Backcalculation of pavement layer moduli and Poisson’s ratio using data mining

Abstract

Pavement deflection data are often used to evaluate a pavement’s structural condition non-destructively. Pavement layers are characterized by their elastic moduli estimated from surface deflections through backcalculation. Using backcalculation analysis, flexible pavement layer in situ material properties can be backcalculated from the measured field data through appropriate analysis techniques. This study focuses on the use of data mining (DM)-based pavement backcalculation tools for determining the in situ elastic moduli and Poisson’s ratio of asphalt pavement from synthetically derived Falling Weight Deflectometer (FWD) deflections at seven equidistant points. In estimation of the elastic modulus and Poisson’s ratio, data mining (DM) method has not been used as a backcalculation tool before. Experimental deflection data groups from NDT are used to show the capability of the DM approaches in backcalculating the pavement layer thickness and compared each other. By looking at the results of the study, Kstar method gives fine results with respect to other DM methods. Backcalculation of pavement layer elastic modulus and Poisson’s ratio with DM has been carried out for the first time.