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[1]李子奇,蒋柱虎,王力,等.基于深度学习的工程结构损伤识别研究进展[J].中国安全生产科学技术,2022,18(12):43-48.[doi:10.11731/j.issn.1673-193x.2022.12.006]
　LI Ziqi,JIANG Zhuhu,WANG Li,et al.Research progress in damage identification of engineering structure based on deep learning[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(12):43-48.[doi:10.11731/j.issn.1673-193x.2022.12.006]
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基于深度学习的工程结构损伤识别研究进展
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
18
期数:
2022年12期
页码:
43-48
栏目:
学术论著
出版日期:
2022-12-31

文章信息/Info

Title:
Research progress in damage identification of engineering structure based on deep learning
文章编号:
1673-193X(2022)-12-0043-06
作者:
李子奇; 蒋柱虎; 王力; 张宇星; 潘启仁
(1.兰州交通大学 土木工程学院，甘肃 兰州 730070；
2.兰州交通大学 甘肃省道路桥梁与地下工程重点实验室，甘肃 兰州 730070)
Author(s):
LI Ziqi;  JIANG Zhuhu;  WANG Li;  ZHANG Yuxing;  PAN Qiren
(1.School of Civil Engineering,Lanzhou Jiaotong University,Lanzhou Gansu 730070,China;
2.Key Laboratory of Road & Bridge and Underground Engineering of Gansu Province,Lanzhou Jiaotong University,Lanzhou Gansu 730070,China)
关键词:
工程结构; 结构损伤识别; 深度学习; 卷积神经网络
Keywords:
engineering structure;  structural damage identification;  deep learning;  convolution neural network
分类号:
TU317；X947
DOI:
10.11731/j.issn.1673-193x.2022.12.006
文献标志码:
A
摘要:
为避免或减轻工程结构在建造和运营期间因结构振动产生不同程度损伤，造成安全隐患危及人们生命财产安全，针对结构振动损伤识别技术展开研究，探讨不同深度学习方法发展情况及其利弊，寻找更具可行性的损伤识别方法，并对其最新研究及应用现状进行全面综述。研究结果表明：应用深度学习开发新的结构损伤识别技术，无需冗余的数据预处理以及手工提取损伤特征，实现以较高精度实现损伤识别任务；一维卷积神经网络（1D-CNN）以其独特的应用优势，在数据样本有限条件下较二维卷积神经网络（2D-CNN）表现更为出色。研究结果可为数据驱动的结构损伤识别问题提供新思路，进一步完善土木结构健康监测研究体系。
Abstract:
In order to avoid or reduce different degrees of damage caused by structural vibration during the construction and operation of engineering structure,resulting in potential safety hazards and endangering the safety of people’s lives and property,the damage identification technology of structural vibration was studied.The development of different deep learning methods and their advantages and disadvantages were explored,then the more feasible damage identification methods were searched,and their latest research and application status were comprehensively reviewed.The results showed that the new structural damage identification technology developed by applying the deep learning could achieve the damage identification tasks with high accuracy without redundant data preprocessing and manual extraction of damage features.With its unique application advantages,the compact 1D-CNN performed better under the condition of limited data samples.The research results can provide new ideas for data-driven structural damage identification and further improve the research system of civil structure health monitoring.

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备注/Memo

备注/Memo:
收稿日期： 2022-05-18
作者简介： 李子奇，博士，副教授，主要研究方向为桥梁抗震理论及工程应用、大跨度桥梁施工控制技术、土木工程结构试验检测新技术。
通信作者： 王力，博士，讲师，主要研究方向为钢-混凝土组合结构理论、桥梁抗震、复杂结构桥梁施工控制技术。
更新日期/Last Update: 2023-01-16