Finite element model correcting method based on reverse substructures

Abstract

The invention discloses a finite element model correcting method based on reverse substructures. The method includes the following steps of firstly, measuring a test mode of an actual engineering structure to establish the test mode of the whole structure; secondly, dividing the test mode of the whole structure into test modes of the independent substructures through the relation between the test mode parameters of the independent substructures and the test mode parameters of the whole structure; thirdly, establishing a target function on the basis of the test modes of the independent substructures, adjusting the parameters of the independent substructures, minimizing the target function, obtaining the optimal structural parameters of the substructures, and correcting finite element models of the independent substructures according to the optimal structural parameters; fourthly, completing structure damage recognition according to the changes, happening before and after structure damage, of finite model structure parameters. By means of the method, by correcting the models of the independent substructures, the models of the whole structure are prevented from being repeatedly analyzed, the accuracy and efficiency of the finite element model correcting method can be greatly improved, and high practicability is achieved.

Description

technical field [0001] The invention belongs to the technical field of civil engineering large-scale structure detection, and more specifically relates to a method for correcting a finite element model based on an inverse substructure. Background technique [0002] Accurate finite element models are an important basis for structural health assessment and damage identification. Structural health assessment relies to a certain extent on the analysis results of model correction and damage identification. The basic idea of ​​the finite element model correction method is based on the structural test data, and compares the information obtained from the test with the original finite element model analysis results. By optimizing the constraints, the physical parameters of the model (stiffness, mass, size, boundary conditions, etc.) are continuously corrected, so that the theoretical values ​​are basically consistent with the experimental values, so as to identify the physical param...

AI Technical Summary

Problems solved by technology

The size of the system matrix of the fine finite element model is large, and repeated finite element model analysis (for example: extracting eigensolutions, solving the sensitivity matrix) takes a lot of computing time

Especially in the case of many correction parameters, the calculation of the sensitivity matrix takes a long time

[0006] (3) There are a large number of material, geometric properties and boundary condition assumptions in the finite element model of large structures, and the structural parameters that need to be corrected are bound to increase. A large number of corrected parameters will easily lead to ill-conditioned or slow convergence problems in the optimization process, and the optimization results are not good.

[0007] (4) In order to effectively identify the correction parameters, the field test needs to arrange multiple measuring points to obtain sufficient analysis data, which requires high hardware performance of the data acquisition, transmission and storage system, so the cost of the health monitoring system is relatively high

These methods improve the efficiency of the model correction process by sacrificing the accuracy of the finite element model to varying degrees, and the corrected model is difficult to accurately reflect some real damage conditions of the structure

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