LAS CONDUCTAS QUE SURGEN EN EL AULA DE CLASE A CAUSA DE LA

As shown in Figure 4.7, both CBR and MR decrease with increasing moisture

content for the 8 soils tested. The highest CBR and MR values were found on the dry side

of wopt and the lowest CBR and MR values were found on the wet side of wopt. Correlations

between resilient modulus and CBR are shown in Figure 4.8. Figure 4.8(a) shows the correlation between resilient modulus of remolded soil samples and laboratory CBR for both 0.1 in. and 0.2 in. penetration and indicates that CBR increases with increasing MR for

both penetrations, with the MR for 0.1 in. penetration being about 6% higher than 0.2 in.

penetration. The correlation equations have a coefficient of determination of 0.40 and 0.48 for 0.1 in. penetration and 0.2 in. penetration, respectively. Figure 4.8(b) shows the correlation between resilient modulus and CBR as a function of different soil types and

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indicates that CBR increases with increasing MR for all of the soils tested herein. The

following correlation equation between MR and CBR was developed for South Carolina

using the CBR data for all 8 soils at 0.10 in. penetration:

𝑀𝑅(𝑝𝑠𝑖) = 5182×𝐶𝐵𝑅0.35 4.8 Resilient modulus tests were also performed for Shelby tube samples collected from 8 different boreholes. CBR tests were then performed with the same moisture content and density to correlate resilient modulus of undisturbed soil samples with CBR. Figure 4.8(c) shows the correlation between resilient modulus of undisturbed soil samples and laboratory CBR for both 0.1 in. and 0.2 in. penetration and indicates that CBR increases with increasing MR for both penetrations, with the MR for 0.1 in. penetration being almost same

values with the 0.2 in. penetration. The correlation equations have a coefficient of determination of 0.64 and 0.63 for 0.1 in. penetration and 0.2 in. penetration, respectively. Figure 4.8(d) shows the correlation between resilient modulus and CBR as a function of different soil types and indicates that CBR increases with increasing MR for all of the soils

tested herein. The following correlation equation between the MR for undisturbed samples

and CBR was developed for South Carolina using the CBR data for all 8 soils at 0.10 in. penetration:

𝑀_𝑅(𝑝𝑠𝑖) = 4457×𝐶𝐵𝑅0.22 _4.9 Using the developed CBR correlation equations, for the same moisture content and density, MR found using the undisturbed samples for the 8 borehole locations were

compared with the MR found for the laboratory remolded specimens. Figure 4.8(e) shows

the relation between remolded MR and undisturbed MR. It indicates that remolded MR is

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coefficient of determination (R2 _{= 0.65). To compare with the CBR, for both undisturbed}

and remolded soil samples resilient modulus, CBR tests have been performed for the same moisture content and density found in undisturbed/remolded samples. It signifies the aging of pavement may have effects on lower resilient modulus for undisturbed soil samples as the selected pavement sections are at least 10 years old.

4.8 CORRELATION OF MODEL PARAMETERS WITH SOIL INDEX PROPERTIES

Using multiple liner regression techniques, the generalized constitutive resilient modulus model parameters (𝑘₁, 𝑘₂, 𝑎𝑛𝑑 𝑘₃) for remolded soils were correlated with soil index properties. The soil properties considered in the statistical analysis include the compacted soil dry density (𝛾𝑑), moisture content (𝑤), maximum dry density (𝛾𝑑,𝑚𝑎𝑥), optimum moisture content (𝑤𝑜𝑝𝑡), percent passing through No. 4 (𝑃4), No. 40 (𝑃40), and No. 200 sieve (𝑃200), 𝐷60, 𝐷50, 𝐷30, 𝐷10, uniformity coefficient (𝐶𝑢), coefficient of curvature (𝐶_𝑐), liquid limit (LL), plastic limit (PL), plasticity index (PI), liquidity index (LI), specific gravity (𝐺_𝑠), and the percent sand, silt, and clay. Combined statistical models were developed using the results for the 8 soils. All of the soils are classified as coarse grained soils (𝑃200>50%) except for Pickens B-4, based on ASTM D 2488. Table 4.3 shows the coefficients for the developed models. Coefficients of determination (𝑅2_{) of 0.43, 0.61} and 0.71 were found for k1, k2, and k3 respectively. Data sample size was limited to 30 soil

samples for the 8 borehole locations. These developed models would be considered as representative statistical models for South Carolina as the soil samples are collected from three different locations of two different geographic regions. However, It is recommended

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to increase the sample size and to develop two separate models for Type A and Type B soils of South Carolina to improve these models.

Table 4.3 shows the significance of different soil properties on the coefficients and overall model significance using 𝑝-value, where p < 0.001 indicates a statistically highly significant effect. p < 0.01 and p < 0.001 indicate statistically moderate and low significant effects, respectively. For the 8 soils tested, the 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 moisture content and dry density showed a statistically significant effect on 𝑘1, and moisture content, dry density, and 𝐺𝑠 showed statistically significant effect on 𝑘₂. The other soil index properties (i.e., 𝑃₄₀, 𝑃₂₀₀, 𝐷₆₀, 𝐷₅₀, 𝐷₃₀ , 𝐷₁₀ , 𝐶_𝑢, 𝐶_𝑐, LL, PL, PI, and the percent sand, silt, and clay) did not show a statistically significant effect on any of the model parameters).

Predicted and measured 𝑘1, 𝑘2, 𝑘3 are shown in Figure 4.9(a), 4.9(b), and 4.9(c) respectively. Model coefficients k1, k2, and k3 are the regression constants of Equation 4.2,

and therefore, these were measured from the applied bulk stresses, and octahedral shear stresses, and the resultant resilient modulus values obtained from 15 different test sequences for each test using regression analysis. Most of the data points for all three models are observed close to the line of equity.

The developed constitutive models of coefficients in Table 4.3 were used to determine 𝑀𝑅 from Equation 4.2 for a representative bulk stress 154.64 kPa and octahedral stress of 13 kPa and defined as the predicted 𝑀𝑅. The laboratory measured 𝑀𝑅 is compared to the predicted 𝑀_𝑅 in Figure 4.10(a) and to the LTPP sand model in Figure 4.10(b). As shown, the predicted 𝑀_𝑅 (from the locally developed constitutive model) more accurately

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predicted 𝑀_𝑅 than the LTPP sand model in terms of lower bias (e.g. -2.07 vs. 37.40 in Figure 4.10(a) and Figure 4.10(b)) and standard error (e.g. 21.56 vs. 34.59 in Figure 4.10(a) and Figure 4.10(b)). LTPP model for silts and LTPP model for clay were also studied. However, LTPP model for sand showed better results when compared to the measured MR

for the soils studied herein. Figure 4.10 demonstrates the importance of performing local calibration studies to find the constitutive model parameters for use in the MEPDG, rather than using the universal constitutive model parameters (i.e., for the Universal LTPP model for sand) that were found within the LTPP program (Yau and Quintus, 2004) from studies on soils and unbound pavement materials from all over the United States.

4.9 MOISTURE EFFECT OF SUBGRADE RESILIENT MODULUS ON