Recent Updates on Advances in Earthquake Hazard, Vulnerability and Risk Assessment
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".
Deadline for manuscript submissions: 20 August 2025 | Viewed by 139
Special Issue Editors
Interests: seismic groud motion modeling; ground motion prediction; seismic hazard and seismic risck assessment
Special Issues, Collections and Topics in MDPI journals
Interests: numerical modelling of ground motion records; probabilistic and deterministic seismic hazard analysis; nonlinear time history analysis; seismic vulnerability and risk analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The use of artificial intelligence (AI) and Machine Learning (ML) methods is rapidly growing, with applications across various fields of earthquake engineering, including the development of ground motion models, forecasting the seismic behavior of structures, and assessing potential damage and risk. However, the lack of high-quality databases remains a significant obstacle to the applications of ML methodologies in this field. A promising future trend involves obtaining reliable and accurate databases through experimental or numerical simulation methods to train ML models. Hybrid analysis methods, although challenging, represent a key area of ongoing development.
This Special Issue invites research on the use of big data, AI, and ML in earthquake engineering, focusing on the latest advancements in areas such as data analysis, ground motion models, ground motion simulations, earthquake prediction, soil–structure interactions, hazard, vulnerability, and risk assessment, as well as the development of strengthening and retrofitting strategies. These contributions will play a key role in shaping the future of earthquake engineering, particularly in advancing disaster mitigation efforts and building more resilient urban systems.
Dr. Alexandra Carvalho
Dr. Shaghayegh Karimzadeh
Guest Editors
Manuscript Submission Information
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Keywords
- earthquake engineering
- seismic hazard
- seismic vulnerability
- seismic risk
- machine learning
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