{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T12:06:26Z","timestamp":1772712386657,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,22]],"date-time":"2023-04-22T00:00:00Z","timestamp":1682121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"publisher","award":["CSTB2022NSCQ-MSX0594"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX0594"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"publisher","award":["cstc2018jscx-msybX0310"],"award-info":[{"award-number":["cstc2018jscx-msybX0310"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002865","name":"Chongqing Science and Technology Commission","doi-asserted-by":"publisher","award":["CSTB2022NSCQ-MSX0594"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX0594"]}],"id":[{"id":"10.13039\/501100002865","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002865","name":"Chongqing Science and Technology Commission","doi-asserted-by":"publisher","award":["cstc2018jscx-msybX0310"],"award-info":[{"award-number":["cstc2018jscx-msybX0310"]}],"id":[{"id":"10.13039\/501100002865","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study aims to develop different-classification-scheme-based building-seismic-resilience (BSR)-mapping models using random forest (RF) and a support vector machine (SVM). Based on a field survey of earthquake-damaged buildings in Shuanghe Town, the epicenter of the Changning M 5.8 earthquake that occurred on 17 June 2019, we selected 19 influencing factors for BSR assessment to establish a database. Based on three classification schemes for the description of BSR, we developed six machine learning assessment models for BSR mapping using RF and an SVM after optimizing the hyper-parameters. The validation indicators of model performance include precision, recall, accuracy, and F1-score as determined from the test sub-dataset. The results indicate that the RF- and SVM-based BSR models achieved prediction accuracies of approximately 0.64\u20130.94 for different classification schemes applied to the test sub-dataset. Additionally, the precision, recall, and F1-score indicators showed satisfactory values with respect to the BSR levels with relatively large sample sizes. The RF-based models had a lower tendency for overfitting compared to the SVM-based models. The performance of the BSR models was influenced by the quantity of total datasets, the classification schemes, and imbalanced data. Overall, the RF- and SVM-based BSR models can improve the evaluation efficiency of earthquake-damaged buildings in mountainous areas.<\/jats:p>","DOI":"10.3390\/rs15092226","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T02:06:11Z","timestamp":1682301971000},"page":"2226","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Different-Classification-Scheme-Based Machine Learning Model of Building Seismic Resilience Assessment in a Mountainous Region"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2045-729X","authenticated-orcid":false,"given":"Haijia","family":"Wen","sequence":"first","affiliation":[{"name":"National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education Chongqing, School of Civil Engineering, Chongqing University, Chongqing 400045, China"}]},{"given":"Xinzhi","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100083, China"}]},{"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education Chongqing, School of Civil Engineering, Chongqing University, Chongqing 400045, China"}]},{"given":"Mingyong","family":"Liao","sequence":"additional","affiliation":[{"name":"National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education Chongqing, School of Civil Engineering, Chongqing University, Chongqing 400045, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6318-2756","authenticated-orcid":false,"given":"Jiafeng","family":"Xiao","sequence":"additional","affiliation":[{"name":"National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education Chongqing, School of Civil Engineering, Chongqing University, Chongqing 400045, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1016\/j.proeng.2018.01.080","article-title":"Earthquake damage estimation systems: Literature review","volume":"212","author":"Kahandawa","year":"2018","journal-title":"Procedia Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1016\/j.engstruct.2010.08.008","article-title":"Framework for analytical quantification of disaster resilience","volume":"32","author":"Cimellaro","year":"2010","journal-title":"Eng. 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