Genetic Algorithm to Optimize Layer Parameters in Light Weight Deflectometer Backcalculation

Abstract

The light weight deflectometer (LWD) is a portable, nondestructive testing device that can estimate pavement layer parameters, namely moduli. Conventional backcalculation of layer parameters from LWD deflections is formulated as an inverse problem where predicted vertical deflections are matched to observed vertical deflections using a gradient search algorithm. In this paper, we present an LWD backcalculation scheme to recover layer parameters, including top-layer thickness, of a two-layer earthwork system. Our approach resolves the problem using a dynamic finite-element (FE) model for the forward calculation of LWD deflection data and implements a genetic algorithm (GA) as the inverse solver. The objective function we minimize is formulated as a measure of the data misfit between predicted and observed data, normalized by the peak deflections, and it includes 180 data points from the dynamic deflection time history. The objective function contains multiple local minima that can potentially trap gradient search algorithms, thus validating application of GA as a global search technique for this problem. The GA is applied to both synthetic and experimental data, and we demonstrate that the recovered top-layer thickness, top-layer modulus, and underlying modulus for the experimental data compare favorably with expected values.