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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Bibliographic Details
Published in:Earthquake Engineering and Engineering Vibration Vol. 19; no. 4; pp. 827 - 838
Main Authors: Yu, Qian, Wang, Chaofeng, McKenna, Frank, Yu, Stella X., Taciroglu, Ertugrul, Cetiner, Barbaros, Law, Kincho H.
Format: Journal Article
Language:English
Published: 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
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
deep learning
soft-story building
CNN
rapid visual screening
street view image
ISSN:1671-3664, 1993-503X
Online Access:Get full text
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Summary: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.
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ISSN:1671-3664
1993-503X
DOI:10.1007/s11803-020-0598-2