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 Jg. 19; H. 4; S. 827 - 838
Hauptverfasser:	Yu, Qian, Wang, Chaofeng, McKenna, Frank, Yu, Stella X., Taciroglu, Ertugrul, Cetiner, Barbaros, Law, Kincho H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	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
Schlagworte:	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
Online-Zugang:	Volltext
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