{"ID":2846385,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.02957","arxiv_id":"2511.02957","title":"Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networks","abstract":"Pavement infrastructure monitoring is challenged by complex spatial dependencies, changing environmental conditions, and non-linear deterioration across road networks. Traditional Pavement Management Systems (PMS) remain largely reactive, lacking real-time intelligence for failure prevention and optimal maintenance planning. To address this, we propose a unified Digital Twin (DT) and Graph Neural Network (GNN) framework for scalable, data-driven pavement health monitoring and predictive maintenance. Pavement segments and spatial relations are modeled as graph nodes and edges, while real-time UAV, sensor, and LiDAR data stream into the DT. The inductive GNN learns deterioration patterns from graph-structured inputs to forecast distress and enable proactive interventions. Trained on a real-world-inspired dataset with segment attributes and dynamic connectivity, our model achieves an R2 of 0.3798, outperforming baseline regressors and effectively capturing non-linear degradation. We also develop an interactive dashboard and reinforcement learning module for simulation, visualization, and adaptive maintenance planning. This DT-GNN integration enhances forecasting precision and establishes a closed feedback loop for continuous improvement, positioning the approach as a foundation for proactive, intelligent, and sustainable pavement management, with future extensions toward real-world deployment, multi-agent coordination, and smart-city integration.","short_abstract":"Pavement infrastructure monitoring is challenged by complex spatial dependencies, changing environmental conditions, and non-linear deterioration across road networks. Traditional Pavement Management Systems (PMS) remain largely reactive, lacking real-time intelligence for failure prevention and optimal maintenance pla...","url_abs":"https://arxiv.org/abs/2511.02957","url_pdf":"https://arxiv.org/pdf/2511.02957v1","authors":"[\"Mohsin Mahmud Topu\",\"Mahfuz Ahmed Anik\",\"Azmine Toushik Wasi\",\"Md Manjurul Ahsan\"]","published":"2025-11-04T19:59:17Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CE\",\"cs.ET\",\"cs.NE\",\"eess.SY\"]","methods":"[\"Graph Neural Network\",\"Reinforcement Learning\"]","has_code":false}