Application of Particle Swarm Algorithm in Nanoscale Damage Detection and Identification of Steel Structure

Abstract

In order to identify the damage of a grid structure, the author proposes a damage identification method for grid structures based on particle swarm optimization. First, using the Modal Assurance Criterion (MAC) and combining the respective advantages of frequency and mode shape in damage identification, a fitness function based on frequency and mode shape is constructed. Second, two test functions are used to compare and analyze, which shows that the improved particle swarm algorithm has better optimization performance. Finally, through an example of a grid structure simulation model, the effectiveness of the proposed method is verified. Three working conditions were set up for damage identification, and the results were analyzed. Results. The particle swarm calculation of case 1 converged in the 53rd generation, and case 2 completed the convergence in the 67th generation, which is consistent with the actual situation. Condition 3 completed the convergence at the 125th generation, indicating that the fitness value has been decreasing until then, and it does not change after the convergence, indicating that the convergence is good. The damage degree of No. 92 member was identified as 95.69%, and the damage degree of No. 105 member was 96.28%. Basically, this is in line with the actual situation. Conclusions. The damage identification method based on the particle swarm algorithm can accurately identify the location and degree of damage.

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Retracted: Application of Particle Swarm Algorithm in Nanoscale Damage Detection and Identification of Steel Structure

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”