Machine learning in coastal bridge hydrodynamics: A state-of-the-art review. (May 2023) - Xu, Guoji; Ji, Chengjie; Xu, Yong; Yu, Enbo; Cao, Zhiyang; Wu, Qinghong; Lin, Pengzhi; Wang, Jinsheng
Abstract: Coastal bridges are vulnerable to complicated hydrodynamics induced by hostile natural hazards, relevant research is thus required to ensure the safe operation of these critical infrastructure assets. Although coastal bridge hydrodynamic analyses can be carried out through commonly used approaches such as theoretical studies, numerical simulations, and experimental tests, they cannot fulfill the ever-increasing demand for applicability and computational efficiency. The recently advanced machine learning (ML) has emerged as a disruptive technology that fully revolutionized various scientific disciplines, resulting in a new paradigm to meet modern research needs. Aiming to provide the research community with holistic information on the key ingredients and current state-of-the-art of applying ML algorithms to coastal bridge hydrodynamics, this study presents a comprehensive review of the deployment of ML in coastal bridge hydrodynamics. The theoretical backgrounds of some representative ML algorithms are briefly introduced, and applications of ML to each of the research themes associated with coastal bridge hydrodynamics are systematically surveyed. Future research directions are also highlighted through the discussion of the current research limitations. According to this review, it is evident that ML models can be trained to learn and infer the intricate relationships between contributing parameters and responses of interest in coastal bridge hydrodynamics. In addition, it is envisioned that the research in coastal bridge hydrodynamics could be further advanced with the evolving ML technologies.