First-Order Reliability Approach to Quantify and Improve Building Portfolio Resilience
Publication: Journal of Structural Engineering
Volume 142, Issue 8
Abstract
The concept of disaster-resilient communities has gained considerable acceptance and attention over the past decade, requiring the assessment of not only the monetary losses surrounding a hazard, but also the complex, time-dependent factors that influence community resilience. This paper presents an analytical, reliability-based approach to quantify seismic resilience based on the robustness and restoration rapidity of a portfolio of buildings following an earthquake event. The reliability problem is formulated using random variables to describe the spatially correlated seismic intensity, structural response, and duration of posthazard recovery for predefined building combinations within a portfolio. Based on these random variables, the first-order reliability method (FORM) is used as a basis to develop a new algorithm to evaluate a probability distribution of resilience for a suite of spatially distributed buildings. In addition, sensitivity measures are computed within FORM and used to prioritize cost-effective mitigation strategies to increase portfolio resilience. This assessment puts prehazard retrofit and posthazard restoration measures into a common preposterior framework to determine the most optimal allocation of resources to improve resilience given budgetary constraints. Preliminary results indicate that prehazard retrofit is often most cost-effective for increasing resilience; however, posthazard restoration efficiency is more cost-effective for achieving high resilience thresholds characterized by longer return periods.
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Acknowledgments
The authors would like to thank Keith Porter at the University of Colorado for many useful conversations regarding this research and for providing building inventory data. Additional thanks to Charlie Kircher with Kircher & Associates for providing building height distribution information. The authors also gratefully acknowledge the support of the National Science Foundation (NSF), grant number CMMI 1063790. The opinions expressed in this paper are those of the authors, and do not necessarily reflect the views or policies of NSF.
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Published In
Journal of Structural Engineering
Volume 142 • Issue 8 • August 2016
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Nov 1, 2013
Accepted: Oct 29, 2014
Published online: Dec 3, 2014
Discussion open until: May 3, 2015
Published in print: Aug 1, 2016
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