Prediction of Asphalt Pavement Service Performance Based on a PSO-LSTM Model

Under the constraints of limited maintenance budgets, research on pavement performance prediction is of considerable practical significance for improving the efficiency of maintenance decision-making, optimizing fund allocation, and ensuring highway serviceability. This study was conducted in conjunction with pavement maintenance projects on ordinary national and provincial highways in Shanxi Province, China. Representative routes were selected based on the natural zoning and road network scale of Linfen City. A comprehensive factor system influencing pavement service performance was established, encompassing pavement characteristics, traffic attributes, climatic and environmental conditions, and maintenance management levels. By employing Particle Swarm Optimization (PSO) to tune the hyperparameters of a Long Short-Term Memory (LSTM) network, a PSO-LSTM prediction model incorporating a sliding window mechanism was constructed for the Pavement Condition Index (PCI) and Ride Quality Index (RQI). The model achieves coefficients of determination (R2) of 0.845 and 0.869 for PCI and RQI, respectively, enabling dynamic prediction of pavement service performance and thereby providing scientific support and a data-driven basis for the formulation of pavement maintenance strategies.