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

Abstract

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.

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

“…By the maximum likelihood method, we can provide estimates for the unknown covariance function hyperparameters σ, pρ i q 1ďiďd`1 and also the Gaussian noise variance σ ε (see [27] for a practical implementation of the method). The dataset D n can then be used to derive the conditional distribution of the Gaussian process Y for any pa, xq: pY pa, xq|D n q " N `mn pa, xq, σ n pa, xq 2 ˘, (5) where m n pa, xq and σ n pa, xq 2 are obtained from the kriging equations [23, p.16 -17]. In the same fashion, we can derive the conditional distribution of the Gaussian process G on the regression function for any pa, xq:…”

“…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].…”

“…The physics-based approaches developed as part of Performance-Based Earthquake Engineering (PBEE) address this problem [19]. However, they are not suitable when the use of detailed finite element simulations is required, in order to take into account all the specificities of the structures of interest as it can be the case nowadays for the seismic safety studies in nuclear industry [5,20,21]. So, in this paper, our approach relies on the use of surrogate models of the computer codes, also referenced as metamodels, based on Gaussian process regression.…”

Influence of the Choice of the Seismic Intensity Measure on Fragility Curves Estimation in a Bayesian Framework Based on Reference Prior

“…In practice, various data sources can be exploited to estimate fragility curves, namely: expert judgments supported by test data [1][2][3]6], experimental data [3,7,8], results of damage collected on existing structures that have been subjected to an earthquake [9][10][11] and analytical results given by more or less refined numerical models using artificial or real seismic excitations (see e.g. [12][13][14][15][16][17]). Parametric fragility curves were historically introduced in the SPRA framework because their estimates require small sample sizes.…”

“…The log-normal model has since become the most widely used model (see e.g. [9][10][11][12][13][14][15][16][17][18][19][20][21][22]). Several strategies can be implemented to fit the median, α, and the log standard deviation, β, of the model.…”