AI-Driven Structural Health Monitoring and Digital Twin Systems for Resilient Infrastructure

Aging infrastructure, climate change and extreme weather events further threaten the resiliency of our critical systems, making clear that traditional inspection methods are no longer sufficient. This paper describes an interconnected platform incorporating AI, SHM and DT methods to advance infrastructure resiliency and sustainability. Advanced sensors such as strain-gauges, accelerometers, optical fibers and acoustic emission devices make SHM systems capable of monitoring various modality data throughout. These diverse datasets are analyzed in machine learning algorithms such as Support Vector Machines (SVM), neural networks, clustering and autoencoders to enhance pattern recognition, anomaly detection and prediction. The data fusion methods by using Bayesian inference, ensemble learning and combinations of multimodal enhance diagnostic accuracy and reduce false positives. Real-time sensor data is used to synchronize digital twins and create virtual copies of physical assets, against which simulation, predictive maintenance or adaptive control can be carried out. The performance of the proposed ecosystem was accurate, sensitive and specific wherein those values exceeded traditional SHM methods. Case studies verified the advantages of multimodal fusion and digital twin techniques for early identification and predictive control. Finally, the study underscores AI-SHM and DT systems as enabling technologies that can transform infrastructure management from reactive to proactive, predictive, and sustainable levels.