AI-Supported Seismic Performance Evaluation of Structures: Challenges, Gaps, and Future Directions at Early Design Stages
| dc.contributor.author | Ak, Fatma | |
| dc.contributor.author | Ekici, Berk | |
| dc.contributor.author | Demir, Ugur | |
| dc.date.accessioned | 2026-02-25T14:59:32Z | |
| dc.date.available | 2026-02-25T14:59:32Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | This study reviews 91 journal articles that intersect with earthquake-resistant building design and artificial intelligence (AI)- based modeling, utilizing machine learning, deep learning, and metaheuristic optimization algorithms. Previous reviews on AI applications have examined engineering problems without considering the impact of architectural design parameters and structural irregularities on seismic performance. This review discusses the role of AI in integrating architectural design variables and seismic performance objectives, highlighting challenges, gaps, and future directions in the early design phase. The reviewed articles demonstrate that AI is successful in addressing seismic performance objectives; however, a holistic framework for assessing architectural and structural variables has not been presented. The review highlights key findings, gaps, and future directions for those involved in earthquake-resistant building design utilizing AI. | en_US |
| dc.description.sponsorship | Izmir Institute of Technology Scientific Research Department (BAP) [2024IYTE-1-0013, 2024IYTE-2-0019]; Scientific and Technological Research Council of Turkiye (TUBITAK) | en_US |
| dc.description.sponsorship | The authors gratefully acknowledge the financial support provided by Izmir Institute of Technology Scientific Research Department (BAP) (Project No: 2024IYTE-1-0013 and Project No: 2024IYTE-2-0019) . Also, the first author acknowledges with appreciation the financial support reveived from The Scientific and Technological Research Council of Turkiye (TUBITAK) for '2210-C Priority Fields Master's Scholarship'. We would like to thank these institutions for providing support that played a crucial role in the successful completion of the research presented in this study. | en_US |
| dc.identifier.doi | 10.1016/j.aei.2025.104301 | |
| dc.identifier.issn | 1474-0346 | |
| dc.identifier.issn | 1873-5320 | |
| dc.identifier.scopus | 2-s2.0-105028991757 | |
| dc.identifier.uri | https://doi.org/10.1016/j.aei.2025.104301 | |
| dc.identifier.uri | https://hdl.handle.net/11147/18935 | |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier Sci Ltd | en_US |
| dc.relation.ispartof | Advanced Engineering Informatics | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Computational Design | en_US |
| dc.subject | Earthquake | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Optimization | en_US |
| dc.subject | Seismic Performance | en_US |
| dc.title | AI-Supported Seismic Performance Evaluation of Structures: Challenges, Gaps, and Future Directions at Early Design Stages | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication | |
| gdc.author.scopusid | 60362534500 | |
| gdc.author.scopusid | 57188803559 | |
| gdc.author.scopusid | 56490432600 | |
| gdc.description.department | İzmir Institute of Technology | en_US |
| gdc.description.departmenttemp | [Ak, Fatma; Ekici, Berk; Demir, Ugur] Izmir Inst Technol, Dept Architecture, Gulbahce Campus, TR-35430 Izmir, Turkiye | en_US |
| gdc.description.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | Q1 | |
| gdc.description.volume | 71 | en_US |
| gdc.description.woscitationindex | Science Citation Index Expanded | |
| gdc.description.wosquality | Q1 | |
| gdc.identifier.wos | WOS:001681683700001 | |
| gdc.index.type | WoS | |
| gdc.index.type | Scopus | |
| relation.isAuthorOfPublication.latestForDiscovery | f3bdb7d9-e8f8-4f53-9381-96c3b2b892d3 | |
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