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Rapid visual screening of soft-story buildings from street view images using deep learning classification

  • Special Section: Recent Progress in Evaluation and Improvement on Seismic Resilience of Engineering Structures
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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. 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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References

  • ATC (1988), “Rapid Visual Screening of Buildings for Potential Seismic Hazards: A Handbook, FEMA 154,” Federal Emergency Management Agency, Washington, DC, USA.

  • ATC (2002), “Rapid Visual Screening of Buildings for Potential Seismic Hazards: A Handbook, FEMA 154, second edition,” Applied Technology Council, National Earthquakes Hazards Reduction Program, USA.

  • ATC (2015), “Rapid Visual Screening of Buildings for Potential Seismic Hazards: A Handbook, FEMA 154, third edition,” Federal Emergency Management Agency, Washington DC, USA.

  • Bency AJ, Rallapalli S, Ganti RK, Srivatsa M and Manjunath B (2017), “Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery,” Proceedings of the IEEE Winter Conference on Applications of Computer Vision.

  • Deng J, Dong W, Socher R, Li LJ, Li K and FeiFei L (2009), “ImageNet: A Large-Scale Hierarchical Image Database,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

  • Gebru T, Krause J, Wang Y, Chen D, Deng J, Aiden EL and FeiFei L (2017), “Using Deep Learning and Google Street View to Estimate the Demographic Makeup of Neighborhoods Across the United States,” Proceedings of the National Academy of Sciences, 114(50): 13108–13113.

    Article  Google Scholar 

  • Girshick R (2015), “Fast r-cnn,” Proceedings of the IEEE International Conference on Computer Vision.

  • He K, Zhang X, Ren S and Sun J (2016), “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

  • Kang J, Körner M, Wang Y, Taubenböck H and Zhu XX (2018), “Building Instance Classification Using Street View Images,” ISPRS Journal of Photogrammetry and Remote Sensing, 145: 44–59.

    Article  Google Scholar 

  • Karbassi A and Nollet M (2007), “The Adaptation of the FEMA 154 Methodology for the Rapid Visual Screening of Existing Buildings in Accordance with Nbcc-2005,” Proceedings of the 9th Canadian Conference on Earthquake Engineering, Ottawa, Ont, 27–29.

  • Law S, Paige B and Russell C (2018), “Take a Look Around: Using Street View and Satellite Images to Estimate House Prices,” arXiv Preprint arXv.1807.07155.

  • Liu X, Chen Q, Zhu L, Xu Y and Lin L (2017). “Place-Centric Visual Urban Perception with Deep Multi-Instance Regression,” Proceedings of the 25th ACM International Conference on Multimedia.

  • Naik N, Philipoom J, Raskar R and Hidalgo C (2014), “Streetscore-Predicting the Perceived Safety of One Million Streetscapes,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.

  • Ningthoujam M and Nanda RP (2018), “Rapid Visual Screening Procedure of Existing Building Based on Statistical Analysis,” International Journal of Disaster Risk Reduction, 28: 720–730.

    Article  Google Scholar 

  • Perrone D, Aiello MA, Pecce M and Rossi F (2015), “Rapid Visual Screening for Seismic evaluation of RC Hospital Buildings,” Structures, Vol. 3, Elsevier, 57–70.

  • Ploeger S, Sawada M, Elsabbagh A, Saatcioglu M, Nastev M and Rosetti E (2016), “Urban Rat: New Tool for Virtual and Site-Specific Mobile Rapid Data Collection for Seismic Risk Assessment,” Journal of Computing in Civil Engineering, 30(2): 04015006.

    Article  Google Scholar 

  • Rathje EM, Dawson C, Padgett JE, Pinelli JP, Stanzione D, Adair A, Arduino P, Brandenberg S J, Cockerill T, Dey C, et al. (2017), “Designsafe: New Cyberinfrastructure for Natural Hazards Engineering,” Natural Hazards Review, 18(3): 06017001.

    Article  Google Scholar 

  • Ren S, He K, Girshick R and Sun J (2015), “Faster R-Cnn: Towards Real-Time Object Detection with Region Proposal Networks,” Advances in Neural Information Processing Systems.

  • Saatcioglu M, Shooshtari M and Foo S (2013), “Seismic Screening of Buildings Based on the 2010 National Building Code of Canada,” Canadian Journal of Civil Engineering, 40(5): 483–498.

    Article  Google Scholar 

  • Srikanth T, Kumar RP, Singh AP, Rastogi BK and Kumar S (2010), “Earthquake Vulnerability Assessment of Existing Buildings in Gandhidham and Adipur Cities Kachchh, Gujarat (India),” European Journal of Scientific Research, 41(3): 336–353.

    Google Scholar 

  • Sun Y, Chen Y, Wang X and Tang X (2014), “Deep Learning Face Representation by Joint Identification-Verification,” Advances in Neural Information Processing Systems.

  • Szegedy C, Ioffe S, Vanhoucke V and Alemi AA (2017), “Inception-V4, Inception-Resnet and the Impact of Residual Connections on Learning,” Proceedings of the 31st AAAI Conference on Artificial Intelligence.

  • Szegedy C, Vanhoucke V, Ioffe S, Shlens J and Wojna Z (2016), “Rethinking the Inception Architecture for Computer Vision,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

  • Wallace NM and Miller TH (2008), “Seismic Screening of Public Facilities in Oregon’s Western Counties,” Practice Periodical on Structural Design and Construction, 13(4): 189–197.

    Article  Google Scholar 

  • Wang C (2019). “NHERI-SimCenter/SURF: v0.2.0,” <https://doi.org/10.5281/zenodo.3463676> (September).

  • Wang C and Chen Q (2018), “A Hybrid Geotechnical and Geological Data-Based Framework for Multiscale Regional Liquefaction Hazard Mapping,” Géotechnique, 68(7): 614–625.

    Google Scholar 

  • Wang C, Chen Q, Shen M and Juang CH (2017), “On the Spatial Variability of Cpt-Based Geotechnical Parameters for Regional Liquefaction Evaluation,” Soil Dynamics and Earthquake Engineering, 95: 153–166.

    Article  Google Scholar 

  • Zhou B, Khosla A, Lapedriza A, Oliva A and Torralba A (2016), “Learning Deep Features for Discriminative Localization,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

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Acknowledgement

This study is based upon work supported by the US National Science Foundation under Grant No. 1612843. NHERI DesignSafe (Rathje et al., 2017) and Texas Advanced Computing Center (TACC) are acknowledged for the generous allotment of compute resources.

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Correspondence to Chaofeng Wang.

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Yu, Q., Wang, C., McKenna, F. et al. Rapid visual screening of soft-story buildings from street view images using deep learning classification. Earthq. Eng. Eng. Vib. 19, 827–838 (2020). https://doi.org/10.1007/s11803-020-0598-2

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  • DOI: https://doi.org/10.1007/s11803-020-0598-2

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