Structural damage identification method based on glowworm group intelligent algorithm

Abstract

The invention puts forward a structural damage identification method based on a glowworm group intelligent algorithm. The structural damage identification method comprises the following specific steps: utilizing finite element modeling to obtain the overall quality matrix and the global stiffness matrix of a structure, and furthermore, obtaining the frequency and the modal parameter of the structure; through a frequency residual error and modal guaranteeing criterion, constructing a target function, i.e., a target function to be optimized, of a damage structure; and utilizing the glowworm group intelligent algorithm to find an optimized optimal solution. The method utilizes the glowworm group intelligent algorithm, adopts frequency domain data to research structural damage identification, converts a damage problem into a target function optimization problem and only needs to obtain the frequency and the modal parameters of the first multiple orders of the structure to be detected to obtain a damage identification result. The method has the advantages of high identification precision and the like and does not depend on an initial value.

Description

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AI Technical Summary

Benefits of technology

This technology uses both time-domain and space-frequency analysis techniques for identifying damaged structures caused by environmental factors like weather or other natural causes. It can be applied without any external input from experts who are skilled at analyzing these signals accurately. Its benefits include improved accuracy over previous methods while also being more efficient than current methodologies due to its ability to optimize objectives based upon specific variables such as temperature or pressure levels during construction processes.

Problems solved by technology

This patented technical problem addressed in this patents relates to identifying structural degradation caused by environmental factors like corrosion over longer periods without stopping building new constructions until they can get through their lifespan. Current techniques require expensive instrumentations that cannot detect early signs of deteriorating conditions accurately enough due to limitations associated with current technologies used for assessment purposes.

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