Regression and Artificial Neural Network Modeling of Resilient Modulus of Subgrade Soils for Pavement Design Applications

A combined laboratory and modeling study was undertaken to develop a database for common subgrade soils in Oklahoma and to develop relationships or models that could be used to estimate resilient modulus (M

R

) from commonly used subgrade soil properties in Oklahoma. Sixty-three soil samples from 14 different sites throughout Oklahoma are collected and tested for the development of the database and models. Additionally, thirty-four soil samples from 3 different sites, located in Rogers and Woodward counties, are collected and tested to evaluate the developed models. The routine material parameters selected in the development of the models include moisture content (w), dry density (

γ

d

), plasticity index (PI), percent passing No. 200 sieve (P

200

), and unconfined compressive strength (Uc). Bulk stress (θ) and deviatoric stress (

σ

d

) are used to identify the state of stress. A total of four, two regression models, namely, Polynomial and Factorial, and two feedforward-type artificial neural network (ANN) models, namely, Radial Basis Function Network (RBFN) and Multi-Layer Perceptrons Network (MLPN) are developed. A commercial software, STATISTICA 7.1, is used to develop these models. The strengths and weaknesses of the developed models are examined by comparing the predicted M

R

values with the experimental values with respect to the R

2

values. An evaluation of the four models indicate that for the combined development and evaluation datasets, the MLPN model is a good model for evaluating M

R

from the selected routinely determined properties. In order to illustrate the application of the developed model, the AASHTO flexible pavement design methodology is used to design asphalt concrete pavement sections.