Explainable AI for predicting pavement roughness under maintenance and no-maintenance scenarios

Accurate forecasting of pavement conditions is fundamental to supporting data-driven infrastructure investment decisions and optimizing maintenance strategies. Although a substantial body of research has applied AI techniques to predict future pavement performance, there remains a critical gap in developing models that account for the comparing effects of maintenance interventions and no-maintenance scenarios. Analyzing pavement conditions under both maintenance and no-maintenance scenarios provides critical insights for network-level pavement asset management. This study conducts a comparative analysis of Artificial Neural Networks, Random Forest, XGBoost, and CatBoost models for predicting the International Roughness Index (IRI) over 2- and 3-year horizons using Highway Performance Monitoring System datasets. The models were optimized through Particle Swarm Optimization and grid search, applying parameter ranges recommended in previous pavement condition prediction studies. A key finding is that all models achieved higher performance under no-maintenance scenarios compared with maintenance- interventions datasets. Moreover, SHAP analysis revealed that previous pavement roughness is the dominant predictor of future IRI under maintenance scenarios, with traffic and structural features gaining importance over longer horizons.