Dynamic detection and identification method for bridge structure defects of bridge crane

Abstract

The invention discloses a dynamic detection and identification method for bridge structure defects of a bridge crane. The method comprises the following steps: determining geometric dimensions, material types and connection supporting conditions of all components of the to-be-tested bridge crane; establishing an initial finite element model of the to-be-tested bridge crane; setting material performance parameters of each component in the initial finite element model; setting boundary conditions of all components in the initial finite element model; carrying out theoretical modal analysis of the to-be-tested bridge crane, determining the section position where the sensor is located during dynamic testing of the to-be-tested bridge crane, recording vibration parameters of the measuring pointposition of the sensor, carrying out operation modal analysis of the to-be-tested bridge crane, determining actual operation modal parameters of the to-be-tested bridge crane, and correcting the original finite element model; and comparing the actual operation vibration parameters of the bridge crane with theoretical vibration parameters based on the corrected finite element model.

Description

technical field [0001] The invention relates to a method for dynamic detection and identification of defects, in particular to a method for dynamic detection and identification of structural defects of bridge crane bridges. Background technique [0002] The existing non-destructive testing methods of cranes in non-working state mainly include radiographic testing (RT), ultrasonic testing (UT), magnetic particle testing (MT), penetrant testing (PT) and other methods, and the testing methods in working state mainly include acoustic emission testing. (AE). Radiographic testing (RT) is a method that uses a certain type of radiation to inspect internal defects in welds. When the ray passes through the weld to be inspected, the intensity of the ray falling on the film is different due to the different absorption ability of the weld defect to the ray, and the sensitivity of the film is also different, so that it can be accurate, reliable and non-destructive. Shows the shape, loca...

AI Technical Summary

Problems solved by technology

However, although the actual measurement of the structural frequency is accurate, it is not sensitive to local changes in the structure; the mode shape, especially the high-order mode shape, is sensitive to local stiffness changes, but it is difficult to measure accurately

The model correction method mainly uses the dynamic test data to continuously correct the stiffness distribution of the structural model through condition optimization constraints, so as to obtain the information on the change of the structural stiffness, and realize the damage identification and location of the structure. Incomplete modal set and insufficient test degrees of freedom can easily lead to non-uniqueness of the solution

According to the response of the structure in different states, the neural network selects parameters sensitive to structural damage as the input vector of the network through feature extraction, and the damage state of the structure as the output, and establishes a training sample set for damage classification. The main limitation of the artificial neural network method is that The accuracy of the acquisition of the training data set is largely limited by the completeness of the training data set

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