Damage identification method of PE sheath of bridge stay cable based on lightweight convolutional neural network

LIU Xiaoyu; HUANG Yong; XU Feng; LI Hui

doi:10.11835/j.issn.2096-6717.2022.115

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Damage identification method of PE sheath of bridge stay cable based on lightweight convolutional neural network

Civil Engineering | 更新时间：2025-11-14

- Damage identification method of PE sheath of bridge stay cable based on lightweight convolutional neural network
- “Deep neural networks and computer vision technology have made new progress in the field of structural health monitoring. Experts have proposed a lightweight region recommendation based convolutional neural network model, which effectively improves the efficiency of cable damage identification.”
- Journal of Civil and Environmental Engineering Vol. 47, Issue 1, Pages: 167-178(2025)
- 作者机构：
  
  1.上海交通大学船舶海洋与建筑工程学院,上海200240
  2.哈尔滨工业大学土木工程学院, 哈尔滨150090
- 作者简介：
  
  LIU Xiaoyu (1998- ), main research interest: structure health monitoring, E-mail: liuxiaoyu_sjtu@sjtu.edu.cn.
  HUANG Yong （corresponding author）， PhD， professor， doctorial supervisor， E-mail： huangyong@hit.edu.cn.
- 基金信息：
  
  National Key Research and Development Program(2021YFF0501003)
- DOI：10.11835/j.issn.2096-6717.2022.115
  CLC： TU997
- Received：07 July 2022，
  
  Published：25 February 2025
- 稿件说明：
移动端阅览
刘啸宇,黄永,徐峰等.基于轻量化卷积神经网络的桥梁斜拉索PE护套损伤识别方法[J].土木与环境工程学报,2025,47(01):167-178.

LIU Xiaoyu,HUANG Yong,XU Feng,et al.Damage identification method of PE sheath of bridge stay cable based on lightweight convolutional neural network[J].Journal of Civil and Environmental Engineering,2025,47(01):167-178.
刘啸宇,黄永,徐峰等.基于轻量化卷积神经网络的桥梁斜拉索PE护套损伤识别方法[J].土木与环境工程学报,2025,47(01):167-178. DOI： 10.11835/j.issn.2096-6717.2022.115.

LIU Xiaoyu,HUANG Yong,XU Feng,et al.Damage identification method of PE sheath of bridge stay cable based on lightweight convolutional neural network[J].Journal of Civil and Environmental Engineering,2025,47(01):167-178. DOI： 10.11835/j.issn.2096-6717.2022.115.

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Damage identification method of PE sheath of bridge stay cable based on lightweight convolutional neural network

DOI：10.11835/j.issn.2096-6717.2022.115