{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T19:38:45Z","timestamp":1781033925163,"version":"3.54.1"},"reference-count":58,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T00:00:00Z","timestamp":1675641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71971147"],"award-info":[{"award-number":["71971147"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Earthquakes pose a significant threat to infrastructure systems. However, improving the seismic resilience of infrastructure systems in earthquake-prone regions is fraught with obstacles. First, this article reviews the current status of earthquake resilience research, points out the gaps of existing research, and then focuses on the adaptability in resilience. Secondly, five groups of influencing factors of infrastructure system adaptability are identified and clustered through literature review and expert knowledge. Thirdly, the structure and conditional probability table of the Bayesian network model are given in detail, and the evaluation model of Bayesian network adaptability is created. A Chinese earthquake-prone county was used to verify the applicability of the model. The research uses forward propagation analysis to calculate the adaptability of the case and obtains the probability of the case\u2019s adaptability. The backward propagation to obtain the ranking of the influence degree of the critical influencing factors on the adaptability and the top three factors are respectively earthquake history, relevant information and contingency mechanisms. Finally, the research suggests measures to improve adaptability.<\/jats:p>","DOI":"10.3390\/systems11020084","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T06:22:25Z","timestamp":1675664545000},"page":"84","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The Adaptive Seismic Resilience of Infrastructure Systems: A Bayesian Networks Analysis"],"prefix":"10.3390","volume":"11","author":[{"given":"Hui","family":"Tang","sequence":"first","affiliation":[{"name":"Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingping","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Management, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Business School, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6573-1291","authenticated-orcid":false,"given":"Igor","family":"Martek","sequence":"additional","affiliation":[{"name":"School of Architecture and Built Environment, Deakin University, Geelong, VIC 3220, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,6]]},"reference":[{"key":"ref_1","unstructured":"EM-DAT (2022, April 10). 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