Multi-Role collaborative framework for structural damage identification considering measurement noise effect

•A novel Multi-Role Collaborative Framework is proposed to enhance evolutionary algorithms.•Two population-based updating mechanisms are adaptively and efficiently switched;•Structural damage on the Guangzhou New TV Tower is accurately identified using limited data and evaluation iterations;•Multipl...

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Veröffentlicht in:Measurement : journal of the International Measurement Confederation Jg. 250; S. 117106
Hauptverfasser: Chen, Zepeng, Zhang, Zhiyu, Chen, Xiangmei, Hou, Rongrong, Ding, Zhenghao, Liu, Feng, Yang, Zhicheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 15.06.2025
Schlagworte:
Multi-role collaborative framework
Noise effect
Seagull optimization algorithm
Sine cosine algorithm
Sparse regularization
Seagull optimization algorithm
Sine cosine algorithm
Sparse regularization
Multi-role collaborative framework
Noise effect
ISSN:0263-2241
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Zusammenfassung:•A novel Multi-Role Collaborative Framework is proposed to enhance evolutionary algorithms.•Two population-based updating mechanisms are adaptively and efficiently switched;•Structural damage on the Guangzhou New TV Tower is accurately identified using limited data and evaluation iterations;•Multiple notches in a laboratory box-section beam are successfully detected with high precision. Swarm intelligence has been extensively applied in structural damage identification, but a single method may not perform well in identification, especially using limited and noised vibration data. In this context, the objective landscape of the formulated identification problem is often ill-posed, indicating the optimized landscape is filled with many local optimal points. If the algorithm gets trapped in local optimal points, it will not obtain satisfactory identification results. To address this issue, this study introduces the sparse regularization technique to construct a well-posed objective function. Furthermore, a novel multi-role collaborative framework is proposed, which integrates different swarm intelligent and enables the individual in the algorithm to switch different roles, meaning employing different updating strategies, for the demands of different identification cases. Therefore, a more accurate identification results can be obtained. A series of numerical simulations and a laboratory validation on a box-section beam with multiple notches are carried out. The features of multi-role adaptive mechanism and diversity search strategies in the proposed framework guarantee its advantages and superiority on obtaining better identifications compared with single swarm intelligence algorithm, providing a new way in developing high-efficiency model updating and damage detection algorithms.
ISSN:0263-2241
DOI:10.1016/j.measurement.2025.117106