Prediction of dynamic modulus of asphalt concrete using hybrid machine learning technique

Due to the issues like operational difficulties, and extensive resource requirements at pavement design stage, dynamic modulus (vertical bar E*vertical bar) is estimated using predictive models. However, these models suffer from issues like systematic bias, prediction accuracy, and extensive testing requirements. This study presents a novel hybrid Principal Component Analysis (PCA) - Gene Expression Programming (GEP) approach to predict the vertical bar E*vertical bar of asphalt concrete. The database developed during NCHRP 9-19 study was used for developing this methodology. The information of all properties (i.e. variables) was used as input. PCA helped in removing the redundancy at the input stage while reducing the dimensionality. The extracted principal components (PC's) were used to develop first set of vertical bar E*vertical bar predictive models. The second set of vertical bar E*vertical bar predictive models were developed using the parameters mostly contributing to the individual PC's. Comparison of these two sets indicated that predictive model obtained using variables as direct input resulted in improved accuracy. Comparison of this finalized model with the existing regression-based equations using goodness of fit indicators indicated that proposed hybrid model offers efficient and accurate alternative. The proposed model has flexibility to be used with any new database with recalibration.