Pavement structural evaluation and rehabilitation designs are highly dependent on the in-situ layer properties. The 2004 AUSTROADS Pavement Design Guide emphasized that much of the misunderstanding of pavement design, and resulting pavement failures over the past 20 years has been associated with uncertainty and resulting lack of reliability in design. In pavement design and evaluation, the risk concept is applicable for the input parameters with a high degree of uncertainty and that have an impact on the final outcome of the design process. It was concluded that in order of significance, the base layer thickness, 1st geophone deflection, and the AC layer thickness are the variables that their exclusion causes large errors.The risk concept provides a means of incorporating some degree of certainty into the process to ensure that the outcomes of the process will provide acceptable levels of service until the end of the intended design life. Finally, the significance of different input variables was assessed using an indirect method by excluding each from the analysis and checking of the model predictability power. It was concluded that the results from the latter method are in better agreement with the theories. Moreover, an analysis was conducted to assess the contribution of inputs in moduli prediction for each of the pavement layers using the Garson algorithm and the Connection Weight methods. For validation, the results from the developed model were compared with typical backcalculation software of ISSEM4, MODCOMP, MODULUS, WESDEF, and BAKFAA as well as 386 LTPP sections. Results indicate that the ANN model can predict the modulus of different layers accurately with a coefficient of determination ( R 2) of more than 0.999 in all cases. The transfer function is sigmoid for hidden layers and linear for the output layer. The optimum neural network consists of two hidden layers and has a general architecture of 9-36-18-3. The outputs are the moduli for different layers. The inputs for the neural network are thicknesses and deflection values at seven distances from the load center. Next, the moduli of different asphalt pavement layers consisting of a surface course, base course, and subgrade were calculated using the Artificial Neural Network (ANN) methodology through backcalculation. The developed dataset contained the moduli values for different pavement sections, and deflections at known distances from the load center. To do so, a synthetic dataset consisting of 10,000 flexible pavements was created using the layered elastic theory. The primary objective of this research was to develop a model to accurately predict the modulus of flexible pavement layers from surface deflections measured using the falling weight deflectometer (FWD) device.

Pavement backcalculation