INFRASTRUCTURE RISK MANAGEMENT IN PREPAREDNESS OF EXTREME EVENTS

  • Eun Ho Oh (School of Civil Engineering, Construction Engineering and Management, Purdue University) ;
  • Abhijeet Deshmukh (School of Civil Engineering, Construction Engineering and Management, Purdue University) ;
  • Makarand Hastak (Construction Engineering and Management, Purdue University)
  • Published : 2009.05.27

Abstract

Natural disasters, such as the recent floods in the Midwest, Hurricane Ike in the Gulf coast region (U.S.), and the earthquake in Sichuan (China), cause severe damage to the infrastructure as well as the associated industries and communities that rely on the infrastructure. The estimated damages due to Hurricane Ike in 2008 were a staggering $27 billion, the third worst in U.S. history. In addition, the worst earthquake in three decades in Sichuan resulted in about 90,000 people dead or missing and $20 billion of the estimated loss. A common observation in the analyses of these natural disaster events is the inadequacy of critical infrastructure to withstand the forces of natural calamities and the lack of mitigation strategies when they occur on the part of emergency-related organizations, industries, and communities. If the emergency-related agencies could identify and fortify the vulnerable critical infrastructure in the preparedness stage, the damage and impacts can be significantly reduced. Therefore, it is important to develop a decision support system (DSS) for identifying region-specific mitigation strategies based on the inter-relationships between the infrastructure and associated industries and communities in the affected region. To establish effective mitigation strategies, relevant data were collected from the affected areas with respect to the technical, social, and economic impact levels. The data analysis facilitated identifying the major factors, such as vulnerability, criticality, and severity, for developing a DSS. Customized mitigation strategies that will help agencies prepare, respond, and recover according to the disaster response were suggested.

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Acknowledgement

This work was supported by the National Science Foundation as SGER under grant No. 0848016. However, the conclusions and opinions expressed here by the authors do not necessarily reflect the views of the funding agency.