Exploring technological innovations in employing structural health monitoring for glass

Abstract

The implementation of structural health monitoring (SHM) for glass structures is still at an early stage of development. Limited information can be found in existing literature regarding the use of SHM on glass elements. However, recent technological advancements have significantly improved our understanding in this area. Both SHM and the utilization of laminated glasses (LGs) for structural purposes are relatively new concepts with a short history. To conduct a comprehensive assessment of glass elements, it is crucial to have a solid understanding of both glass and SHM sciences. This research aims to explore various approaches for implementing SHM on structural glass elements, with a specific focus on the utilization of artificial intelligence (AI) and machine learning (ML) techniques. By harnessing the power of ML, the SHM system can analyze large amounts of data collected from sensors placed on glass structures. Furthermore, the application of AI in SHM can facilitate real-time monitoring, predictive maintenance, and risk assessment for glass structures. The findings emphasize the critical role of early detection in preventing damage to glass elements and demonstrate the effectiveness of SHM in achieving this objective. The use of AI features, such as ML, presents promising opportunities for advancing the capabilities of SHM in the structural glass domain. The incorporation of AI features, such as ML algorithms, could offer additional advantages in this field.