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arXiv · 2511.02957

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

Mohsin Mahmud Topu·Mahfuz Ahmed Anik·Azmine Toushik Wasi·Md Manjurul Ahsan
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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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Mohsin Mahmud Topu, Mahfuz Ahmed Anik, Azmine Toushik Wasi, Md Manjurul Ahsan. 2025-11-04. Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networks. https://arxiv.org/abs/2511.02957

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