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Rebounding from Disruptive Events: Optimization of Post-Disaster Reconstruction Activities for Critical and Interdependent Infrastructure Networks



Project Details
Author(s) Sharareh Kermanshachi, University of Texas at Arlington
Co-Author(s) Kelly Bergstrand, University of Texas at Arlington; Yi Leaf Zhang, University of Texas at Arlington; Jiwon Suh, University of Texas at Arlington
CTEDD Funding Year 2019 General RFP
Project Status In Progress
UTC Funding $80,462.45
End Date December 30, 2020

SummarySummary

The aim of this project is to collect data on the recovery processes due to natural disasters to develop time and cost estimation models for post-disaster recovery activities, identify tipping points to timely post-disaster recovery processes, and determine effective policies and educational programs which prevent substantial delays in the restoration period. Due to increasing frequency and severity of natural hazards occurrence, most recovery activities take longer duration than the initial anticipated plan created immediately after the disaster happen. Postponement in the recovery process imposes significant challenges to the communities and societies such as increased level of crime and poverty, bankruptcy of small businesses, permanent migration of the individuals, economic crisis and many more undesired consequences. Therefore, it is vital to understand the sources of delay in the recovery and reconstruction processes due to extreme natural hazards and adopt effective strategies preventing delay and the long-term effects on vulnerable communities. This project addresses this gap of knowledge by collecting information related to post-disaster conditions of damaged areas and analyzing the pace and consistency of recovery accomplishments. ...Comparative analysis among targeted locations will be performed to determine the differences in the behavior of recovery activities. To collect perspectives of policy-makers, technical practitioners, and public involved in the recovery process, Subject Matter Experts SMEs from cities, departments of transportation, local agencies, NGOs, and private consulting firms will be contacted. Collected data will be utilized to develop a data-driven model which integrates and quantifies the social, physical, and policy factors and determine the tipping pints in the recovery process yielding major interruptions in the process of the post-disaster recovery. The outcomes of this research project will include two data-driven models which integrate and quantify the interactions of social, policy, and physical factors. By recognizing various sources of delays in the process of post-disaster recovery and their interdependencies, this study will help national, state, and local decision- and policy-makers to timely assess the disaster recovery time and cost factors, allocate sufficient resources in these areas and take proactive actions to address social, policy, and physical challenges in the reconstruction process. Read more


Subjects#: Decision-making, Disaster Recovery, Infrastructure, Project Optimization, Quantitative Analysis