Neural Network Based Model to Estimate Dynamic Modulus E* for Mixtures in Costa Rica

Abstract

Several dynamic modulus (E*) predictive models of asphalt mixtures have been developed as an alternative to laboratory testing. The 1999 I-37A Witczak equation is one of the most commonly used alternatives. This equation is based on mixtures laboratory results in the U.S. In Latin American countries there are significant differences in material properties, traffic information, and environmental conditions compared to the U.S.; therefore, there is a limitation is the use of this equation using local conditions. The National Laboratory of Materials and Structural Models at the University of Costa Rica (LanammeUCR) has previously performed a local calibration of this equation based on results from different types of Costa Rican mixtures. However, there was still room for improvement using advanced regression techniques such as neural networks (NN). The objective of this study was to develop an improved and more effective dynamic modulus regression model for mixtures in Costa Rica using Neural Networks. Results indicated that the new and improved model based on neural networks (E* NN-model) not only met the model adequacy checking criteria but also exhibited the best goodness of fit parameters and the lowest overall bias.