Irmela Zentner

On the importance of uncertain factors in seismic fragility assessment

This paper addresses the definition of importance measures for helping the modeller to detect the factors on which to focus modelling activity and data collection in seismic fragility analysis. We study sensitivity measures consistent with the decision-support criteria of interest, namely, the (mean) fragility curve and the “High Confidence of Low Probability of Failure†(HCLPF) value. The importance measures are obtained analytically for the EPRI safety factor method, which is nowadays used worldwide for seismic risk assessment of nuclear plants. We illustrate and discuss the use of both variance-based and CDF-based importance measures in the application to two case studies, the first analytical and based on the EPRI method, the second numerical.

Sensitivity analysis for reliable design verification of nuclear turbosets

In this paper, we present an application of sensitivity analysis for design verification of nuclear turbosets. Before the acquisition of a turbogenerator, energy power operators perform independent design assessment in order to assure safe operating conditions of the new machine in its environment. Variables of interest are related to the vibration behaviour of the machine: its eigenfrequencies and dynamic sensitivity to unbalance. In the framework of design verification, epistemic uncertainties are preponderant. This lack of knowledge is due to inexistent or imprecise information about the design as well as to interaction of the rotating machinery with supporting and sub-structures. Sensitivity analysis enables the analyst to rank sources of uncertainty with respect to their importance and, possibly, to screen out insignificant sources of uncertainty. Further studies, if necessary, can then focus on predominant parameters. In particular, the constructor can be asked for detailed information only about the most significant parameters.

Simulation of non-stationary conditional ground motion fields in the time domain

This paper addresses the topic of simulating spatially variable ground motion fields conditioned on a known accelerogram. The conditional ground motion fields can be used in design or verification studies where seismic analysis has to be performed for a couple of natural accelerograms that have been preselected by seismologists or other experts. The methodology is based on conditional densities. In contrast to most authors, the conditional densities method is not applied to the Fourier coefficients, but it is used for the construction of a conditional Gaussian process model in the time domain. This has the advantage that fully non-stationary conditional time histories can be simulated directly in the time domain. The cross-correlation functions needed for this approach are evaluated from commonly used ground motion models expressed as evolutionary power spectral densities. An application to the El Centro earthquake record is presented. The properties of the simulated ground motion fields are analysed and compared to the data and the theoretical model.

Numerical methods for seismic fragility analysis of structures and components in nuclear industry - Application to a reactor coolant system

In the nuclear industry, the probabilistic risk assessment approach has become the most commonly used methodology for evaluating the seismic risk of nuclear plants. In this framework, fragility curves express the conditional probability of failure of a structure or component for a given seismic input motion parameter. In engineering practice, fragility curves are determined in a simplified way using safety factors with respect to design earthquake. For critical components, however, it can be important to have a more accurate evaluation of seismic fragility based on numerical simulation. In this paper, we will present a complete probabilistic study of a nuclear power plant component based on Monte Carlo simulation and evaluate fragility curves. Moreover, we discuss different methods available for statistical estimation of fragility curves and comment on the choice of the ground motion parameter. Indeed, choosing an appropriate ground motion parameter can reduce uncertainty.

Sensitivity of the Stochastic Response of Structures Protected by the Vibrating Barrier Control Device

The sensitivity of the stochastic response of a novel passive control device named Vibrating Barrier (ViBa) developed for reducing the seismic response of structures to earthquake excitation is scrutinized. The Vibrating Barrier (ViBa) is a massive structure, hosted in the soil, calibrated for protecting structures by exploiting the structure-soil-structure interaction effect. The soil is modelled as a linear elastic medium with hysteretic damping by resorting to the Boundary Element Method in the frequency domain. In order to accomplish efficient sensitivity analyses, a reduced model is determined by means of the Craig-Bampton procedure. Moreover, a lumped parameter model is used for converting the hysteretic damping soil model rigorously valid in the frequency domain to the approximately equivalent viscous damping model in order to perform conventional time-history analysis. The sensitivity is evaluated by determining a semi-analytical method based on the dynamic modification approach for the case of multi-variate stochastic input process. A non-stationary zero mean Gaussian random process is considered as stochastic input. The paper presents the sensitivity of the maximum response statistics to the design parameters of the ViBa in protecting a model of an Industrial Building. Comparisons with pertinent Monte Carlo Simulation will show the effectiveness of the proposed approach.

Generation of artificial earthquake accelerograms compatible with mean and mean ± standard deviation

The sustained dissemination of database of recorded accelerograms along with the increasing number of strong-motion networks installed worldwide revealed that the current methodologies for simulating artificial earthquakes possess the drawback that the simulated time-histories do not manifest the variability observed for natural accelerograms. As a consequence, the dispersion of resulting structural response analysis can be underestimated. In order to take into account the natural variability of earthquakes, a methodology for simulating artificial earthquake accelerograms matching mean and mean ± standard deviation response spectra is proposed in this paper. This dispersion can be determined from attenuation relationships or evaluated from selected accelerograms of a strong-motion database. The procedure requires the definition of an evolutionary response-spectrum-compatible power spectral density function with random parameters. The simulated ground motion time-histories will manifest variability so that one observed in natural records.