{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T14:55:38Z","timestamp":1771685738257,"version":"3.50.1"},"reference-count":79,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["LA\/P\/0069\/2020"],"award-info":[{"award-number":["LA\/P\/0069\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["UID\/00350\/2020"],"award-info":[{"award-number":["UID\/00350\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["LA\/P\/0069\/2020"],"award-info":[{"award-number":["LA\/P\/0069\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["UID\/00350\/2020"],"award-info":[{"award-number":["UID\/00350\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Buildings"],"abstract":"<jats:p>After an earthquake, rapid assessment of building damage is crucial for emergency response, reconstruction planning, and public safety. This study evaluates the performance of various Generative Artificial Intelligence (GAI) models in analyzing post-earthquake images to classify structural damage according to the EMS-98 scale, ranging from minor damage to total destruction. Correct classification rates for masonry buildings varied from 28.6% to 64.3%, with mean damage grade errors between 0.50 and 0.79, while for reinforced concrete buildings, rates ranged from 37.5% to 75.0%, with errors between 0.50 and 0.88. Fine-tuning these models could substantially improve accuracy. The practical implications are significant: integrating accurate GAI models into disaster response protocols can drastically reduce the time and resources required for damage assessment compared to traditional methods. This acceleration enables emergency services to make faster, data-driven decisions, optimize resource allocation, and potentially save lives. Furthermore, the widespread adoption of GAI models can enhance resilience planning by providing valuable data for future infrastructure improvements. The results of this work demonstrate the promise of GAI models for rapid, automated, and precise damage evaluation, underscoring their potential as invaluable tools for engineers, policymakers, and emergency responders in post-earthquake scenarios.<\/jats:p>","DOI":"10.3390\/buildings14103255","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T12:44:31Z","timestamp":1728909871000},"page":"3255","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Effectiveness of Generative AI for Post-Earthquake Damage Assessment"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7356-9893","authenticated-orcid":false,"given":"Jo\u00e3o M. C.","family":"Est\u00eav\u00e3o","sequence":"first","affiliation":[{"name":"ISE\u2014University of Algarve (UAlg), Campus da Penha, 8005-139 Faro, Portugal"},{"name":"CIMA\u2014Centre for Marine and Environmental Research, ARNET\u2014Infrastructure Network in Aquatic Research, UAlg, Campus de Gambelas, 8005-139 Faro, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"key":"ref_1","unstructured":"PSBD (2023). 2023 Kahramanmara\u015f and Hatay Earthquakes Report, Presidential Strategy and Budget Directorate."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107857","DOI":"10.1016\/j.engfailanal.2023.107857","article-title":"Investigating the structural damage in Hatay province after Kahramanmara\u015f-T\u00fcrkiye earthquake sequences","volume":"157","author":"Altunsu","year":"2024","journal-title":"Eng. Fail. 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