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

arXiv: https://arxiv.org/abs/2511.02957 · HTML: https://mlsanity.com/paper/2511.02957/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 189 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Builds on (most cited first, 25 of 25)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Time Waits for No One! Analysis and Challenges of Temporal Misalignment](https://mlsanity.com/paper/2111.07408/) (2021)
- [Digital Twin: Values, Challenges and Enablers](https://mlsanity.com/paper/1910.01719/) (2019)
- [Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities](https://mlsanity.com/paper/2302.01018/) (2023)
- [Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis](https://mlsanity.com/paper/2311.00164/) (2023)
- [STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph](https://mlsanity.com/paper/2210.16789/) (2022)
- [A digital twin framework for civil engineering structures](https://mlsanity.com/paper/2308.01445/) (2023)
- [Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation](https://mlsanity.com/paper/2404.11858/) (2024)
- [Spatial-Temporal Bearing Fault Detection Using Graph Attention Networks and LSTM](https://mlsanity.com/paper/2410.11923/) (2024)
- [Enabling Spatial Digital Twins: Technologies, Challenges, and Future Research Directions](https://mlsanity.com/paper/2306.06600/) (2023)
- [DefectTwin: When LLM Meets Digital Twin for Railway Defect Inspection](https://mlsanity.com/paper/2409.06725/) (2024)
- [A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends](https://mlsanity.com/paper/2409.19629/) (2024)
- [Architecting Digital Twins for Intelligent Transportation Systems](https://mlsanity.com/paper/2502.17646/) (2025)
- [Sparsity-Aware Communication for Distributed Graph Neural Network Training](https://mlsanity.com/paper/2504.04673/) (2025)
- [Considering Spatial Structure of the Road Network in Pavement Deterioration Modeling](https://mlsanity.com/paper/2508.02749/) (2025)
- [Digital Twin-based Driver Risk-Aware Intelligent Mobility Analytics for Urban Transportation Management](https://mlsanity.com/paper/2407.15025/) (2024)
- [Dynamic Portfolio Rebalancing: A Hybrid new Model Using GNNs and Pathfinding for Cost Efficiency](https://mlsanity.com/paper/2410.01864/) (2024)
- [Predictive Digital Twin for Condition Monitoring Using Thermal Imaging](https://mlsanity.com/paper/2411.05887/) (2024)
- [STGAN: Spatial-temporal Graph Autoregression Network for Pavement Distress Deterioration Prediction](https://mlsanity.com/paper/2503.01152/) (2025)
- [A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization](https://mlsanity.com/paper/2504.03692/) (2025)
- [GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks](https://mlsanity.com/paper/2403.15077/) (2024)

## Built on by (most cited first, 2 of 2)

- [ST-ResGAT: Explainable Spatio-Temporal Graph Neural Network for Road Condition Prediction and Priority-Driven Maintenance](https://mlsanity.com/paper/2603.14107/) (2026)
- [A Digital Twin Framework for Traffic-Aware UAV Pavement Monitoring in Open-Traffic Conditions](https://mlsanity.com/paper/2606.20742/) (2026)

## Abstract (arXiv)

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.