Buckling curves of stainless steel CHS members: Current state and proposed provisions

Jindra, Daniel; Kala, Zdeněk; Kala, Jiří

doi:10.1016/j.jcsr.2022.107521

Cited by 3 publications

(3 citation statements)

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“…Suppose the increase in entropy is similar for similar model case, the increase in entropy can be a general rule for identifying model uncerta random variables in a stochastic model. The issue is the quantification of such an i Let us focus on increasing the variance up to the entropy value according to E (12). Why this entropic approach?…”

Section: Calibration Of Models Of Uncertaintiesmentioning

confidence: 99%

“…Let us focus on increasing the variance up to the entropy value according to Equation (12). Why this entropic approach?…”

Section: Calibration Of Models Of Uncertaintiesmentioning

confidence: 99%

“…Since then, ROSAs have been applied in studies of the structural reliability of steel [5,6], concrete [7] and composite [8] beams. The concept [3,4] builds on Sobol [9,10], but with its focus on P f , it surpasses classical methods examining the correlation [11,12], variance [13,14] or distribution [15,16] of model output without a clear relationship to structural reliability.…”

Section: Introductionmentioning

confidence: 99%

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Quantification of Model Uncertainty Based on Variance and Entropy of Bernoulli Distribution

Kala

2022

Mathematics

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This article studies the role of model uncertainties in sensitivity and probability analysis of reliability. The measure of reliability is failure probability. The failure probability is analysed using the Bernoulli distribution with binary outcomes of success (0) and failure (1). Deeper connections between Shannon entropy and variance are explored. Model uncertainties increase the heterogeneity in the data 0 and 1. The article proposes a new methodology for quantifying model uncertainties based on the equality of variance and entropy. This methodology is briefly called “variance = entropy”. It is useful for stochastic computational models without additional information. The “variance = entropy” rule estimates the “safe” failure probability with the added effect of model uncertainties without adding random variables to the computational model. Case studies are presented with seven variants of model uncertainties that can increase the variance to the entropy value. Although model uncertainties are justified in the assessment of reliability, they can distort the results of the global sensitivity analysis of the basic input variables. The solution to this problem is a global sensitivity analysis of failure probability without added model uncertainties. This paper shows that Shannon entropy is a good sensitivity measure that is useful for quantifying model uncertainties.

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Section: Calibration Of Models Of Uncertaintiesmentioning

confidence: 99%

“…Let us focus on increasing the variance up to the entropy value according to Equation (12). Why this entropic approach?…”

Section: Calibration Of Models Of Uncertaintiesmentioning

confidence: 99%