Strategic Implications for Civil Infrastructure and Logistical Support Systems in Post-Earthquake Disaster Management: The Case of St. Louis
Abstract
An important role of post-earthquake emergency management is to minimize the restoration time, which is the sum of the travel time and the response time. The travel time is the time needed to reach the affected area from the dispatch location, while the response time is the time required to bring the situation under control after reaching the affected area. A number of built environment variables, e.g., building collapse probability, and natural variables, e.g., flooding probability, are known to affect the restoration time. Data from St. Louis, Missouri, USA are used in conjunction with a discrete-event-based simulation model to identify the statistically significant variables via an analysis of variance. The experimental results show that in order to reduce the loss of life, the volume of resources and the building collapse and flooding probabilities are significant factors that should be accounted for in the emergency-response planning for an earthquake.
Recommended Citation
G. Fraioli et al., "Strategic Implications for Civil Infrastructure and Logistical Support Systems in Post-Earthquake Disaster Management: The Case of St. Louis," IEEE Engineering Management Review, Institute of Electrical and Electronics Engineers (IEEE), Dec 2020.
The definitive version is available at https://doi.org/10.1109/EMR.2020.3043183
Department(s)
Engineering Management and Systems Engineering
Second Department
Civil, Architectural and Environmental Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Earthquake; Emergency management; Training; Built environment (BUE)
International Standard Serial Number (ISSN)
0360-8581; 1937-4178
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
08 Dec 2020
Comments
Early Access
The research was partially funded by a smart living grant from S&T’s Intelligent Systems Center.