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.
- artificial neural network (ANN)
- statistical analysis
ASJC Scopus subject areas
- Automotive Engineering
- Mechanical Engineering