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

2025-11-04

AI Summary

Conventional pavement management systems suffer from passive responsiveness and inadequate modeling of spatial dependencies and nonlinear degradation patterns across road networks. Method: This paper proposes an active maintenance framework integrating digital twin technology with graph neural networks (GNNs). It constructs a dynamic graph representation of the road network by fusing multi-source data—including UAV remote sensing, LiDAR point clouds, and heterogeneous sensor streams—to explicitly model spatial correlations and forecast pavement deterioration. A reinforcement learning module optimizes maintenance policies within a closed-loop “perception–prediction–decision–feedback” architecture. Contribution/Results: Evaluated on a realistic synthetic dataset, the method achieves an R² of 0.3798—substantially outperforming traditional regression models—and effectively captures nonlinear degradation dynamics. An interactive, explainable visualization dashboard enables real-time simulation and intelligent decision support. The framework establishes a novel paradigm for sustainable, data-driven pavement health management.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Monitoring pavement health with complex spatial dependencies and environmental changes
• Overcoming reactive maintenance by enabling real-time predictive maintenance planning
• Modeling road networks as graphs to forecast non-linear pavement deterioration

Innovation

• Digital Twin framework for real-time pavement monitoring
• Graph Neural Networks model spatial deterioration patterns
• Reinforcement learning enables adaptive maintenance planning