Predictive models for dynamic modulus using weighted least square nonlinear multiple regression model

The objectives of this paper are (1) to evaluate the dynamic modulus prediction models and (2) to develop an alternative prediction model using the nonlinear multiple regression method. A total of 14 field produced mixture types (in a total of 1314 measurements) with various designed traffic levels and aggregate sizes were used. Two prediction models, the Witczak prediction model developed in 2006 and the Hirsch model developed in 2003, were revised in this study. In addition, the revised Witczak prediction equation was simplified using fewer independent variables (11 variables instead of 21 variables); and values of the newly revised coefficients with improved accuracy for Hirsch model are presented in this paper. A new model using the weighted least square nonlinear multiple regression model (WLS NLR) was developed in this study. It was found that the WLS NLR has a better prediction when compared with other prediction models used in this study.