Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81108
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dc.contributorDepartment of Building and Real Estate-
dc.creatorZhang, ZY-
dc.creatorHsu, TY-
dc.creatorWei, HH-
dc.creatorChen, JH-
dc.date.accessioned2019-07-29T03:17:59Z-
dc.date.available2019-07-29T03:17:59Z-
dc.identifier.issn2076-3417en_US
dc.identifier.urihttp://hdl.handle.net/10397/81108-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Zhang, Z.; Hsu, T.-Y.; Wei, H.-H.; Chen, J.-H. Development of a Data-Mining Technique for Regional-Scale Evaluation of Building Seismic Vulnerability. Appl. Sci. 2019, 9, 1502, 17 pages is available at https://dx.doi.org/10.3390/app9071502en_US
dc.subjectBuilding seismic vulnerabilityen_US
dc.subjectData miningen_US
dc.subjectEarthquakeen_US
dc.subjectSeismic risken_US
dc.subjectSupport Vector Machineen_US
dc.titleDevelopment of a data-mining technique for regional-scale evaluation of building seismic vulnerabilityen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1en_US
dc.identifier.epage17en_US
dc.identifier.volume9en_US
dc.identifier.issue7en_US
dc.identifier.doi10.3390/app9071502en_US
dcterms.abstractAssessing the seismic vulnerability of large numbers of buildings is an expensive and time-consuming task, requiring the collection of highly complex and multifaceted data on building characteristics and the use of sophisticated computational models. This study reports on the development of a data mining technique: Support Vector Machine (SVM) for resolving such multi-dimensional data problems for assessing buildings' seismic vulnerability at a regional scale. Particularly, we developed an SVM model for rapid assessment of the macroscale seismic vulnerability of buildings in terms of spectral yield and ultimate points of their capacity curves. Two case studies, one with 11 building characteristics and the other with 20, were used to test the proposed SVM model. The results show that when 20 building characteristics are included, an individual building's seismic vulnerability in term of its spectral yield and ultimate points can be predicted by the proposed SVM model with an average 64% accuracy if the training dataset contains 400 samples, rising to 74% with 4400 training samples. Coupling the proposed technique with demand curves based on buildings' locations will enable rapid and reliable seismic-risk assessment at a regional scale, requiring only basic building characteristics rather than complex computational models.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationApplied sciences, 1 Apr. 2019, v. 9, no. 7, 1502, p. 1-17-
dcterms.isPartOfApplied sciences-
dcterms.issued2019-
dc.identifier.isiWOS:000466547500237-
dc.identifier.scopus2-s2.0-85069771492-
dc.identifier.artn1502en_US
dc.description.validate201907 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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