Lorentz attractor excitation-based structural damage identification using state space curvature reconstruction- enhanced transformer
Autor(en): |
Xian Wang
Zengying You Xiaoqing Pu Nan Shao Jiawen Xu |
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Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Smart Materials and Structures, 18 Oktober 2024, n. 11, v. 33 |
Seite(n): | 115005 |
DOI: | 10.1088/1361-665x/ad7e85 |
Abstrakt: |
Vibration-based structural damage identification has been widely investigated. Different from previous studies that analyze vibrational responses in time and frequency domains, a new Lorentz attractor excitation-based damage identification is becoming a novel strategy with the advantage of capturing the structure’s nonlinear dynamic effects. In this study, Lorentz attractor-based chaotic signals were employed as excitation signals for the structural damage identification of a frame structure. Nonlinear responses were recorded and damages of bolt looseness at different locations were considered. The structural damages could be revealed in the state-space plot of the responses. A state space curvature reconstruction method was introduced to enhance the key features of the nonlinear responses. A small-sample damage identification is performed using a deep learning algorithm—a transformer with an accuracy of 92.38%. The advantages of the proposed method over conventional deep learning algorithms were validated. The proposed method can be applied to health conditions identification of buildings, bridges, and trusses. |
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Datenseite - Reference-ID
10801388 - Veröffentlicht am:
10.11.2024 - Geändert am:
10.11.2024