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

Document Type

Article

Publication Date

3-1-2026

Abstract

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.

Publication Title

Results in Engineering

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