Detecting and Geolocating City-Scale Soft-Story Buildings by Deep Machine Learning for Urban Seismic Resilience-Reference-Cited by-同舟云学术

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Detecting and Geolocating City-Scale Soft-Story Buildings by Deep Machine Learning for Urban Seismic Resilience

Published:2022-02 Issue:1 Volume:23 Page:
ISSN:1527-6988
Container-title:Natural Hazards Review
language:en
Short-container-title:Nat. Hazards Rev.

Author:

Kalfarisi Rony¹,Hmosze Maadh²^ORCID,Wu Zheng Yi³

Affiliation:

1. Software Engineer II, Bentley Systems Singapore Pte Ltd., 1 Harbourfront Place, #18-01 to 03 HarbourFront Tower One, Singapore 098633.

2. Assistant Lecturer, Dept. of Computer Center, Jabir ibn Hayyan Medical Univ., P.O. Box 13 Kufa. Najaf 54001, Iraq. ORCID:

3. Bentley Fellow, Bentley Systems, Incorporated, 27 Siemon Co Dr., Watertown, CT 06795 (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Subject

General Social Sciences,General Environmental Science,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29NH.1527-6996.0000541

Reference26 articles.

1. Structural engineering aspects of the June 27, 1998 Adana–Ceyhan (Turkey) earthquake

2. ASCE. 2002. Seismic evaluation of existing buildings: Second public ballot. Reston, VA: ASCE.

3. Influence of Structural Walls on the Seismic Performance of RC Buildings during the May 19, 2011 Simav Earthquake in Turkey

4. Bency A. J. S. Rallapalli R. K. Ganti M. Srivatsa and B. S. Manjunath. 2017. “Beyond spatial auto-regressive models: Predicting housing prices with satellite imagery.” In Proc. 2017 IEEE Winter Conf. on Applications of Computer Vision (WACV). New York: IEEE.

5. Seismic collapse performance of Los Angeles soft, weak, and open-front wall line woodframe structures retrofitted using different procedures

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