Integrating Artificial Intelligence in Structural Health Monitoring: A Path Toward Climate Resilient Infrastructure

Abstract

This paper reviews the evolution and advancements in structural health monitoring(SHM). The traditional methods relied on sensor-based measurements and statistical modeling for anomaly detection. The introduction of wireless smart sensor networks improved scalability and deployment. Recent changes in technologies, with the integration of Artificial Intelligence (AI), Machine Learning (ML), and IoT, have enabled real-time monitoring to be more productive and predictive. Core techniques include vibration analysis, fibre optics, non-destructive testing, and image-based methods.AI models such as Random forests, CNNs, RNNs, and SVMs enhance fault detection and damage classification. Applications extend to bridges, buildings, aerospace, offshore platforms, and smart cities. SHM benefits include improved safety, automation, and scalability, reduced cost, etc. Key challenges remain in data heterogeneity, sensor reliability, and model interpretability. Future research emphasizes hybrid AI-IoT frameworks and sustainable solutions for resilient infrastructure