This paper focuses on the development of backcalculation models based on artificial neural networks (ANNs) for predicting the layer moduli of the jointed plain concrete pavements, that is, the elastic modulus of the portland cement concrete (PCC) layer and the coefficient of subgrade reaction for the pavement foundation. The ANN-based models were trained to predict the layer moduli by using the falling-weight deflectometer (FWD) deflection basin data and the thickness of the concrete pavement structure. The ISLAB2000 finite element program, extensively tested and validated for more than 20 years, has been employed as an advanced structural model for solving the responses of the rigid pavement systems and generating a knowledge database. ANN-based backcalculation models trained with the results from the ISLAB2000 solutions have been found to be viable alternatives for rapid assessment (capable of analyzing 100,000 FWD deflection profiles in a single second) of the rigid pavement systems. The trained ANN-based models are capable of predicting the concrete pavement parameters with very low (