Integrating Machine Learning in Finite Element Analysis for Structural Health Monitoring

The possibility of incorporating machine literacy techniques into finite element analysis (FEA) for structural health monitoring (SHM) activities. Monitoring the structural health of engineering structures, such as islands, structures, and aerospace elements, is an essential component in ensuring the safety, dependability, and longevity of these structures. When it comes to dealing with complicated and dynamic structural actions, traditional SHM methods that are based on drug-based models and detector data have limits. In the case of discrepancy, machine literacy algorithms provide the power to learn patterns and connections directly from data, which enables SHM systems to be more accurate and adaptable. The purpose of this research is to investigate the objectification of machine literacy models into FEA-based SHM fabrics. These models include supervised literacy, unsupervised literacy, and underlying literacy. The approach that has been developed has the objective of enhancing the prophetic powers of SHM systems by utilizing the complementary strengths of data-driven algorithms and drug-based modelling. The effectiveness and implicit benefits of incorporating machine literacy in finite element analysis (FEA) for structural health monitoring (SHM) are illustrated through case studies and performance evaluation. This sets the stage for the development of more advanced and intelligent structural monitoring systems.