Machine Learning Applications in Earthquake Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".
Deadline for manuscript submissions: 30 December 2025 | Viewed by 237
Special Issue Editors
Interests: machine learning in earthquake engineering; seismic risk assessment; FEM and nonlinear analysis of reinforced concrete and masonry structures; seismic retrofit with FRPs
Interests: civil engineering; masonry structures; retrofitting of structures; assessment of structures; vulnerability of structures
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Special Issue Information
Dear Colleagues,
Machine Learning is expected to significantly advance earthquake engineering research and practice. Currently, there are two main approaches in this field: physics-based methods, which are transparent, interpretable, and somewhat predictable, and data-driven Machine Learning models, which are unique and can be difficult to interpret. Consequently, there is a growing trend toward finding a balance between these approaches. Since the lack of physical interpretation in Machine Learning models can limit their applicability, integrating physical research into Machine Learning-based earthquake engineering studies is essential. Despite the increasing number of studies, the application of Machine Learning to earthquake engineering is still in its early stages compared to other disciplines. However, with the support of next-generation data sharing and sensor technologies, Machine Learning holds great potential to revolutionize earthquake engineering. It has been applied in four key areas: seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation. The literature identifies seven classes of Machine Learning methods: artificial neural networks, support vector machines, response surface models, logistic regression, decision trees, random forests, and hybrid methods, which combine multiple soft computing algorithms, such as fuzzy logic. This Special Issue invites contributions on all these topic areas, as well as on Machine Learning methods in earthquake engineering.
Dr. Konstantinos G. Megalooikonomou
Dr. Leonidas Alexandros S. Kouris
Guest Editors
Manuscript Submission Information
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Keywords
- earthquake engineering
- structural dynamics
- seismic risk assessment
- seismic hazard analysis
- machine learning
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