Physics-informed neural network with fuzzy partial differential equation for pavement performance prediction

Abstract

The prediction of pavement deterioration is critical for road maintenance and construction. A thorough understanding of road deterioration mechanisms can enhance the effectiveness of maintenance efforts and prevent further degradation. In this paper, a physics-informed neural network (PINN) was developed to incorporate insights from both big data and the macroscopic deterioration behavior of pavements. A fuzzy partial differential equation (FPDE) was employed as the representative constraint equation based on pavement fatigue cracking theory. Ten years of deterioration data were collected from a selected highway in China to validate the theoretical and practical aspects of the proposed method. The results indicate that the PINN model achieves superior physical consistency, with the prediction accuracy improving by 20.9 % and 11.4 % compared to the BPNN and XGBoost models, respectively. This study introduces a method that aligns data consistency with physical laws and enhances the interpretability of pavement deterioration.