On the importance of uncertain factors in seismic fragility assessment

Borgonovo, Emanuele; Zentner, Irmela; Pellegri, A.; Tarantola, Stefano; Rocquigny, Etienne de

doi:10.1016/j.ress.2012.08.007

Cited by 37 publications

(14 citation statements)

References 23 publications

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“…increasing, decreasing or abrupt shifts of hydrologic extremes), as some valuable lessons can also be drawn from this domain of application to define and communicate an evolving probability of failure (named return period in this domain), and (2) deriving a global indicator of sensitivity via variance-based global sensitivity analysis (see e.g. Borgonovo et al, 2013). The latter approach introduces promising perspectives to ease the fitting process by filtering out beforehand some negligible mechanical and geometrical parameters.…”

Section: Discussion and Further Workmentioning

confidence: 99%

Non-stationary extreme value analysis applied to seismic fragility assessment for nuclear safety analysis

Rohmer¹,

Gehl²,

Marcilhac-Fradin³

et al. 2020

Nat. Hazards Earth Syst. Sci.

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Abstract. Fragility curves (FCs) are key tools for seismic probabilistic safety assessments that are performed at the level of the nuclear power plant (NPP). These statistical methods relate the probabilistic seismic hazard loading at the given site to the required performance of the NPP safety functions. In the present study, we investigate how the tools of non-stationary extreme value analysis can be used to model in a flexible manner the tail behaviour of the engineering demand parameter as a function of the considered intensity measure. We focus the analysis on the dynamic response of an anchored steam line and of a supporting structure under seismic solicitations. The failure criterion is linked to the exceedance of the maximum equivalent stress at a given location of the steam line. A series of three-component ground-motion records (∼300) were applied at the base of the model to perform non-linear time history analyses. The set of numerical results was then used to derive a FC, which relates the failure probability to the variation in peak ground acceleration (PGA). The probabilistic model of the FC is selected via information criteria completed by diagnostics on the residuals, which support the choice of the generalised extreme value (GEV) distribution (instead of the widely used log-normal model). The GEV distribution is here non-stationary, and the relationships of the GEV parameters (location, scale and shape) are established with respect to PGA using smooth non-linear models. The procedure is data-driven, which avoids the introduction of any a priori assumption on the shape or form of these relationships. To account for the uncertainties in the mechanical and geometrical parameters of the structures (elastic stiffness, damping, pipeline thicknesses, etc.), the FC is further constructed by integrating these uncertain parameters. A penalisation procedure is proposed to set to zero the variables of little influence in the smooth non-linear models. This enables us to outline which of these parametric uncertainties have negligible influence on the failure probability as well as the nature of the influence (linear, non-linear, decreasing, increasing, etc.) with respect to each of the GEV parameters.

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Section: Discussion and Further Workmentioning

confidence: 99%

Non-stationary extreme value analysis applied to seismic fragility assessment for nuclear safety analysis

Rohmer¹,

Gehl²,

Marcilhac-Fradin³

et al. 2020

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Bayesian techniques within framework of GAMLSS (Umlauf et al, 2018) are promising options to overcome this problem. Finally, combining the proposed penalisation with tools of global sensitivity analysis (like Borgonovo et al, 2013) is worth testing in future work to improve the interpretability of the procedure.…”

Section: Discussionmentioning

confidence: 99%

Non-stationary extreme value analysis applied to seismic fragility assessment for nuclear safety analysis

Rohmer¹,

Gehl²,

Marcilhac-Fradin³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. Fragility curves (FC) are key tools for seismic probabilistic safety assessments that are performed at the level of the nuclear power plant (NPP). These statistical methods relate the probabilistic seismic hazard loading at the given site and the required performance of the NPP safety functions. In the present study, we investigate how the tools of non-stationary extreme value analysis can be used to model in a flexible manner the tail behaviour of the engineering demand parameter as a function of the considered intensity measure. We focus the analysis on the dynamic response of an anchored steam line and of a supporting structure under seismic solicitations. The failure criterion is linked to the exceedance of the maximum equivalent stress at a given location of the steam line. A series of three-component ground-motion records (~ 300) were applied at the base of the model to perform non-linear time history analyses. The set of numerical results was then used to derive a FC, which relates the failure probability to the variation of peak ground acceleration (PGA). The probabilistic model of the FC is selected via information criteria completed by diagnostics on the residuals, which support the choice of the generalized extreme value GEV distribution (instead of the widely used log-normal model). The GEV distribution is here non-stationary and the relationships of the GEV parameters (location, scale and shape) are established with respect to PGA using smooth non-linear models. The procedure is data-driven, which avoids the introduction of any a priori assumption on the shape/form of these relationships. To account for the uncertainties in the mechanical and geometrical parameters of the structures (elastic stiffness, damping, pipeline thicknesses, etc.), the FC is further constructed by integrating these uncertain parameters. A penalisation procedure is proposed to set to zero the variables of little influence in the smooth non-linear models. This enables us to outline which of these parametric uncertainties have negligible influence on the failure probability as well as the nature of the influence (linear, non-linear, decreasing, increasing, etc.) with respect to each of the GEV parameters.

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“…In literature, global methods have been used (a) to conduct “SA” for the input parameters at the level of failure mechanisms with respect to the physical performance outputs 32,44–65 or the component failure probabilities 66,67 and (b) to conduct “IM analysis” for the PRA basic events (i.e. at the level of components) with respect to the system risk.…”

Section: Review Of Existing Sensitivity and Im Analysis Methods In Pramentioning

confidence: 99%

Global importance measure methodology for integrated probabilistic risk assessment

Sakurahara

Reihani

Kee

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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An integrated probabilistic risk assessment framework combines spatio-temporal probabilistic simulations of underlying failure mechanisms with classical probabilistic risk assessment logic consisting of event trees and fault trees. The importance measure methods commonly used in classical probabilistic risk assessment, for example, Fussell–Vesely importance measure and Risk Achievement Worth, generate the ranking of components at the basic event level utilizing the one-at-a-time and local methods. These classical importance measure methods, however, are not sufficient for integrated probabilistic risk assessment where, in addition to the component-level risk importance ranking, the ranking of risk-contributing factors (e.g. physical design parameters) associated with the underlying failure mechanisms is desired. In this research, the global importance measure method is suggested and implemented for the integrated probabilistic risk assessment framework. The global importance measure can account for four key aspects of integrated probabilistic risk assessment, including (a) ranking of input parameters at the failure mechanism level based on the contribution to the system risk metrics, (b) uncertainty of input parameters, (c) non-linearity and interactions among input parameters inside the model, and (d) uncertainty associated with the system risk estimates; therefore, it enhances the accuracy of risk importance ranking when the risk model has a high level of non-linearity, interactions, and uncertainties. This article shows (a) qualitative justifications for the selection of the global importance measure method for the integrated probabilistic risk assessment framework and (b) quantitative proof of concept using three case studies, including two illustrative fault tree examples and one practical application of the integrated probabilistic risk assessment framework for Generic Safety Issue 191 at nuclear power plants (a sump blockage issue following a loss-of-coolant accident).

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On the importance of uncertain factors in seismic fragility assessment

Cited by 37 publications

References 23 publications

Non-stationary extreme value analysis applied to seismic fragility assessment for nuclear safety analysis

Non-stationary extreme value analysis applied to seismic fragility assessment for nuclear safety analysis

Non-stationary extreme value analysis applied to seismic fragility assessment for nuclear safety analysis

Global importance measure methodology for integrated probabilistic risk assessment

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