Guzina, BojanCao, Dongwei2022-06-062022-06-062001-10https://hdl.handle.net/11299/227818The Falling Weight Deflectometer (FWD) is a widely used non-destructive test device for estimating the pavement stiffness properties. However, the conventional elastostatic interpretation of FWD measurements is generally associated with a number of inconsistencies. The purpose of this project is to develop a reliable and effective dynamic backcalculation method capable of estimating the location and properties of the permanent or seasonal stiff layer (as well as other pavement stiffness properties) from FWD measurements. The backcalculation method is implemented in the form of a user-friendly software that allows unedited deflection time histories from the FWD test to be used as an input to the back-analysis. The backcalculation scheme developed in this study is based on the Artificial Neural Network (ANN) approach and employs a three-dimensional multilayer viscoelastic dynamic model as a predictive tool.enFalling weight deflectometerBackcalculationHot mix asphaltArtificial neural networkReport