across South America. Breytenbach presented the results of over 5000 tests from a all geological environments across Africa, with the database separated into three initial compaction levels, with both these two data sets shown in Figures 5(a) and 5(b) respectively. Despite the varied locations and lithologies, the results followed the same trend and typically fell with the limits identified at Mesa A. Morin and Todor’s data showed more scatter and could be related to soil sampling and testing practice in the late 1960’s.

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Karratha, WA (5)

Cape Lambert, WA (4)

Mesa A & B, WA (116)

Rail Duplication, Pilbara, WA (31)

Millstream Link (212)

Sandstone, WA (5)

Dalwallinu, WA (6)

Windarling, WA (9)

Kalgoorlie, WA (2)

Chittering, WA (9)

York, WA (3)

Chidlow, WA (2)

Quarry, Gosnells, WA (1)

Ascot, WA (4)

Mardella, WA (3)

Yunderup, WA (2)

Busselton, WA (4)

Bunbury, WA (16)

Vasse, WA (2)

Narrogin, WA (5)

Eyre Hwy, Dundas, WA (1)

Gawler, SA (11)

Victoria Hwy, NT (4)

Upper and Lower Limitsof Mesa A & B Raw Data

Data Set = 457 points

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African Soils @ 100% MMDD - Brytenbach

Upper and Lower Limits of Mesa A & B Raw Data

(a) (b) Figure 5 - FMF and Soaked CBR for (a) International Data Sets and (b) Breytenbach’s 100% MMDD Data.

It should be noted that for samples in the author’s database, all PSD results have been recalculated to account for the situation where +19mm was removed from the CBR sample (without replacement) prior to testing which is common in Australian testing. The reason for this is that the notes accompanying AS 1289 6.1.1 quote the following:

The removal of small amounts of stone retained on the 19 mm sieve will affect the CBR obtained only by amounts comparable with experimental error involved in measuring CBR.”

Thus the laboratories and geotechnical professionals are led to believe that removal of +19mm gravels is acceptable. Nothing could be further from the truth, as experimental error in CBR tests can be significant and the removal of small amounts of +19mm material is significant. The second sentence of the same note within AS 1289 6.1.1 manages to contradict the first by confirming the deleterious effect of removing the +19mm material as follows:

The exclusion of a large portion of stone coarser than 19 mm (such as is present, for example, in a gravel of 75 mm maximum size) may have a major effect on CBR compared with that obtainable with the soil as a whole, and on the optimum moisture content.

Figure 6– Relationship between % -19mm material initially in sample and Soaked CBR

In order to confirm this effect, the author undertook a series of soaked CBR tests using a set of samples from one relatively homogenous area in a borrow pit, with the +19mm material removed without replacement as shown in Figure 6. It is clearly evident that where samples that had a small proportions of gravel initially, (ie 80-95% passing the +19mm sieve), and it was removed without replacement in the CBR test the soaked CBR of the material reduced significantly, inversely in proportion to the percentage of +19mm gravel removed from the initial sample. It is concluded that this practice of +19mm removal without replacement in CBR tests should be avoided as it does not represent the material in the field and significantly reduces the CBR value for the material. The author recommends that the procedure outlined in the notes of AS 1289 6.1.1 is specified and followed for all CBR testing.

The procedure is as follows:

The same percentage of coarse material (passing the 53 mm and retained on the 4.75 mm sieves) in the test sample as in the original field sample is to be maintained, the material retained on the 19 mm sieve may be replaced by an equal portion by mass of the materials passing the 19 mm sieve and retained on the 4.75 mm sieve.

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African Soils - Morin & Todor

South American Soils - Morin & Todor

Thailand Soils - Archiwichai

Indonesia - Siswosoebrotho

Sth Africa - Paige-Green

Queensland - Sharp

Upper and Lower Limits of Mesa A & B Raw Data

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% Passing 19mm

95% MMDD Soaked

It is important that the maximum dry density and optimum moisture content used to determine the laboratory density ratio and laboratory moisture ratio be determined on the material passing the 19 mm sieve which is the same as that used to determine the CBR value. (preceding sentence needs rewording)If material retained on the 19 mm sieve is replaced by an equal portion by mass of materials passing the 19 mm sieve, the values determined on material without the replacement are not applicable. The percentage by mass of material retained on the 19 mm sieve and whether it was excluded or replaced should be included in the report.

This practice was followed with the Mesa A and B borrow pit search, which subsequently reduced the propensity for low values which occurred at the site investigation stage. The CBR curves were also supplied by the testing laboratory during Mesa A materials search, thus allowing verification of the data and calculation to the nearest 1% for the dataset, thus preventing rounding biasing the dataset.

Given the observed significant relationship between FMF and CBR, further investigation of the effect of increasing compaction on the CBR was undertaken. The degree of compaction prior to soaking was investigated and the results for the author’s dataset are plotted in Figure 7. It was observed in Figure 7 that the degree of compaction prior to soaking did not affect the observed relationship between FMF and CBR, despite the bias of samples in the 96-98% MMDD range all being tested at FMF values < 1000.

The effect of soaking on sample swell, thus changing the relative compaction was observed to be significant within the author’s dataset. Hence the actual relative compaction value at the time of the CBR test was investigated further. The dataset plotted against relative compaction after soaking is shown in Figure 8. From the curves of best fit for each of the compaction ranges, it was evident that as the relative compaction at the time of test increased, the best fit power curve moved up the graph and to the right. It was believed that a series of best fit curves were not the best representation of the data, so further investigation of the effect of increasing relative compaction at the time of testing on the CBR value was undertaken.

Figure 7– Relationship between relative modified compaction of sample before soaking, and FMF and CBR.

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Soaked CBR <90% MMDD, n=6

Soaked CBR 90-92% MMDD, n=20




Soaked CBR 98-100% MMDD, n =81

Soaked CBR > 100% MMDD, n =6

Figure 8– Relationship between relative modified compaction of sample after soaking and FMF and CBR.

Figure 9 – Relative change in Soaked CBR value with increase in compaction ratio

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Soaked CBR <90% MMDD, n=29






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Power (Soaked CBR <90% MMDD, n=29)

Power (Soaked CBR 90-92% MMDD, n=52)

Power (Soaked CBR 92-94% MMDD, n=64)

Power (Soaked CBR 94-96% MMDD, n=178)Power (Soaked CBR 96-98% MMDD, n=95)

Power (Soaked CBR 98-100% MMDD, n=40)Power (Soaked CBR > 100% MMDD, n =6)

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Weathered Dolerite, South Africa

Weathered Norite, South Africa

Weathered Basalt, CH 22800 Millstream Link

Basalt Scree, CH 26700 Millstream Link

Weathered Tuff, CH 47500 Millstream Link

Weathered Tuff, CH 47500 Millstream Link

Power (Weathered Dolerite, South Africa)

Power (Weathered Norite, South Africa)

Power (Weathered Basalt, CH 22800 Millstream Link )

Power (Basalt Scree, CH 26700 Millstream Link)

Power (Weathered Tuff, CH 47500 Millstream Link)

Power (Weathered Tuff, CH 47500 Millstream Link)

From the various data sets it was found that when a series of soaked CBR tests was undertaken on the same samples compacted at varying MMDD ratio’s varying from 90% to 100% there was a consistent non linear increase in the soaked CBR value for each sample, despite the differing soil origins and CBR value. Thus in order to isolate the effect of increasing compaction from the material itself, the ratio of CBR at 100%, 98%, 97%, 95% and 93% MMDD to that at 90% MMDD was calculated for each sample and plotted in Figure 9. [Please note each of these samples did not swell]

Using the relationships observed in Figure 9, a family of design ratio’s were then developed for soaked CBR value at 90%, 92%, 95%, 98% and 100% MMDD using the following correlations:

Soaked CBR @ 92% MMDD = 1.5 * Soaked CBR @ 90% MMDD




These ratio’s were then applied to deriving appropriate design curves for the dataset instead of the best fit curves shown in Figure 8. Using the above ratio’s, the following family of design curves were developed as shown in Figure 10 and below [Please note they are for MMDD ratio after Soaking and not before]:

Soaked CBR @ 90% MMDD = 20 * FMF-0.9





When these curves were plotted against the West Australian data set in Figure 11 were then statistically checked against the compaction points. The findings were as follows:

Soaked CBR @ 90% MMDD +/- 0.5% = 89% of values on or above design line




Soaked CBR @ 100% MMDD +/- 0.5%= 100% of values on or above design line

The results of the statistical analyses indicate that the proposed correlation curves are within normally accepted ranges for probability based design and can be used as minimum design curves for predicting the soaked CBR based on the FMF and possible swell of natural occurring soils. The design curves become more reliable (and thus more conservative) with increasing compaction. Please note that the calculated CBR is in percentage format.

For practical engineering purposes this design correlation should be limited to assessing material with a nominal FMF < 1500, as typically for construction, materials with a soaked CBR > 15-20% are required. The correlation should also generally be limited to FMF values < 1500-2500 as there is a scarcity of data to confirm the effect of compaction on soaked CBR at high FMF values. Ideally this design correlation would also be capped at CBR of 100% with this being a practical limit for the design value of pavement materials.

It should also be noted that the aim of design curves and this research was to quantify the anecdotal relationship between FMF and CBR used in materials searches, and not to be used a primary design tool for subgrade or basecourse design. For materials searches and basecourse design, the curves are a tool to assess the potential of naturally occurring materials to produce acceptable basecourse or fill materials meeting a minimum CBR specification.

Once an area of suitable materials is identified it is still recommended that an adequate number of CBR tests is undertaken to confirm the degree of swelling and subsequent actual soaked CBR value of the materials being used to ensure that overly conservative CBR values are not used which potentially could result in unnecessary construction costs.

Figure 10 –Family of Design Curves for FMF, Soaked CBR, and MMDD Ratio After Soaking

Figure 11 – Example of Soaked CBR values relative to design curves

Soaked CBR @ 90% MMDD = 20* FMF-0.9






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y = 20x-0.9

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y = 30x-0.9

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Soaked CBR, 90% MMDD +/- 0.5%





Power (90% MMDD Design Line)





3. ACKNOWLEDGEMENTS

The author gratefully acknowledges the assistance of the Millstream Link Alliance, making data available from that project.

4. REFERENCES

Archwichai, L, Youngme, W., Somphdung, S., Changsuwan, S., Wannakao, P., Hokjaroen., S., and Wannakao, L. (1993). Engineering properties of lateritic soils from Khon Kaen and its vicinity, Thailand. Journal of SouthEast Asian Earth Sciences, Vol. 8, No’s 1-4,. pp. 549-556, 1993

AS 1289.5.2.1 (2003). Australian Standard™: Methods of testing soils for engineering purposes. Method 5.2.1: Soil compaction and density tests—Determination of the dry density/moisture content relation of a soil using modified compactive effort. Standards Australia.

AS 1289.6.1.1 (1998). Australian Standard™: Methods of testing soils for engineering purposes. Method 6.1.1: Soil strength and consolidation tests—Determination of the California Bearing Ratio of a soil—Standard laboratory method for a remoulded specimen. Standards Australia.

Australian Road Research Board (1993). Unsealed Roads Manual – Guidelines to Good Practice.

Breytenbach I.J., (2009). The relationship between index testing and California Bearing Ratio values for natural road construction materials in South Africa. M.Sc. dissertaton, Department of Geology, Faculty of Natural and Agricultural Sciences, University of Pretoria, South Africa.

Country Roads Board of Victoria (1980). The design of flexible pavements - Technical Bulletin No.31.

Dumbleton, M.J., and West, G., (1966). The influence of the coarse fraction on the plastic properties of clay soils. Ministry of Transport, RRL Report No. 36.

Main Roads Western Australia (2009). Engineering Road Note 9, April 2009 – Procedure for design of flexible pavements.

Morin, W.J., and Todor, P.C., (1970). Laterite and lateritic soils and other problem soils of the tropics: An engineering evaluation and highway design study for United States Agency for International Development. AID/csd 3682. Lyon Associates Inc., and Road Research Institute, Brazilian National Highway Department

Paige-Green, P. and Netterberg, F., (2004). Cement stabilization of road pavement layers: Laboratory testing programme Phase 1. CSIR, Confidential Contract Report CR-2003/42, June 2004.

Sharp, K., Vuong, B., Rollings, R., Baran, E., Foley, G., Johnson-Clarke, J., and Metcalf, J. (2001). An evaluation of the field and laboratory propertites of lateritic gravels. ARRB, Research Report ARR343.

Siswosoebrotho, B.I, Widodo, P., and Augusta, E., (2005). The Influence of Fines Content and Plasticity on the Strength and Permeability of Aggregate for Basecourse Material. Proceedings of the Eastern Asian Society for transportation Studies, Vol. 5, 2005, pp. 845-856,

prediction of soaked cbr for coarse grained soil mixtures condensed to 10 pages

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