Abstract

To predict the hazard-induced risks of buildings and infrastructures and assess the losses caused by hazards, the fragility curve method is a common quantitative risk assessment procedure for civil structures. It has been popularly used for decades for different hazards including earthquakes, hazardous winds, tsunamis, and fires. However, there are limited reports regarding blast risk assessment of buildings using the fragility curve method. This study developed empirical blast fragility curves for wood residential buildings using the real 2013 West fertilizer plant explosion data. The development processes included five key steps: (1) selecting and calculating the blast hazard intensity measure and the air-blast incident overpressure; (2) selecting the damage states rating systems and classifying the damage state of each damaged building; (3) determining the frequency distribution of damaged buildings for each damage state; (4) proving the cumulative lognormal distribution function to describe the fragility relationship between the blast damage states and the blast hazard intensity measure; and (5) constructing the empirical fragility curves by fitting the building damage information to the selected fragility relationship distribution function. The resulted blast fragility curves of this study can be used by government officials to predict blast-induced damages of residential buildings, to plan the optimal locations and operational capacities of emergency facilities, to estimate total economic losses due to potential explosions, and to plan the social, physical and economic resilience of communities.

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Data Availability Statement

Some data, models, or code generated or used during the study are available from the corresponding author by request, such as building damage pictures, ArcGIS geodatabase, and the fragility analysis document.

Acknowledgments

This paper is based on work supported by the National Science Foundation under Grant No. ACI-1640818. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Go to Journal of Performance of Constructed Facilities
Journal of Performance of Constructed Facilities
Volume 34Issue 3June 2020

History

Received: Feb 5, 2019
Accepted: Sep 23, 2019
Published online: Feb 29, 2020
Published in print: Jun 1, 2020
Discussion open until: Jul 29, 2020

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Associate Professor, Dept. of Engineering Technology, College of Engineering, Univ. of North Texas, 3940 N. Elm St. F115M, Denton, TX 76207 (corresponding author). ORCID: https://orcid.org/0000-0001-5990-8425. Email: [email protected]
Research Assistant, College of Engineering, Univ. of North Texas, 3940 N. Elm St., Denton, TX 76207. Email: [email protected]
Research Professor, College of Engineering, Univ. of North Texas, 3940 N. Elm St., Denton, TX 76207. Email: [email protected]
Interim Chair & BLG Endowed Professor, Dept. of Mechanical Engineering, Cleveland State Univ., Cleveland, OH 44115. Email: [email protected]
William J. Tolone [email protected]
Professor, Dept. of Software and Information Systems, College of Computing and Informatics, Univ. of North Carolina at Charlotte, 9201 University City Blvd. 443H, Charlotte, NC 28223. Email: [email protected]
Postdoctoral Research Fellow, College of Computing and Informatics, Univ. of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223; Researcher, Knowledge-Based and AI Systems, SRTA-City, Universities and Research District, New Borg Elarab, Alexandria 21934, Egypt. ORCID: https://orcid.org/0000-0001-6059-5032. Email: [email protected]; [email protected]
Sreyasee Das Bhattacharjee [email protected]
Assistant Professor of Teaching, Dept. of Computer Science and Engineering, State University of New York at Buffalo, NY 14260; Adjunct Professor, College of Computing and Informatics, Univ. of North Carolina at Charlotte, Charlotte, NC 28223. Email: [email protected]; [email protected]
Assistant Professor, Dept. of Computer Science, North Carolina Agricultural and Technical State Univ., 1601 E. Market St., Greensboro, NC 27411; Adjunct Professor, College of Computing and Informatics, Univ. of North Carolina at Charlotte, Charlotte, NC 282231. Email: [email protected]; [email protected]

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