Quantifying post-disaster business recovery through Bayesian methods-Reference-Cited by-同舟云学术

Quantifying post-disaster business recovery through Bayesian methods

Published:2020-06-13 Issue:6 Volume:17 Page:838-856
ISSN:1573-2479
Container-title:Structure and Infrastructure Engineering
language:en
Short-container-title:Structure and Infrastructure Engineering

Author:

Aghababaei Mohammad¹,Koliou Maria¹^ORCID,Watson Maria²^ORCID,Xiao Yu³

Affiliation:

1. Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, USA

2. Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX, USA

3. Nohad A. Toulan School of Urban Studies and Planning, Portland State University, Portland, OR, USA

Funder

U.S. National Institute of Standards and Technology and Colorado State University

Publisher

Informa UK Limited

Subject

Mechanical Engineering,Ocean Engineering,Geotechnical Engineering and Engineering Geology,Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/15732479.2020.1777569

Reference60 articles.

1. Aghababaei, M. (2017). Probabilistic damage models for detailed risk analysis of buildings with reliability methods (Master’s thesis). Dept of Civil Eng., Sharif University of Technology, Tehran, Iran.

2. Detailed seismic risk analysis of buildings using structural reliability methods

3. Performance Assessment of Building Infrastructure Impacted by the 2017 Hurricane Harvey in the Port Aransas Region

4. Component damage models for detailed seismic risk analysis using structural reliability methods

5. Alesch, D. J., Holly, J. N., Mittler, E. & Nagy, R. (2001). Organizations at risk: What happens when small businesses and not-for-profits encounter natural disasters. Retrieved from chamberofecocommerce.com.

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