期刊
MEASUREMENT SCIENCE AND TECHNOLOGY
卷 33, 期 10, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1361-6501/ac7940
关键词
structural damage detection; decision-level fusion; convolutional neural network; vibration signal
资金
- Program of Study Abroad for Young Scholars in Guangdong University of Technology [220410009]
This study proposes a structural damage detection method based on decision-level fusion with multi-vibration signals, which extracts damage information from the vibration signals of a structure using convolutional neural networks, and improves accuracy by fusing detection results from multiple CNN models.
When a structure is damaged, its vibration signals change. If a single vibration signal is used for structural damage detection (SDD), it may sometimes lead to low detection accuracy. To avoid this phenomenon, this paper presents a SDD method based on decision-level fusion (DLF) with multi-vibration signals. In this study, acceleration (ACC), strain (E), displacement (DIS), and the fusion signal of all three of these signals (ACC, E and DIS), are studied. The damage information can be extracted from the vibration signal of a structure by using convolution neural networks (CNN). The above four vibration signals are used as the inputs to train four CNN models, and each model outputs a corresponding result. Finally, a DLF strategy is used to fuse the detection results of each CNN. To demonstrate the effectiveness and correctness of the proposed method, a steel frame bridge is investigated with numerical simulations and vibration experiments. The research shows that the damage detection method based on DLF with multi-vibration signals can effectively improve the accuracy of the CNN damage detection.
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