A Critical Review of IoT-Based Structural Health Monitoring for Dams

Structural health monitoring (SHM) is a modern and innovative method used to assess the integrity, safety, and performance of structures without causing any damage. The need for SHM is particularly critical for infrastructure, such as dams, where timely detection of damage allows for the implementation of remedial safety measures. Structural integrity can be compromised by events, such as earthquakes, floods, and other disasters, which create significant disturbances in man-made constructions. The longevity of a structure is influenced by construction methods and the quality of materials used. Researchers have developed various techniques to evaluate the lifespan of constructed structures and propose safety measures. Recent advancements in technology, including the Internet of Things (IoT), artificial intelligence (AI), and wireless integrated sensor devices, have revolutionized the field of SHM. IoT-based wireless sensors monitor different structural parameters and transmit data to cloud-based storage systems in real-time. AI techniques, particularly machine learning, analyze this data to predict potential issues and provide actionable insights to user agencies, researchers, and administrative bodies, such as Dam Monitoring Centers (DMCs). The ability of these technologies to monitor dam behavior in real-time has garnered significant attention. This study reviews current research on real-time SHM of dams using AI and IoT cloud-based sensor techniques, aiming to support researchers and practitioners in advancing SHM methodologies.