Advancing asphalt pavement monitoring and prognostics with physics-informed digital twins: a feasibility study
- Publisher:
- Taylor & Francis
- Publication Type:
- Journal Article
- Citation:
- International Journal of Pavement Engineering, 2025, 26, (1), pp. 2566277
- Issue Date:
- 2025-01-01
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Asphalt pavements experience progressive deterioration from repeated traffic loads and environmental exposure, leading to premature failure and increased maintenance costs. Traditional monitoring methods fail to provide real-time insights into pavement health, limiting proactive maintenance strategies. This study establishes a framework for the development of a digital twin-enabled cyber-physical platform for the monitoring of asphalt pavements by leveraging the potential of modern sensors and physics engine software. The study combines embedded ‘smart rock’ sensors capable of capturing real-time mechanical responses of asphalt under varying loads and temperatures with physics informed virtual asphalt models developed using physics engines. Laboratory validation established strong agreement between the virtual model simulations and real asphalt performance data. This alignment provides a foundational breakthrough to correlate changes in mechanical behaviour with emerging distress patterns, enabling early damage detection and failure prediction. Overall, sensor-virtual model fusion can potentially enable proactive, self-updating road performance prediction via cyber-physical systems.
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