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

TL;DR

This paper introduces a digital twin and graph neural network framework for real-time pavement health monitoring and predictive maintenance, improving accuracy and enabling proactive infrastructure management.

Contribution

It presents a novel integration of digital twins and GNNs for scalable, data-driven pavement monitoring and maintenance optimization, with real-world-inspired validation.

Findings

Achieved an R2 of 0.3798 in deterioration prediction

Outperformed baseline regressors in forecasting accuracy

Developed an interactive dashboard and reinforcement learning module

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.