Structural fragility and principle of maximum entropy

Goodman, J.

doi:10.1016/0167-4730(85)90006-2

Cited by 17 publications

(9 citation statements)

References 8 publications

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“…This decrease results from differences in the characteristic of the lower tails of the Weibull and Iognormal distributions. One might expect to see similar sensitivities of HCLPF values if other fragility models suggested in the literature (e.g., Goodman, 1985) were used. Such differences may be important in a seismic margins study such as that for the Maine Yankee Plant (Prassinos, et al, 1987), where the estimated plant HCLPF was close to the review earthquake level.…”

Section: Fragility Modelingmentioning

confidence: 89%

“…A third fragility model is the modified Iognormal distribution, sometimes referred to as a Johnson distribution (Goodman, 1985):…”

Section: Basic Fragility Modelsmentioning

confidence: 99%

“…in which Am and MR --median and c.o.v of R. As a four-parameter distribution, Eqn 3.9 requires data (or a reasonable assumption) on parameters Xmin and xmax, as well as on central value and dispersion to estimate ;L and r_. Eqn 3.8 is a "maximum entropy" distribution for a random variable in which the mean, standard deviation and range are known (Goodman, 1985). To obtain the fragility family needed to describe modeling uncertainty, one might again assume that the median is described by a Iognormal distribution, with logarithmic standard deviation _u as before.…”

Section: Basic Fragility Modelsmentioning

confidence: 99%

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Validation of seismic probabilistic risk assessments of nuclear power plants

Ellingwood

1994

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A seismic probabilistic risk assessment (PRA) of a nuclear plant requires identification and information regarding the seismic hazard at the plant site, dominant accident sequences leading to core damage, and structure and equipment fragilities. Uncertainties are associated with each of these ingredients of a PRA. Sources of uncertainty due to seismic hazard and assumptions underlying the component fragility modeling may be significant contributors to uncertainty in estimates of core damage probability. Design and construction errors also may be important in some instances. When these uncertainties are propagated through the PRA, the frequency distribution of core damage probability may span three orders of magnitude or more. This large variability brings into question the credibility of PRA methods and the usefulness of insights to be gained from a PRA. List of Tables vii List of Figures ix 6.0 Role of error in safety evaluation 103 6.1 Design/construction errors 103 6.2 Significance of error for nuclear plant safety 104 6.3 Sensitivity of risk estimates to postulated errors 107 6.3.1 Limerick generating station 108 6.3.2 Millstone 3 109 6.4 Summary 110 7.0 Conclusions and recommendations 113 8.0 Acknowledgements 121 9.0 References vi Tables 2.1(a) Component and seismic fragility parameters-Limerick 6 2.1(b) Nonseismic random failure rates-Limerick 7 2.2(a) Component and seismic fragility parameters-Millstone 3 2.2(b) Nonseismic random failure rates-Millstone 3 8 2.3(a) Component and seismic fragility parameters-Maine Yankee 9 2.3(b) Nonseismic random failure rates-Maine Yankee 9 A3.1 Sampling distribution of MLE Y1 A3.2 Sampling distribution of Y2-(XLXu) 1/2 A3.3

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Section: Fragility Modelingmentioning

confidence: 89%

“…A third fragility model is the modified Iognormal distribution, sometimes referred to as a Johnson distribution (Goodman, 1985):…”

Section: Basic Fragility Modelsmentioning

confidence: 99%

Section: Basic Fragility Modelsmentioning

confidence: 99%

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Validation of seismic probabilistic risk assessments of nuclear power plants

Ellingwood

1994

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“…. Average values are calculated as shown: x = -0.99, y = -1.05, according to (11) and (12) This section defines the procedures for deriving fragility parameters (θ, β) when data are available from a suitable series of specimens, however, the damage state of interest was not initiated in any of the specimens. For each available specimen, "i," the maximum demand at which the specimen was loaded, "d i ", and whether or not the specimen experienced any distress or damage must be known.…”

Section: Methods B Bounding Demand Datamentioning

confidence: 99%

“…It has strong precedent in seismic risk analysis (e.g., [Kennedy and Short 1994]; [Kircher et al 1997]). Finally, there is a strong theoretical reason to use the lognormal: it has zero probability density at and below zero EDP, is fully defined by measures of the first and second moments-ln(x m ) and β-and imposes the minimum information given these constraints, in the information-theory sense (Goodman 1985). Figure 1(a) shows the form of a typical fragility function when plotted in the form of a cumulative distribution function; and (b), the calculation of the probability that a component will be in damage state "i" at a particular level of demand, d.…”

Section: Fragility Function Definitionmentioning

confidence: 99%