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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Vydáno v:	Earthquake Engineering and Engineering Vibration Ročník 19; číslo 4; s. 827 - 838
Hlavní autoři:	Yu, Qian, Wang, Chaofeng, McKenna, Frank, Yu, Stella X., Taciroglu, Ertugrul, Cetiner, Barbaros, Law, Kincho H.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Harbin Institute of Engineering Mechanics, China Earthquake Administration 01.10.2020 Springer Nature B.V International Computer Science Institute, University of California, Berkeley, CA, USA %Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA %Civil and Environmental Engineering, University of California, Los Angeles, CA, USA %Civil and Environmental Engineering, Stanford University, CA, USA
Témata:	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 deep learning soft-story building CNN rapid visual screening street view image
ISSN:	1671-3664, 1993-503X
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1671-3664 1993-503X
DOI:	10.1007/s11803-020-0598-2