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

By Yazhou Xie, Majid Ebad Sichani, Jamie E. Padgett, Reginald DesRoches. Earthquake spectra, 2020

443 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Geophysics

View Paper: https://www.semanticscholar.org/paper/8702e8a17eed574c314d77c19a0ff2c2f26fdbb3

Citations in the corpus