State-of-the-art techniques for pavement performance evaluation have attracted considerable attention in recent years. Artificial Neural Networks (ANNs) can simulate the human brain to discover hidden patterns within datasets, thereby enhancing the accuracy of performance evaluations in civil engineering. Routine backcalculation of layer properties from Falling Weight Deflectometer (FWD) deflection time histories provide critical information for asphalt pavement performance assessment. This study proposes a general framework that integrates the ANN with swarm optimization algorithms for asphalt pavement layer property backcalculation at the project level. The proposed procedure employed the spectral element method (SEM) to calculate the deflection time histories, explicitly accounting for the dynamic effect of FWD loading and the viscoelastic property of asphalt concrete (AC). The geometrical characteristic of the load time history and the load-deflection hysteresis curve were extracted to construct the training dataset for the ANN model. Three swarm optimization algorithms were utilized to determine the initial weights and biases of the ANN. Subsequently, the parameters of the Williams-Landel-Ferry (WLF) function were optimized using the field temperatures and the backcalculated layer property to characterize the temperature-dependent behaviour of AC. A well agreement between the backcalculated and measured deflection time histories, together with the consistency of the backcalculated layer properties within reasonable ranges, demonstrated the feasibility of the proposed procedure. In contrast to conventional backpropagation neural networks (BPNNs), whose built-in optimization schemes tend to become trapped in local optima and may yield unreasonable layer properties, swarm optimization algorithms expand the candidate solution space and facilitate global optimization. The standard deviation (STD) and coefficient of variation (COV) obtained from ANNs enhanced with swarm optimization are significantly lower than those from BPNNs; the maximum reduction in COV for ANN + HPO (Hunter–Prey optimization) relative to BPNN exceeds 50%. The ANN + MA (Mayfly Algorithm) and ANN + HPO exhibit superior performance and greater computational efficiency for layer property backcalculation compared with a conventional ANN model.
Wang et al. (Wed,) studied this question.