Dynamic Analysis-Based Approach To Determine Flexible Pavement Layer Moduli Using Deflection Basin Parameters

Abstract

Most of the deflection analysis programs used today to analyze falling weight deflectometer (FWD) data are based on static analysis, which often underestimates the subgrade strength. Unfortunately, dynamic analysis usually involves complex calculations and requires significant computation time, thus making it impractical for routine applications. A methodology based on deflection basin parameters and artificial neural networks (ANN) for processing dynamic FWD measurements to estimate layer strengths is presented in this paper. Two-dimensional, dynamic finite element analysis using the ABAQUS program was employed to develop the deflection information for this study. Unlike the majority of the existing backcalculation programs that iteratively adjust the layer moduli to match the measured deflections, the proposed method first determines the subgrade modulus by means of two deflection basin parameters—Base Damage Index and Shape Factor F2—and then applies the estimated subgrade modulus and other parameters as input variables to a trained ANN to estimate the upper layers’ moduli. In contrast to other programs that require the input of seed values for layer moduli, this method does not require initial estimates as input. A set of field FWD measurements were analyzed both by this method and by the MODULUS program. Results reveal that the proposed method is able to better predict the asphalt concrete layer modulus while taking into account the dynamic effects of the FWD test. This method is also shown to be computationally efficient, which makes it applicable for routine tasks and field use.