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Feature Selection Using Tree Model and Classification Through Convolutional Neural Network for Structural Damage Detection

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Abstract

Structural damage detection (SDD) remains highly challenging, due to the difficulty in selecting the optimal damage features from a vast amount of information. In this study, a tree model-based method using decision tree and random forest was employed for feature selection of vibration response signals in SDD. Signal datasets were obtained by numerical experiments and vibration experiments, respectively. Dataset features extracted using this method were input into a convolutional neural network to determine the location of structural damage. Results indicated a 5% to 10% improvement in detection accuracy compared to using original datasets without feature selection, demonstrating the feasibility of this method. The proposed method, based on tree model and classification, addresses the issue of extracting effective information from numerous vibration response signals in structural health monitoring.

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Acknowledgements

This research is supported by the Project of Guangdong Province High Level University Construction for Guangdong University of Technology (Grant No. 262519003) and the College Student Innovation Training Program of Guangdong University of Technology (Grant Nos. S202211845154 and xj2023118450384).

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Authors and Affiliations

  1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, 510006, China

    Zihan Jin, Jiqiao Zhang, Qianpeng He, Silang Zhu, Tianlong Ouyang & Gongfa Chen

  2. Guangdong Tianxin Electric Power Engineering Testing Co., Ltd, Guangzhou, 510663, China

    Zihan Jin

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  1. Zihan Jin
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  3. Qianpeng He
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  4. Silang Zhu
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Correspondence to Jiqiao Zhang.

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Jin, Z., Zhang, J., He, Q. et al. Feature Selection Using Tree Model and Classification Through Convolutional Neural Network for Structural Damage Detection. Acta Mech. Solida Sin. (2024). https://doi.org/10.1007/s10338-024-00491-7

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  • DOI: https://doi.org/10.1007/s10338-024-00491-7

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