Modcomp backcalculation software6/11/2023 ![]() ![]() Results indicate that the ANN model can predict the modulus of different layers accurately with a coefficient of determination (R2) 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.
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