Stochastic Model Modification Method Based on Stochastic Response Surface Estimation Parameter Uncertainty

Abstract

The invention relates to a stochastic model modification method based on uncertainty of a stochastic response surface estimated parameter, which comprises the following steps: 1, representing an uncertain parameter of a structure system as a function of a standard stochastic variable and representing a response of the structure system as a polynomial chaos expansion which uses the standard stochastic variable as an independent variable and is based on a Hermite polynomial, and solving an undetermined coefficient of the polynomial chaos expansion so as to establish a stochastic response surface model of the response of the structure system and calculate a statistical characteristic value of the response of the structure system; 2, utilizing an error function of a statistical characteristic value of the stochastic response surface model and the statistical characteristic value of the actually measured response to establish a target function required by stochastic model modification; 3, utilizing the target function to construct optimization inverse problems and modifying a parameter statistical characteristic value step by step; and 4, on the basis of the parameter statistical characteristic value obtained by stochastic modification, utilizing a stochastic response surface to calculate and obtain the statistical characteristic value of the response of the structure system. The method ensures modification accuracy when improving modification efficiency.

Description

technical field [0001] The invention relates to the technical field of structural model correction and parameter identification, in particular to a random model correction method for estimating parameter uncertainty based on a random response surface. Background technique [0002] For static and dynamic response analysis and parameter identification of complex engineering structures, an accurate and effective physical model (usually referred to as finite element model) is often essential, which is especially important in structural health monitoring and damage identification. In practical applications, the above model should not only be basically consistent with the actual structure in terms of static and dynamic response, but also be sensitive to the uncertainty of structural parameters caused by changes in the working environment or operating loads, as well as the parameters themselves due to manufacturing The uncertainty (also known as variability) caused by errors has a ...

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Problems solved by technology

It should be noted that the stochastic model correction method is actually a further deepening and expansion of the deterministic model correction theory, involving theoretical methods such as probability statistics and fuzzy sets. At present, there are very few known research results at home and abroad, and relevant research is urgently needed Theoretical research, and testing the feasibility and reliability of theoretical methods on practical structures

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