A resilience-based method for prioritizing post-event building inspections

Ali Lenjani, Ilias Bilionis (), Shirley J. Dyke (), Chul Min Yeum and Ricardo Monteiro
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Ali Lenjani: Purdue University
Ilias Bilionis: Purdue University
Shirley J. Dyke: Purdue University
Chul Min Yeum: University of Waterloo
Ricardo Monteiro: IUSS

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2020, vol. 100, issue 2, No 20, 877-896

Abstract: Abstract Despite the wide range of possible scenarios in the aftermath of a disruptive event, each community can make choices to improve its resilience, or its ability to bounce back. A resilient community is one that has prepared for, and can thus absorb, recover from, and adapt to the disruptive event. One important aspect of the recovery phase is assessing the extent of the damage in the built environment through post-event building inspections. In this paper, we develop and demonstrate a resilience-based methodology intended to support rapid post-event decision making about inspection priorities with limited information. The method uses the basic characteristics of the building stock in a community (floor area, number of stories, type of construction, and configuration) to assign structure-specific fragility functions to each building. For an event with a given seismic intensity, the probability of each building reaching a particular damage state is determined, and is used to predict the actual building states and priorities for inspection. Losses are computed based on building usage category, estimated inspection costs, the consequences of erroneous decisions, and the potential for unnecessary restrictions in access. The aim is to provide a means for a community to make rapid cost-based decisions related to inspection of their building inventory. We pose the decision problem as an integer optimization problem that attempts to minimize the expected loss to the community. The advantages of this approach are that it: (1) is simple, (2) requires minimal inventory data, (3) is easily scalable, and (4) does not require significant computing power. Use of this approach before the hazard event can facilitate planning and resources allocation in advance of an event to achieve the desirable resiliency goals of a community.

Keywords: Urban resilience; Risk management; Uncertainty quantification; Inspection planning; Dynamic programming; Natural hazards (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11069-019-03849-0

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