Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networks
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917060953505792 |
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| author | Topu, Mohsin Mahmud Anik, Mahfuz Ahmed Wasi, Azmine Toushik Ahsan, Md Manjurul |
| author_facet | Topu, Mohsin Mahmud Anik, Mahfuz Ahmed Wasi, Azmine Toushik Ahsan, Md Manjurul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02957 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networks Topu, Mohsin Mahmud Anik, Mahfuz Ahmed Wasi, Azmine Toushik Ahsan, Md Manjurul Machine Learning Computational Engineering, Finance, and Science Emerging Technologies Neural and Evolutionary Computing Systems and Control 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. |
| title | Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networks |
| topic | Machine Learning Computational Engineering, Finance, and Science Emerging Technologies Neural and Evolutionary Computing Systems and Control |
| url | https://arxiv.org/abs/2511.02957 |