A Review on Technological Advancements in the Field of Data Driven Structural Health Monitoring

Recent advancements in sensor technology, as well as fast progress in internet-based cloud computation; data-driven approaches in structural health monitoring (SHM) are gaining prominence. The majority of time is utilized for reviewing & analyzing the data received from various sensors deployed in structures. This data analysis helps in understating the structural stability and its current state with certain limitations. Considering this fact, integration with Machine Learning (ML) in SHM has attracted significant attention among researchers. This paper is principally aimed at understanding and reviewing of vast literature available in sensor-based data-driven approaches using ML. The implementation and methodology of vibration-based, vision-based monitoring, along with some of the ML algorithms used for SHM are discussed. Nevertheless, a perspective on the importance of data-driven SHM in the future is also presented. Conclusions are drawn from the review discuss the prospects and potential limitations of ML approaches in data-driven SHM applications.