KNOWLEDGE DISCOVERY AND DATA MINING IN PAVEMENT INVERSE ANALYSIS

This paper describes the use of data mining tools for predicting the non-linear layer moduli of asphalt road pavement structures based on the deflection profiles obtained from non-destructive deflection testing. The deflected shape of the pavement under vehicular loading is predominantly a function of the thickness of the pavement layers, the moduli of individual layers, and the magnitude of the load. The process of inverse analysis, more commonly referred to as 'backcalculation; is used to estimate the elastic (Young's) moduli of individual pavement layers based upon surface deflections. A comprehensive synthetic database of pavement response solutions was generated using an advanced non-linear pavement finite-element program. To overcome the limitations associated with conventional pavement moduli backcalculation, data mining tools such as support vector machines, neural networks, decision trees, and meta-algorithms like bagging were used to conduct asphalt pavement inverse analysis. The results successfully demonstrated the utility of such data mining tools for real-time non-destructive pavement analysis.