Identifying Pareto-optimal seismic rehabilitation strategies for water distribution networks considering decision maker’s risk attitudes

Sumaya Sharveen () and Mohsen Shahandashti ()
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Sumaya Sharveen: University of Texas at Arlington
Mohsen Shahandashti: University of Texas at Arlington

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 13, No 10, 11743-11764

Abstract: Abstract There is limited existing research that identifies the optimum rehabilitation strategy, taking utility decision-makers’ risk attitudes into account. The objective of this study is to detect the critical pipes of a water distribution network (WDN) for rehabilitation, maximizing the post-earthquake serviceability of the WDN while minimizing the risk of choosing a specific rehabilitation strategy. For that purpose, a multi-objective optimization framework is formulated. System Serviceability Index (SSI) is quantified to represent the serviceability of a WDN after an earthquake. One of the two objective functions in the optimization problem maximizes the expected SSI value. The second objective function minimizes the value at risk (VaR) or conditional value at risk (CVaR) of the decision-making. The solution methodology comprises five steps: pipe seismic repair rate calculation, hydraulic modeling, and analysis, Monte Carlo simulation, nondominated sorting genetic algorithm (NSGA) for optimization, and nondominated or Pareto-optimal rehabilitation strategies identification. The proposed approach is applied to a WDN to demonstrate its effectiveness. The proposed approach offers a range of nondominated or Pareto-optimal rehabilitation strategies facilitating the decision-making based on tradeoffs between post-earthquake serviceability and risk within a specific budget limit. The proposed approach outperforms existing methods by providing risk-averse decision-makers with a set of optimal rehabilitation strategies with known risk levels.

Keywords: Seismic vulnerability assessment; Risk assessment; Combinatorial optimization; Rehabilitation decision (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11069-024-06655-5

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