{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T01:44:58Z","timestamp":1788399898299,"version":"build-2803163510"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2020,2,1]],"date-time":"2020-02-01T00:00:00Z","timestamp":1580515200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2020,2,1]],"date-time":"2020-02-01T00:00:00Z","timestamp":1580515200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"},{"start":{"date-parts":[[2020,2,1]],"date-time":"2020-02-01T00:00:00Z","timestamp":1580515200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Earthquake Spectra"],"published-print":{"date-parts":[[2020,2]]},"abstract":"<jats:p>\n                    The ability to rapidly assess the spatial distribution and severity of building damage is essential to post\u2010event emergency response and recovery. Visually identifying and classifying individual building damage requires significant time and personnel resources and can last for months after the event. This article evaluates the feasibility of using machine learning techniques such as discriminant analysis,\n                    <jats:italic>k<\/jats:italic>\n                    \u2010nearest neighbors, decision trees, and random forests, to rapidly predict earthquake\u2010induced building damage. Data from the 2014 South Napa earthquake are used for the study where building damage is classified based on the assigned Applied Technology Council (ATC)\u201020 tag (red, yellow, and green). Spectral acceleration at a period of 0.3\u2009s, fault distance, and several building specific characteristics (e.g. age, floor area, presence of plan irregularity) are used as features or predictor variables for the machine learning models. A portion of the damage data from the Napa earthquake is used to obtain the forecast model, and the performance of each machine learning technique is evaluated using the remaining (test) data. It is noted that the random forest algorithm can accurately predict the assigned tags for 66% of the buildings in the test dataset.\n                  <\/jats:p>","DOI":"10.1177\/8755293019878137","type":"journal-article","created":{"date-parts":[[2019,11,29]],"date-time":"2019-11-29T04:52:31Z","timestamp":1575003151000},"page":"183-208","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":269,"title":["Classifying earthquake damage to buildings using machine learning"],"prefix":"10.1002","volume":"36","author":[{"given":"Sujith","family":"Mangalathu","sequence":"first","affiliation":[{"name":"Equifax Inc Alpharetta Atlanta USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Sun","sequence":"additional","affiliation":[{"name":"Yahoo Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chukwuebuka C.","family":"Nweke","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering University of California Los Angeles Los Angeles CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengxiang","family":"Yi","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering University of California Los Angeles Los Angeles CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henry V.","family":"Burton","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering University of California Los Angeles Los Angeles CA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,2]]},"reference":[{"key":"e_1_2_9_2_1","volume-title":"ASCE 7: Minimum design loads for buildings and other structures. 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Doctoral dissertation Georgia Institute of Technology Atlanta USA."},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2018.01.008"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/eqe.2991"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1539-6924.2011.01618.x"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1193\/1.2428313"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11069-013-0618-x"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/eqe.3010"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0267-7261(96)00043-7"},{"key":"e_1_2_9_25_1","unstructured":"USGS(2014)Available at:https:\/\/earthquake.usgs.gov\/earthquakes\/eventpage\/nc72282711\/executive#dyfi(accessed 2017)."},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1193\/1.2923924"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1193\/1.1586057"},{"key":"e_1_2_9_28_1","volume-title":"ShakeMap Manual: Technical Manual, User\u2019s Guide, and Software Guide","author":"Wald DJ","year":"2005"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICOSP.2006.345752"},{"key":"e_1_2_9_30_1","unstructured":"YuneH(2014) Family makes the case for a second Napa earthquake fatality.Napa Valley Register 24 September. 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