Damage identification of building structures based on multi-channel one-dimensional convolutional neural networks

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (24) : 216-224.

XIONG Qingsong1,2,3,XIONG Haibei1,2,YUAN Cheng4,CHEN Lin1,2,KONG Qingzhao1,2

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Abstract

In addressing the paradigm of acquiring monitoring data in practical engineering for building structures and the limitations of current data-driven algorithms, a building structure damage identification algorithm based on a multi-channel one-dimensional convolutional neural network (1D CNN) is proposed. This method merges feature extraction from vibration data of various building floors, implicitly utilizes multi-channel 1D CNN to extract topological relationships between monitoring points, and extracts high-dimensional damage features from signals to achieve effective identification of building structural damage. Validity of the proposed approach is verified using the IASC-ASCE benchmark model. Comparative analysis with traditional single-channel 1D CNN demonstrates a significant improvement in identification accuracy, with a predictive accuracy reaching 0.989. Furthermore, the introduction of monitoring mechanisms during model training greatly enhances training efficiency. Comparative analysis indicates that the proposed multi-channel model architecture converges faster during training and exhibits more stable extraction of structural damage features.

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damage identification / one-dimensional conv

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XIONG Qingsong1, 2, 3, XIONG Haibei1, 2, YUAN Cheng4, CHEN Lin1, 2, KONG Qingzhao1, 2. Damage identification of building structures based on multi-channel one-dimensional convolutional neural networks[J]. Journal of Vibration and Shock, 2024, 43(24): 216-224

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