A Risk-based Optimization Modeling Framework for Mitigating Fire Events for Water and Fire Response Infrastructures
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The purpose of this dissertation is to address risk and consequences of and effective mitigation strategies for urban fire events involving two critical infrastructures- water distribution and emergency services. Water systems have been identified as one of the United States' critical infrastructures and are vulnerable to various threats caused by natural disasters or malevolent actions. The primary goals of urban water distribution systems are reliable delivery of water during normal and emergency conditions (such as fires), ensuring this water is of acceptable quality, and accomplishing these tasks in a cost-effective manner. Due to interdependency of water systems with other critical infrastructures-e.g., energy, public health, and emergency services (including fire response)- water systems planning and management offers numerous challenges to water utilities and affiliated decision makers. The dissertation is divided into three major sections, each of which presents and demonstrates a methodological innovation applied to the above problem. First, a risk based dynamic programming modeling approach is developed to identify the critical components of a water distribution system during fire events under three failure scenarios: (1) accidental failure due to soil-pipe interaction, (2) accidental failure due to a seismic activity, and (3) intentional failure or malevolent attack. Second, a novel evolutionary computation based multi-objective optimization technique, Non-dominated Sorting Evolution Strategy (NSES), is developed for systematic generation of optimal mitigation strategies for urban fire events for water distribution systems with three competing objectives: (1) minimizing fire damages, (2) minimizing water quality deficiencies, and (3) minimizing the cost of mitigation. Third, a stochastic modeling approach is developed to assess urban fire risk for the coupled water distribution and fire response systems that includes probabilistic expressions for building ignition, WDS failure, and wind direction. Urban fire consequences are evaluated in terms of number of people displaced and cost of property damage. To reduce the assessed urban fire risk, the NSES multi-objective approach is utilized to generate Pareto-optimal solutions that express the tradeoff relationship between risk reduction, mitigation cost, and water quality objectives. The new methodologies are demonstrated through successful application to a realistic case study in water systems planning and management.
Evolutionary Computation based Algorithms
Urban Fire Risk
Kanta, Lufthansa Rahman (2009). A Risk-based Optimization Modeling Framework for Mitigating Fire Events for Water and Fire Response Infrastructures. Doctoral dissertation, Texas A&M University. Available electronically from