Rapid visual screening of soft-story buildings from street view images using deep learning classification
Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelih...
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| Veröffentlicht in: | Earthquake Engineering and Engineering Vibration 2020-10, Vol.19 (4), p.827-838 |
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| container_title | Earthquake Engineering and Engineering Vibration |
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| creator | Yu, Qian Wang, Chaofeng McKenna, Frank Yu, Stella X. Taciroglu, Ertugrul Cetiner, Barbaros Law, Kincho H. |
| description | Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelihood of collapse during moderate or severe earthquakes. Identifying and retrofitting buildings with such irregularities—generally termed as soft-story buildings—is, therefore, vital in earthquake preparedness and loss mitigation efforts. Soft-story building identification through conventional means is a labor-intensive and time-consuming process. In this study, an automated procedure was devised based on deep learning techniques for identifying soft-story buildings from street-view images at a regional scale. A database containing a large number of building images and a semi-automated image labeling approach that effectively annotates new database entries was developed for developing the deep learning model. Extensive computational experiments were carried out to examine the effectiveness of the proposed procedure, and to gain insights into automated soft-story building identification. |
| doi_str_mv | 10.1007/s11803-020-0598-2 |
| format | Article |
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In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelihood of collapse during moderate or severe earthquakes. Identifying and retrofitting buildings with such irregularities—generally termed as soft-story buildings—is, therefore, vital in earthquake preparedness and loss mitigation efforts. Soft-story building identification through conventional means is a labor-intensive and time-consuming process. In this study, an automated procedure was devised based on deep learning techniques for identifying soft-story buildings from street-view images at a regional scale. A database containing a large number of building images and a semi-automated image labeling approach that effectively annotates new database entries was developed for developing the deep learning model. Extensive computational experiments were carried out to examine the effectiveness of the proposed procedure, and to gain insights into automated soft-story building identification.</description><identifier>ISSN: 1671-3664</identifier><identifier>EISSN: 1993-503X</identifier><identifier>DOI: 10.1007/s11803-020-0598-2</identifier><language>eng</language><publisher>Harbin: Institute of Engineering Mechanics, China Earthquake Administration</publisher><subject>Automation ; Buildings ; Civil Engineering ; Computer applications ; Control ; Deep learning ; Disaster management ; Dynamical Systems ; Earth Sciences ; Earthquakes ; Emergency preparedness ; Geotechnical Engineering & Applied Earth Sciences ; Identification ; Image classification ; Labour ; Machine learning ; Mitigation ; Multistory buildings ; Procedures ; Recent progress in evaluation and improvement on seismic resilience of engineering structures ; Retrofitting ; Seismic activity ; Seismic hazard ; Seismic surveys ; Special Section: Recent Progress in Evaluation and Improvement on Seismic Resilience of Engineering Structures ; Stiffness ; Vibration ; Vulnerability</subject><ispartof>Earthquake Engineering and Engineering Vibration, 2020-10, Vol.19 (4), p.827-838</ispartof><rights>Institute of Engineering Mechanics, China Earthquake Administration 2020</rights><rights>Institute of Engineering Mechanics, China Earthquake Administration 2020.</rights><rights>Copyright © Wanfang Data Co. 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All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c353t-610c88ef2934acce33a9023c43fb30f96a7727700ea4ce1d449213d59d76f0903</citedby><cites>FETCH-LOGICAL-c353t-610c88ef2934acce33a9023c43fb30f96a7727700ea4ce1d449213d59d76f0903</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://www.wanfangdata.com.cn/images/PeriodicalImages/dzgcygczd-e/dzgcygczd-e.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11803-020-0598-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11803-020-0598-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Yu, Qian</creatorcontrib><creatorcontrib>Wang, Chaofeng</creatorcontrib><creatorcontrib>McKenna, Frank</creatorcontrib><creatorcontrib>Yu, Stella X.</creatorcontrib><creatorcontrib>Taciroglu, Ertugrul</creatorcontrib><creatorcontrib>Cetiner, Barbaros</creatorcontrib><creatorcontrib>Law, Kincho H.</creatorcontrib><title>Rapid visual screening of soft-story buildings from street view images using deep learning classification</title><title>Earthquake Engineering and Engineering Vibration</title><addtitle>Earthq. Eng. Eng. Vib</addtitle><description>Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelihood of collapse during moderate or severe earthquakes. Identifying and retrofitting buildings with such irregularities—generally termed as soft-story buildings—is, therefore, vital in earthquake preparedness and loss mitigation efforts. Soft-story building identification through conventional means is a labor-intensive and time-consuming process. In this study, an automated procedure was devised based on deep learning techniques for identifying soft-story buildings from street-view images at a regional scale. A database containing a large number of building images and a semi-automated image labeling approach that effectively annotates new database entries was developed for developing the deep learning model. 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Eng. Eng. Vib</stitle><date>2020-10-01</date><risdate>2020</risdate><volume>19</volume><issue>4</issue><spage>827</spage><epage>838</epage><pages>827-838</pages><issn>1671-3664</issn><eissn>1993-503X</eissn><abstract>Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelihood of collapse during moderate or severe earthquakes. Identifying and retrofitting buildings with such irregularities—generally termed as soft-story buildings—is, therefore, vital in earthquake preparedness and loss mitigation efforts. Soft-story building identification through conventional means is a labor-intensive and time-consuming process. 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| source | Alma/SFX Local Collection; SpringerLink Journals - AutoHoldings |
| subjects | Automation Buildings Civil Engineering Computer applications Control Deep learning Disaster management Dynamical Systems Earth Sciences Earthquakes Emergency preparedness Geotechnical Engineering & Applied Earth Sciences Identification Image classification Labour Machine learning Mitigation Multistory buildings Procedures Recent progress in evaluation and improvement on seismic resilience of engineering structures Retrofitting Seismic activity Seismic hazard Seismic surveys Special Section: Recent Progress in Evaluation and Improvement on Seismic Resilience of Engineering Structures Stiffness Vibration Vulnerability |
| title | Rapid visual screening of soft-story buildings from street view images using deep learning classification |
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