Machine Learning-Based Seismic Damage Assessment of Residential Buildings Considering Multiple Earthquake and Structure Uncertainties-Reference-Cited by-同舟云学术

Machine Learning-Based Seismic Damage Assessment of Residential Buildings Considering Multiple Earthquake and Structure Uncertainties

Published:2023-08 Issue:3 Volume:24 Page:
ISSN:1527-6988
Container-title:Natural Hazards Review
language:en
Short-container-title:Nat. Hazards Rev.

Author:

Yuan Xinzhe¹^ORCID,Li Liujun²,Zhang Haibin³,Zhu Yanping⁴,Chen Genda⁵^ORCID,Dagli Cihan⁶

Affiliation:

1. Formerly, Research Associate, Dept. of Civil, Architectural and Environmental Engineering, Missouri Univ. of Science and Technology, Rolla, MO 65401. ORCID: .

2. Assistant Professor, Dept. of Soil and Water System, Univ. of Idaho, Moscow, ID 83844.

3. Research Consultant, Dept. of Civil, Architectural and Environmental Engineering, Missouri Univ. of Science and Technology, Rolla, MO 65401.

4. Postdoctoral Fellow, Dept. of Civil, Architectural and Environmental Engineering, Missouri Univ. of Science and Technology, Rolla, MO 65401.

5. Professor, Dept. of Civil, Architectural and Environmental Engineering, Missouri Univ. of Science and Technology, Rolla, MO 65401 (corresponding author). ORCID: .

6. Professor, Dept. of Engineering Management and Systems Engineering, Missouri Univ. of Science and Technology, Rolla, MO 65401.

Publisher

American Society of Civil Engineers (ASCE)

Subject

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

Link

https://ascelibrary.org/doi/pdf/10.1061/NHREFO.NHENG-1681

Reference56 articles.

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2. Hindcasting community-level building damage for the 2011 Joplin EF5 tornado

3. A vector-valued ground motion intensity measure consisting of spectral acceleration and epsilon

4. Camelo, V. S. 2003. Dynamic characteristics of woodframe buildings. Pasadena, CA: California Institute of Technology.

5. Caruana R. and A. Niculescu-Mizil. 2006. “An empirical comparison of supervised learning algorithms.” In Proc. 23rd Int. Conf. on Machine Learning 161–168. New York: Association for Computer Machinery.

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