Evaluation of Multilayer Pavement Viscoelastic Properties from Falling Weight Deflectometer using Neural Networks

Abstract

The surface deflection caused by the falling weight deflectometer (FWD) is a dynamic problem usually treated as a static problem. This chapter presents a dynamic solution of the backcalculation problem using a viscoelastic model, introducing a viscosity variable. The calibration process is based on the numerical simulation of the FWD test to approximate the deflection curves obtained in the road track. Description of the experimental curves and the hypothesis of the numerical problem assumed are also described. The samples used to train and validate the artificial neural network (ANN) are conformed by four values per curve times the number of geophones, the input data, while the output data are five Young moduli and five viscosities. The backcalculation process is based on experimental tests from the FWD and calibrated using an FEM simulation of the problem.