Predicting bedrock depth under asphalt pavement through a data-driven method based on particle swarm optimization-back propagation neural network

Abstract

Performance evaluations during and after asphalt pavement construction are essential to ensure long-term and safe operations. These evaluations are performed by obtaining each layer’s modulus of the pavement structure by back-calculating the deflection response. However, bedrock under the pavement can seriously affect this analysis. To limit this effect, this paper proposes predicting the bedrock depth by utilizing a data-driven machine learning algorithm based on particle swarm optimization-back propagation neural network (PSO-BPNN) aided by falling weight deflectometer (FWD) tests. Additionally, to provide sufficient data for the PSO-BPNN, a spectral element method with fixed-end boundary conditions (B-SEM) is utilized to calculate the theoretical deflections of multiple pavements at different bedrock depths. After performing a sensitivity analysis, the peak deflections at different measurement points and three time points corresponding to the peak deflection and the two subsequent fluctuation peaks at the load center measurement point are extracted as the input of the PSO-BPNN prediction model. Due to the pervasiveness of measurement errors in field tests, the raw input data are processed by adding random and systematic errors, and the corresponding bedrock depths are taken as the output dataset. The results demonstrate that the PSO-BPNN model is effective and feasible in predicting bedrock depth. Furthermore, in contrast to existing estimation models, the PSO-BPNN prediction model is more accurate and reliable with broad application prospects in road engineering.