Surrogate modelling to enable structural assessment of collision between vertical concrete dry casks

Sichani, Majid Ebad; Padgett, Jamie E.

doi:10.1080/15732479.2019.1618878

Cited by 6 publications

(1 citation statement)

References 30 publications

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“…Second, an ensemble of different ML algorithms, or similar algorithms with different values for their internal parameters, should be examined to create a more robust overall model (Butler et al, 2018). Recent works in earthquake engineering have embraced this trend to use multiple ML algorithms in solving the same problem, where the algorithm with the best performance was identified (Luo and Paal, 2019; Mangalathu and Jeon, 2018; Sichani, 2018; Thomas et al, 2017). Third, physical understanding of a problem can be increasingly used to design and learn ML models.…”

Section: Discussionmentioning

confidence: 99%

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

et al. 2020

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Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional approaches, ML offers advantages to handle complex problems, provide computational efficiency, propagate and treat uncertainties, and facilitate decision making. Also, the maturing of ML has led to significant advances in not only the main-stream artificial intelligence (AI) research but also other science and engineering fields, such as material science, bioengineering, construction management, and transportation engineering. This study conducts a comprehensive review of the progress and challenges of implementing ML in the earthquake engineering domain. A hierarchical attribute matrix is adopted to categorize the existing literature based on four traits identified in the field, such as ML method, topic area, data resource, and scale of analysis. The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation. Moreover, research challenges and the associated future research needs are discussed, which include embracing the next generation of data sharing and sensor technologies, implementing more advanced ML techniques, and developing physics-guided ML models.

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