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Structural health monitoring (SHM) is an important and practical procedure for ensuring the structural integrity and serviceability of civil engineering structures such as bridges, buildings, and dams. Model-driven or data-driven strategies for structural response prediction are now widely combined with advances in engineering for use in SHM applications. Engineers have recently demonstrated increasing interest in using machine learning (ML) and artificial intelligence (AI) to achieve a variety of benefits and possibilities, notably for predicting structural reactions. This study serves as a comprehensive overview of the use of ML applications for structural response prediction in the context of SHM for civil engineering structures, with a particular focus on ML, deep learning (DL), and meta-heuristic algorithms. Accordingly, this study summarizes existing knowledge, presents concepts in a simple way, highlights trends, provides methodological insights, and provides a valuable resource for researchers, stakeholders, and decision-makers to benefit from. It is observed that the use of ML, DL, and meta-heuristic algorithms to predict the response of civil engineering structures within an acceptable accuracy range can be employed for SHM, resulting in improved speed, efficiency, and accuracy compared to conventional approaches. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of the Magnetorheological damper as a smart dissipating device in the isolated buildings for prevention of seismic pounding. To that end, an isolated building was modeled by considering the pounding effect with different gap distances. Multi-objective optimization was performed to optimize the fuzzy logic control of damper for each gap distance. The results showed that the optimized semi-active control system can prevent the pounding of the building and greatly improve the structure's behavior as compared to the isolated pounded building and even to the case without the pounding at some considered gap distances. [ABSTRACT FROM AUTHOR]