Margin and uncertainty quantitative structure reliability evaluation method based on fuzzy random parameters

Abstract

The invention provides a margin and uncertainty quantitative structure reliability evaluation method based on fuzzy random parameters. The margin and uncertainty quantitative structure reliability evaluation method comprises the following steps of: 1, determining a fuzzy random variable and a confidence coefficient gamma of a structure, and initializing a cut set alpha to be equal to 0; 2, constructing envelope distribution of each variable under the current cut set; 3, according to the envelope distribution, sampling to obtain first four-order center distances of the envelope distribution; 4,according to a structure response function and the first four-order center distances of envelope distribution, deriving to obtain first four-order center distances of a structure response boundary; 5, fitting a structure response boundary distribution expression according to the first four-order center distance of the structure response boundary; 6, according to the confidence coefficient and thestructure response boundary distribution expression, calculating the margin M alpha and the response uncertainty U alpha under the alpha cut set; 7, calculating a confidence coefficient CF alpha = Malpha / U alpha under the alpha cut set; judging whether the alpha is smaller than 1, if so, enabling an increment alpha to be equal to alpha+delta alpha, and jumping to the step 2, otherwise, carryingout the step 8; and 8, acquiring a structural overall reliability evaluation result CF=min{CF alpha}, wherein the alpha belongs to [0, 1].

Description

technical field [0001] The present invention relates to a method for assessing the reliability of a structure based on fuzzy random parameters for Qualification and Margin of Uncertainty (QMU). Carrying out reliability assessment belongs to the field of structural reliability analysis. Background technique [0002] In the first aspect, for complex systems or structures with insufficient statistical data and small sample size, it is difficult to calculate and characterize their reliability by traditional reliability calculation methods. After the United States stopped nuclear weapon system-wide tests in 1992, due to the limitations of experimental data and uncertainties, the military needed a reliability assessment method that does not depend on tests and is based on cognitive uncertainties. The reliability evaluation method of deterministic quantification (QMU) came into being. Los Alamos National Laboratory, Lawrence Livermore National Laboratory and Sandia National Labora...

AI Technical Summary

Problems solved by technology

However, in the QMU theoretical system currently studied, there is a lack of a method that simultaneously considers the inherent random distribution and the cognitive uncertainty based on the distribution parameters. In engineering practice, the statistics of some design parameters are completely mastered, while some data is not complete, so situations involving two types of information are widespread

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