Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networks

• URL: http://arxiv.org/abs/2511.02957v1
• Date: Tue, 04 Nov 2025 19:59:17 GMT
• Title: Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networks
• Authors: Mohsin Mahmud Topu, Mahfuz Ahmed Anik, Azmine Toushik Wasi, Md Manjurul Ahsan,
• Abstract summary: We propose a unified Digital Twin (DT) and Graph Neural Network (GNN) framework for scalable, data-driven pavement health monitoring and predictive maintenance.<n>Our model achieves an R2 of 0.3798, outperforming baseline regressors and effectively capturing non-linear degradation.<n>This DT-GNN integration enhances forecasting precision and establishes a closed feedback loop for continuous improvement.
• Score: 5.876489372173655
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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