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Table 17
Model Correlations Developed by Malla and Joshi (2006)
Several independent variables are used to reflect soil of their study. They noted considerable differences between
type and current soil physical condition (Titi et al. 2006). predictions and measurements, which were attributed to dif-
These are percent passing sieve #4 (PNo.4), percent passing ferences in test procedures and other conditions present in
sieve #40 (PNo.40 ), percent passing sieve #200 (PNo.200 ), the LTPP database.
liquid limit (LL), plastic limit (PL), plasticity index (PI),
liquidity index (LI), amount of sand (%Sand), amount of
silt (%Silt), amount of clay (%Clay), water content (w), Matrix Tables
and dry unit weight (γd). The optimum water content
(wopt.) and maximum dry unit weight (γdmax) and combi- In this section, the existing literature information is sum-
nations of variables were also included (Titi et al. 2006). marized in a matrix format. The main focus of the table is to
The developed models were evaluated based on the mul- provide a thorough assessment of various laboratory and field
tiple collinearity problems and coefficient determination methods for determining the resilient properties of unbound
values. bases and subgrades. Again, this assessment is based on the
available information presented in this report.
Table 18 presents equations recommended for fine-
grained, plastic coarse-grained, and nonplastic coarse- Tables 19 and 20 provide a matrix-style comparison of
grained soils. Figure 86 presents a typical comparison various items, including applicable soil types, relation to
analysis for fine-grained soils. The results show that the pre- design modulus, type of MR interpretation (direct or indi-
dicted moduli using recommended equations matched well rect), standardization, need for skilled personnel to perform
with measured moduli. the test, cost details, applicability in new pavement con-
struction projects, pavement rehabilitation projects, need of
Titi et al. (2006) also presented a comprehensive analysis additional tests for validation, and type of MR correlations
in which they predicted moduli using correlations developed that the test results provide. Table 21 presents an overview of
from the LTPP database by Yau and Von Quintus (2004) the assessments of the modeling correlations. Assessments
and compared the correlations with the measured moduli are based on repeatability and reliability of correlations,
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Table 18
Correlations Developed for Wisconsin Subgrade Soils (Titi et al. 2006)
Soil type Model Correlations
Fine-Grained Soils
Coarse-grained,
non-plastic
Coarse-grained, Plastic
