Details
| Original language | English |
|---|---|
| Article number | 120721 |
| Journal | Engineering structures |
| Volume | 340 |
| Early online date | 5 Jun 2025 |
| Publication status | E-pub ahead of print - 5 Jun 2025 |
Abstract
Applying machine learning (ML) in earthquake engineering has introduced new opportunities for better predicting, evaluating, and mitigating structural damage under seismic hazards. The rapid advancement of ML in earthquake engineering necessitates a thorough understanding of its potential and limitations to guide future research and practical applications effectively. This literature review focuses on the recent advancements of ML in structural seismic performance evaluation and design optimization. This paper comprehensively explores recent trends and innovations for each area, highlights ongoing challenges, and suggests future directions involving emerging technologies. Key findings reveal significant progress in ML methodologies. Still, challenges related to the accurate prediction of nonlinear hysteretic responses, the need for improved generalizability of ML models, the scarcity of high-quality data, effective feature selection techniques, and regional scale investigations remain. Moreover, the future research needs and strategies for addressing these challenges are presented.
Keywords
- Earthquake engineering, Feature selection, Generalizability, Machine learning, Seismic design optimization, Seismic performance
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
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In: Engineering structures, Vol. 340, 120721, 01.10.2025.
Research output: Contribution to journal › Review article › Research › peer review
}
TY - JOUR
T1 - Machine learning in earthquake engineering
T2 - A review on recent progress and future trends in seismic performance evaluation and design
AU - Hu, Shuling
AU - Guo, Tong
AU - Alam, M. Shahria
AU - Koetaka, Yuji
AU - Ghafoori, Elyas
AU - Karavasilis, Theodoros L.
N1 - Publisher Copyright: © 2025 The Authors
PY - 2025/6/5
Y1 - 2025/6/5
N2 - Applying machine learning (ML) in earthquake engineering has introduced new opportunities for better predicting, evaluating, and mitigating structural damage under seismic hazards. The rapid advancement of ML in earthquake engineering necessitates a thorough understanding of its potential and limitations to guide future research and practical applications effectively. This literature review focuses on the recent advancements of ML in structural seismic performance evaluation and design optimization. This paper comprehensively explores recent trends and innovations for each area, highlights ongoing challenges, and suggests future directions involving emerging technologies. Key findings reveal significant progress in ML methodologies. Still, challenges related to the accurate prediction of nonlinear hysteretic responses, the need for improved generalizability of ML models, the scarcity of high-quality data, effective feature selection techniques, and regional scale investigations remain. Moreover, the future research needs and strategies for addressing these challenges are presented.
AB - Applying machine learning (ML) in earthquake engineering has introduced new opportunities for better predicting, evaluating, and mitigating structural damage under seismic hazards. The rapid advancement of ML in earthquake engineering necessitates a thorough understanding of its potential and limitations to guide future research and practical applications effectively. This literature review focuses on the recent advancements of ML in structural seismic performance evaluation and design optimization. This paper comprehensively explores recent trends and innovations for each area, highlights ongoing challenges, and suggests future directions involving emerging technologies. Key findings reveal significant progress in ML methodologies. Still, challenges related to the accurate prediction of nonlinear hysteretic responses, the need for improved generalizability of ML models, the scarcity of high-quality data, effective feature selection techniques, and regional scale investigations remain. Moreover, the future research needs and strategies for addressing these challenges are presented.
KW - Earthquake engineering
KW - Feature selection
KW - Generalizability
KW - Machine learning
KW - Seismic design optimization
KW - Seismic performance
UR - http://www.scopus.com/inward/record.url?scp=105007143016&partnerID=8YFLogxK
U2 - 10.1016/j.engstruct.2025.120721
DO - 10.1016/j.engstruct.2025.120721
M3 - Review article
AN - SCOPUS:105007143016
VL - 340
JO - Engineering structures
JF - Engineering structures
SN - 0141-0296
M1 - 120721
ER -