Parametric models averaging for optimized non-parametric fragility curve estimation based on intensity measure data clustering

Trevlopoulos, Konstantinos; Feau, Cyril; Zentner, Irmela

doi:10.1016/j.strusafe.2019.05.002

Cited by 21 publications

(10 citation statements)

References 33 publications

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“…The validity of the lognormal assumption can be criticized and different methods can be used to determine the parameters of the lognormal model (see e.g. Baker 2015;Mai et al 2017;Trevlopoulos et al 2019). The study of the influence of these assumptions on the results presented here is out of the scope of this work, nevertheless the trends observed in Fig.…”

Section: Fragility Curves Assessmentmentioning

confidence: 95%

Influence of Input Motion's Control Point Location in Nonlinear SSI Analysis of Equipment Seismic Fragilities: Case Study on the Kashiwazaki-Kariwa NPP

Wang

Feau

2020

Pure Appl. Geophys.

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The aim of this case study is to evaluate the influence of input seismic motion control point on the fragility curves of some nuclear power plant's equipment considering (1) a strong soil-structure interaction problem and (2) the variability of the input seismic signals. To this end, a current engineering methodology was implemented for computation efficiency, based on a simplified model representing the largely embedded Unit 7 reactor building of the Kashiwazaki-Kariwa NPP (Japan). Seismic signals were generated with NGA GMPE according to the July 2007 NCOE scenario. In the process, soil non-linearity caused by each seismic signal was taken into account using the equivalent linear method. A study on the uncertainty propagation through the simplified soil-structure system is also presented. It allowed the implementation of an empirical sensitivity analysis to confirm the main results, which suggest that defining the control point of the input motion at the soil surface as prescribed in the French nuclear practice is not appropriate and may lead to biased outcomes when performing non-linear soil-structure fragility analysis. Instead, it should be defined at the ''outcropping bedrock'' level.

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Section: Fragility Curves Assessmentmentioning

confidence: 95%

Influence of Input Motion's Control Point Location in Nonlinear SSI Analysis of Equipment Seismic Fragilities: Case Study on the Kashiwazaki-Kariwa NPP

Wang

Feau

2020

Pure Appl. Geophys.

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“…Fragility analysis and, more broadly, seismic risk assessment based on stochastic simulations is not new, and an incomplete list of studies includes [11,12,13,14]. Moreover, to the best of our knowledge, the first study using also kriging surrogate modeling is [15] and more recently [16].…”

Section: R a F Tmentioning

confidence: 99%

Seismic fragility analysis based on artificial ground motions and surrogate modeling of validated structural simulators

Abbiati

Broccardo

Abdallah

et al. 2021

Earthq Engng Struct Dyn

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This study introduces a computational framework for efficient and accurate seismic fragility analysis based on a combination of artificial ground motion modeling, polynomial-chaos-based global sensitivity analysis, and hierarchical kriging surrogate modeling. The framework follows the philosophy of the Performance-Based Earthquake Engineering PEER approach, where the fragility analysis is decoupled from hazard analysis. This study addresses three criticalities that are present in the current practice. Namely, reduced size of hazardconsistent size-specific ensembles of seismic records, validation of structural simulators against large-scale experiments, high computational cost for accurate fragility estimates. The effectiveness of the proposed framework is demonstrated for the Rio Torto Bridge, recently tested using hybrid simulation within the RETRO project.

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“…When little data is available, whether experimental, from post-earthquake feedback or from numerical calculations, a classic approach to circumvent estimation difficulties is to use a parametric model of the fragility curve, such as the lognormal model historically introduced in [1] (see e.g. [5,6,7,8,9,10]). As the validity of parametric models is questionable, non-parametric estimation techniques have also been developed, such as kernel smoothing [8,9] as well as other methodologies [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…[5,6,7,8,9,10]). As the validity of parametric models is questionable, non-parametric estimation techniques have also been developed, such as kernel smoothing [8,9] as well as other methodologies [10,11]. Most of these strategies are compared in [8,9,12] and [8] presents their advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty quantification and global sensitivity analysis of seismic fragility curves using kriging

Gauchy¹,

Feau²,

Garnier³

2022

Preprint

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Seismic fragility curves have been introduced as key components of Seismic Probabilistic Risk Assessment studies. They express the probability of failure of mechanical structures conditional to a seismic intensity measure and must take into account the inherent uncertainties in such studies, the so-called epistemic uncertainties (i.e. coming from the uncertainty on the mechanical parameters of the structure) and the aleatory uncertainties (i.e. coming from the randomness of the seismic ground motions). For simulation-based approaches we propose a methodology to build and calibrate a Gaussian process surrogate model to estimate a family of non-parametric seismic fragility curves for a mechanical structure by propagating both the surrogate model uncertainty and the epistemic ones. Gaussian processes have indeed the main advantage to propose both a predictor and an assessment of the uncertainty of its predictions. In addition, we extend this methodology to sensitivity analysis. Global sensitivity indices such as aggregated Sobol indices and kernel-based indices are proposed to know how the uncertainty on the seismic fragility curves is apportioned according to each uncertain mechanical parameter. This comprehensive Uncertainty Quantification framework is finally applied to an industrial test case consisting in a part of a piping system of a Pressurized Water Reactor.

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Parametric models averaging for optimized non-parametric fragility curve estimation based on intensity measure data clustering

Cited by 21 publications

References 33 publications

Influence of Input Motion's Control Point Location in Nonlinear SSI Analysis of Equipment Seismic Fragilities: Case Study on the Kashiwazaki-Kariwa NPP

Influence of Input Motion's Control Point Location in Nonlinear SSI Analysis of Equipment Seismic Fragilities: Case Study on the Kashiwazaki-Kariwa NPP

Seismic fragility analysis based on artificial ground motions and surrogate modeling of validated structural simulators

Uncertainty quantification and global sensitivity analysis of seismic fragility curves using kriging

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