Prediction of void beneath concrete slabs based on FEM-ANN framework

Abstract

The voids beneath cement concrete slabs are a major invisible disease, resulting in a rapid decrease in service performance in the composite pavement. Accurate voids prediction is essential for the extensive application and long-term service of composite pavement. This research provides a FEM-ANN (Finite Element Modeling-Artificial Neural Network) method to predict the voids beneath concrete slabs. These ANN models include the original back propagation (BP), the particle swarm optimization (PSO) BP model, the genetic algorithm (GA) BP model, and the whale optimization algorithm (WOA) BP model. The voids FEM model is established and validated by the measured data in the field, and the relative error of measured and simulated results is within 4%. The cross-validation results show that the WOA-BP model has the best prediction performance, with the highest score of 8. Therefore, this FEM-ANN framework is an efficient method for estimating the voids beneath concrete slabs.