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作者:

Zhang, Longguan (Zhang, Longguan.) | Jia, Junfeng (Jia, Junfeng.) (学者:贾俊峰) | Bai, Yulei (Bai, Yulei.) | Du, Xiuli (Du, Xiuli.) | Guo, Binli (Guo, Binli.) | Guo, He (Guo, He.)

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Scopus SCIE

摘要:

The accurate assessment of the effective pre-stress in steel strands is a challenging task, and ultrasonic guided wave (UGW) technique has shown certain application prospects in this field. However, the existing UGW-based approaches require manual parameter extraction from signals in time domain or frequency domain, which is a cumbersome and time-consuming process, and pre-stress identification based on individual parameters may not be reasonable. This study proposes a framework for identifying effective pre-stress in steel strands based on UGW and one-dimensional convolutional neural network (1D-CNN), which does not require any parameter extraction operation and achieves high identification accuracy. The output features of various convolutional layers in 1D-CNN were downscaled and visualized, and the prediction results of 1D-CNN were compared with those of a support vector regression (SVR) model. Results show that with the deepening of the network, the correlation between output features of the convolutional layers and pre-stress values increases significantly, indicating that the 1D-CNN model is able to automatically extract features related to the variation of pre-stress. The pre-stress prediction accuracy using 1D-CNN is significantly higher than that using SVR, and the prediction error is within 3%. The proposed 1D-CNN model exhibits excellent noise-robustness, with the prediction error remaining within 10% even at the SNR level of -5 dB. Even after removing half of conditions in the training set, the proposed 1D-CNN model is still able to achieve accurate identification of effective pre-stress.

关键词:

steel strand 1D-CNN feature visualization pre-stress ultrasonic guided wave SHM

作者机构:

  • [ 1 ] [Zhang, Longguan]Beijing Univ Technol, State Key Lab Bridge Safety & Resilience, Beijing 100124, Peoples R China
  • [ 2 ] [Jia, Junfeng]Beijing Univ Technol, State Key Lab Bridge Safety & Resilience, Beijing 100124, Peoples R China
  • [ 3 ] [Bai, Yulei]Beijing Univ Technol, State Key Lab Bridge Safety & Resilience, Beijing 100124, Peoples R China
  • [ 4 ] [Du, Xiuli]Beijing Univ Technol, State Key Lab Bridge Safety & Resilience, Beijing 100124, Peoples R China
  • [ 5 ] [Guo, Binli]CCCC Infrastruct Maintenance Grp Co Ltd, Beijing, Peoples R China
  • [ 6 ] [Guo, He]CCCC Rd & Bridge Inspect & Maintenance Co LTD, Beijing, Peoples R China

通讯作者信息:

  • [Jia, Junfeng]Beijing Univ Technol, State Key Lab Bridge Safety & Resilience, Beijing 100124, Peoples R China;;

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来源 :

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL

ISSN: 1475-9217

年份: 2024

6 . 6 0 0

JCR@2022

被引次数:

WoS核心集被引频次: 3

SCOPUS被引频次: 1

ESI高被引论文在榜: 0 展开所有

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中文被引频次:

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