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

TL;DR

This work introduces a graph-based Digital Twin (DT) framework for pavement health monitoring that leverages Graph Neural Networks (GNNs) to model spatiotemporal deterioration across road networks. By streaming real-time data from UAVs, LiDAR, sensors, and historical records into a unified DT and coupling it with inductive GNNs, the approach delivers proactive maintenance insights and what-if simulations within an interactive visualization environment. Empirical results on a real-world-inspired dataset show the GNN achieving an R^2 of $0.3798$, outperforming traditional regressors while enabling a closed-loop decision process for adaptive maintenance and lifecycle optimization. The framework advances pavement management toward proactive, data-driven, and sustainable urban infrastructure, with future extensions toward reinforcement learning-based scheduling, multi-agent coordination, and smart-city integration.

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.

Figures (11)

• Figure 1: Theoretical GNN-Based DT Framework for Pavement Health Monitoring
• Figure 2: The Architecture of Graph Neural Network for Pavement Deterioration Prediction
• Figure 3: Detailed framework of graph-based Digital Twins for pavement health monitoring and maintenance.
• Figure 4: Schematic Representation of Data Integration Layer Processes and Components
• Figure 5: Workflow and Components of Graph Construction Layer