Conclusions Based on Undisturbed Subgrade Soil Resilient Modulus Study Resilient modulus of subgrade soils was characterized for different regions in South

Carolina. Shelby tube samples of subgrade soils were collected from existing pavements in three different locations. Statistical analysis was performed to develop resilient modulus estimation models for undisturbed soils using soils index properties. Regarding the undisturbed subgrade soil resilient modulus study, the following conclusions are made:

▪ For undisturbed soil sample resilient modulus tests, even for a relatively short pavement section (1.34 miles long in Pickens), resilient modulus showed a wide range of values (COV = 42%) that must be considered when selecting input values for MEPDG.

▪ Resilient modulus found for undisturbed soil samples did not show a distinct pattern with the in-situ moisture content as has been shown for laboratory prepared samples. Good correlation was obtained between laboratory resilient modulus and the FWD modulus and can be used to estimate 𝑀_𝑅 as a Level 3 input.

▪ For granular materials, percent passing No. 4 sieve and percent sand showed statistically significant effect on 𝑘1. Percent silt showed statistically significant effect on 𝑘₂, and percent passing No. 4 showed statistically significant effect on 𝑘₃. For silt-clay materials, liquidity index, plasticity index in-situ water content and dry density showed a significant effect of on 𝑘₁ and 𝑘₂.

▪ Developed constitutive models predicted the resilient modulus more accurately (standard error was 11.52 and 18.63 for granular and silt-clay materials,

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respectively) than the universal LTPP models (standard error was 13.60 and -21.14 for granular and silt-clay materials, respectively).

▪ Subgrade rutting predicted by the developed constitutive model was in closer agreement to the rutting predicted by the laboratory measured resilient modulus than the FWD model or LTPP model.

5.2.3 Conclusions Based on Remolded Subgrade Soil Resilient Modulus Study

Correlations between the subgrade soil resilient modulus obtained for remolded samples and California Bearing Ratio (CBR) were established for a range of moisture content. Statistical models were developed to correlate generalized constitutive resilient modulus model parameters with soil index properties. The soil samples were prepared at moisture contents above and below the optimum moisture content. Pavement rutting was studied using the resilient modulus determined for the subgrade soils compacted at wopt and

±2%wopt. Regarding the study of remolded subgrade soil resilient modulus, the following

conclusions are made:

▪ The peak value of both CBR and resilient modulus was not found at the optimum moisture content and maximum dry density, rather it was found on the dry side of optimum and at a dry density less than the maximum.

▪ For different types of coarse grained soils, soil compacted 2% dry of optimum showed higher resilient modulus than soil compacted at the optimum moisture content Soil compacted 2% wet of optimum showed lower resilient modulus than soil compacted at the optimum moisture content.

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▪ Resilient modulus increases with increasing cyclic stress for samples compacted 2% dry of optimum but it decreases with increasing cyclic stress for samples compacted 2% wet of optimum.

▪ Resilient modulus always decreases for increasing moisture content for different types of soils. However, no distinct relation between resilient modulus and soil dry density was shown.

▪ Good correlation was made between soil resilient modulus and CBR for both remolded soil and undisturbed soil. Resilient modulus increases with increasing CBR (for both 0.1 in. and 0.2 in. penetrations) for different types of soils. It was found that remolded MR is 1.5 times higher than that of the undisturbed soil samples

for the same CBR with good coefficient of determination (R2 = 0.65).

▪ For remolded materials, percent passing No. 4 sieve, liquidity index, optimum moisture content, and maximum dry density showed a statistically significant effect on all three model coefficients (𝑘₁, 𝑘₂, and 𝑘₁).

▪ The locally developed constitutive models quantified the improvement in prediction of the 𝑀𝑅 more accurately than the universal LTPP models in terms of lower bias (e.g. -2.07 vs. 37.40) and standard error (e.g., 21.56 vs. 34.59).

▪ It was found that the subgrade soil moisture condition has a significant influence on the subgrade resilient modulus and resulting subgrade rutting if graded aggregate base is used. However, if a higher strength base layer is used (e.g., cement stabilized base or asphalt treated aggregate base), the moisture effect is less significant.

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5.3.OVERALL CONCLUSIONS

In summary, the following conclusions can be made based on this study:

▪ Two different types of soil above and below the fall line have statistically significant effect on South Carolina pavement performance.

▪ Resilient modulus for both the undisturbed and remolded soil samples increases with increase in moisture content for different type of South Carolina soils. In general, w, wopt, γd, γd,max, P4, and LI have significant effect on three resilient

modulus model parameters (k1, k2, and k3) for both undisturbed and remolded soil samples. The locally developed constitutive models of coefficients predicted resilient modulus more accurately than the universal LTPP models for both undisturbed and remolded soil samples.

▪ Resilient modulus found for undisturbed soil samples did not show a distinct pattern with the in-situ moisture content. However, resilient modulus decreases with increasing moisture content for remolded soil samples. The subgrade soil moisture condition has a significant influence on the subgrade soil resilient modulus and the resulting subgrade rutting if graded aggregate base is used.

▪ The locally developed constitutive models of coefficients predicted resilient modulus more accurately that the universal LTPP models for both undisturbed and remolded soil samples.

▪ Developed models (evaluation, index properties, FWD, CBR) can be used to estimate resilient modulus and predict pavement rutting and hence, enhance MEPDG local calibration for South Carolina.

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