Identification of Optimization-Based Probabilistic Earthquake Scenarios for Regional Loss Estimation

Vaziri, Pantea; Davidson, Rachel A.; Apivatanagul, Pruttipong; Nozick, Linda K.

doi:10.1080/13632469.2011.597486

Cited by 34 publications

(33 citation statements)

References 21 publications

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“…There are two or three different ways to achieve each step (and more minor variations within methods) ( Table I). Vaziri et al [10] compares the different approaches to estimating the hazardconsistent annual occurrence probabilities in [7][8][9]18]. For example, Jayaram and Baker [11] propose a method that is basically the method combination [ [9] chose 59 earthquake scenarios from regional fault sources.…”

Section: Conceptual Comparison Of Methodsmentioning

confidence: 99%

“…The objective of the optimization is to minimize the sum of the errors over all sites i (called control points in Vaziri et al [10]) and return periods r, between points on the 'true' hazard curves and the corresponding points on hazard curves developed with the reduced set of earthquakes and hazardconsistent annual occurrence probabilities:…”

Section: Optimization-based Probabilistic Scenario Methods For Earthqumentioning

confidence: 99%

“…To address the computational demands, however, Chang et al [7], Campbell and Seligson [8], Lee et al [9], and Vaziri et al [10] introduced methods for the development of probabilistic earthquake scenarios in the first step. Jayaram and Baker [11] and Vaziri et al [10] extend the concept to introduce methods for development of probabilistic ground motion maps in the second step too. For clarity of presentation, we use the term probabilistic earthquake scenario j as in Chang et al [7] to mean one of a set of earthquake scenarios (i.e., source-magnitude combination) with associated hazard-consistent annual occurrence probabilities P j .…”

Section: Regional Probabilistic Seismic Risk Analysis Frameworkmentioning

confidence: 99%

“…This method is mentioned (although not applied) in Vaziri et al [10] and is applied to the analogous hurricane hazard problem in Legg et al [20] and Apivatanagul et al [21]. If the candidate set if relatively small, the result can depend on the arbitrary initial choice of centroid.…”

Section: 4mentioning

confidence: 99%

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Probabilistic seismic hazard analysis for spatially distributed infrastructure

Han

Davidson

2012

Earthq Engng Struct Dyn

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SUMMARY Two key issues distinguish probabilistic seismic risk analysis of a lifeline or portfolio of structures from that of a single structure. Regional analysis must consider the correlation among lifeline components or structures in the portfolio, and the larger scope makes it much more computationally demanding. In this paper, we systematically identify and compare alternative methods for regional hazard analysis that can be used as the first part of a computationally efficient regional probabilistic seismic risk analysis that properly considers spatial correlation. Specifically, each method results in a set of probabilistic ground motion maps with associated hazard‐consistent annual occurrence probabilities that together represent the regional hazard. The methods are compared according to how replicable and computationally tractable they are and the extent to which the resulting maps are physically realistic, consistent with the regional hazard and regional spatial correlation, and few in number. On the basis of a conceptual comparison and an empirical comparison for Los Angeles, we recommend a combination of simulation and optimization approaches: (i) Monte Carlo simulation with importance sampling of the earthquake magnitudes to generate a set of probabilistic earthquake scenarios (defined by source and magnitude); (ii) the optimization‐based probabilistic scenario method, a mixed‐integer linear program, to reduce the size of that set; (iii) Monte Carlo simulation to generate a set of probabilistic ground motion maps, varying the number of maps sampled from each earthquake scenario so as to minimize the sampling variance; and (iv) the optimization‐based probabilistic scenario again to reduce the set of probabilistic ground motion maps. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: Conceptual Comparison Of Methodsmentioning

confidence: 99%

Section: Optimization-based Probabilistic Scenario Methods For Earthqumentioning

confidence: 99%

Section: Regional Probabilistic Seismic Risk Analysis Frameworkmentioning

confidence: 99%

Section: 4mentioning

confidence: 99%

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Probabilistic seismic hazard analysis for spatially distributed infrastructure

Han

Davidson

2012

Earthq Engng Struct Dyn

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“…For our modeling, the hazard is represented by a set of earthquake scenarios (and their hazard-adjusted probabilities of occurrence) selected using the mathematical optimization method developed by Vaziri et al (2012). The method which select the events and their hazard-adjusted occurrence probabilities so as to minimize the error between the annual exceedance probability of PGA curves implied by the selected scenarios, and the "true" PGA curves, which for this analysis are given in the USGS Seismic Hazard Maps (USGS 2008b).…”

Section: Romero -9mentioning

confidence: 99%

Seismic Retrofit for Electric Power Systems

et al. 2015

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This paper develops a two-stage stochastic program and solution procedure to optimize the selection of seismic retrofit strategies to increase the resilience of electric power systems to earthquake hazards. The model explicitly considers the range of earthquake events that are possible and, for each, an approximation to the distribution of damage that is experienced. This is important because electric power systems are spatially distributed; hence their performance is driven by the distribution of damage of the components. We test this solution procedure against the nonlinear integer solver in LINGO 13 and apply the formulation and solution strategy to the Eastern Interconnection where the seismic hazard stems from the New Madrid Seismic Zone. Disciplines Electrical and Computer Engineering | Power and Energy CommentsThis is a manuscripts of an article published as Romero, Natalia, Linda K. Nozick, Ian Dobson, Ningxiong Xu, and Dean A. Jones. "Seismic retrofit for electric power systems." Earthquake Spectra 31, no. 2 (2015) This preprint is a PDF of a manuscript that has been accepted for publication in Earthquake Spectra. It is the final version that was uploaded and approved by the author(s) with figures embedded in the text. Romero -1Copyright ( This paper develops a two-stage stochastic program and solution procedure to optimize the selection of seismic retrofit strategies to increase the resilience of electric power systems to earthquake hazards. The model explicitly considers the range of earthquake events that are possible and, for each, an approximation to the distribution of damage that is experienced. This is important because electric power systems are spatially distributed; hence their performance is driven by the distribution of damage of the components. We test this solution procedure against the nonlinear integer solver in LINGO 13 and apply the formulation and solution strategy to the Eastern Interconnection where the seismic hazard stems from the New Madrid Seismic Zone.

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Fully probabilistic seismic displacement analysis of spatially distributed slopes using spatially correlated vector intensity measures

Wang

2013

Earthq Engng Struct Dyn

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SUMMARY Earthquake‐induced slope displacement is an important parameter for safety evaluation and earthquake design of slope systems. Traditional probabilistic seismic hazard analysis usually focuses on evaluating slope displacement at a particular location, and it is not suitable for spatially distributed slopes over a large region. This study proposes a computationally efficient framework for fully probabilistic seismic displacement analysis of spatially distributed slope systems using spatially correlated vector intensity measures (IMs). First, a spatial cross‐correlation model for three key ground motion IMs, that is, peak ground acceleration (PGA), Arias intensity, and peak ground velocity, is developed using 2686 ground motion recordings from 11 recent earthquakes. To reduce the computational cost, Monte Carlo simulation and data reduction techniques are utilized to generate spatially correlated random fields for the vector IMs. The slope displacement hazards over the region are further quantified using empirical predictive equations. Finally, an illustrative example is presented to highlight the importance of the spatial correlation and the advantage of using spatially correlated vector IMs in seismic hazard analysis of spatially distributed slopes. Copyright © 2013 John Wiley & Sons, Ltd.

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Identification of Optimization-Based Probabilistic Earthquake Scenarios for Regional Loss Estimation

Cited by 34 publications

References 21 publications

Probabilistic seismic hazard analysis for spatially distributed infrastructure

Probabilistic seismic hazard analysis for spatially distributed infrastructure

Seismic Retrofit for Electric Power Systems

Fully probabilistic seismic displacement analysis of spatially distributed slopes using spatially correlated vector intensity measures

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