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《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

卷:

39卷

期数:

2022年04期

页码:

127-136

栏目:

出版日期:

2022-07-12

文章信息/Info

Title:

Research on Classification and Recognition of Concrete Structure Diseases Based on Residual Network

文章编号:

1673-2049(2022)04-0127-10

作者:

丁 威^1,2,3,马亥波¹,舒江鹏¹,NIZHEGORODTSEV Denis V⁴,叶建龙⁵

(1. 浙江大学 建筑工程学院,浙江 杭州 310058; 2. 浙江大学 平衡建筑研究中心,浙江 杭州 310058; 3. 浙江大学建筑设计研究院有限公司,浙江 杭州 310058; 4. 圣彼得堡国立建筑工程大学 土木工程学院,圣彼得堡 190005; 5. 浙江数智交院科技股份有限公司,浙江 杭州 310058)

Author(s):

DING Wei^1,2,3, MA Hai-bo¹, SHU Jiang-peng¹, NIZHEGORODTSEV Denis V⁴, YE Jian-long⁵

(1.College of Civil Engineering and Architecture,Zhejiang University,Hangzhou 310058,Zhejiang,China;2.Center for Balance Architecture,Zhejiang University,Hangzhou 310058,Zhejiang,China;3.The Architecture Design&Research Institution of Zhejiang University Co.,Ltd,Hangzhou 310058,Zhejiang,China;4.Faculty of Civil Engineering,Saint-Petersburg State University of Architecture and Civil Engineering,Saint-Petersburg 190005,Russia;

关键词:

混凝土结构;  病害分类识别;  残差网络;  多属性标注;  迁移学习

Keywords:

concrete structure;  disease classification and recognition;  residual network;  multiattribute label;  transfer learning

分类号:

TU317

DOI:

10.19815/j.jace.2021.10112

文献标志码:

A

摘要:

针对混凝土结构病害识别类型单一、精度较低的现状,提出了基于残差网络和迁移学习的病害分类识别方法,通过构建多属性病害数据集,利用迁移学习优化残差网络模型,提出混凝土结构健康状态识别的多个任务。首先收集混凝土结构的病害状态图像,依次通过数据清洗、尺寸均一化、数据扩增和多人投票标注,最终得到包含6 680张图像的混凝土结构病害多属性数据集,并依据不同标注属性进行了相应训练集、验证集和测试集的划分; 然后利用迁移学习对预训练的ResNet-34网络前3个部分进行参数冻结,后续2个部分的参数进行重新训练,并在模型末端添加新的参数,基于已构建的数据集进行训练; 最后在提出的构件类别检测、剥落检测、病害检测和病害类别检测任务中,分别获得84.88%、98.56%、97.18%和85.34%的F1分数。结果表明:通过构建多属性标注的混凝土结构病害数据集训练深度学习模型,可较好地实现多场景特征下的病害识别效果; 采用迁移学习技术可从开源数据中获取较好的特征提取效果; 改进的ResNet-34网络可克服网络退化问题,并针对混凝土结构病害识别的多个任务获得较好的效果; 相对于单一的混凝土结构病害识别,进行病害部位、程度、多类别的系统性检测,可为结构状态评估提供详细信息,更贴合工程实际需要。

Abstract:

In view of the single type and low accuracy of concrete structure disease recognition, a disease classification and recognition method based on residual network and transfer learning was proposed. By establishing a multiattribute disease dataset and using transfer learning to improve the residual network, the multiple tasks for identifying the health status of concrete structures were proposed. First of all, the original images of disease state of concrete structure were collected and sequentially underwent data cleaning, size normalization, data augmentation and multi-person voting to label. Then, a multiattribute dataset of concrete structure diseases containing 6 680 images was obtained, and corresponding training set, validation set and testing set were divided according to different label attributes. Sequentially, the first three parts of parameters of the pre-trained ResNet-34 were frozen by transfer learning. The parameters of subsequent two parts were retrained, and new parameters were added at the end of the model and training was conducted based on the established dataset. Finally, the F1 score of 84.88%, 98.56%, 97.18% and 85.34% were obtained in the proposed tasks of component type detection, spalling detection, disease detection and disease type detection, respectively. The results show that by building a multiattribute labeled dataset of concrete structure diseases to train a deep learning model, the disease recognition under multi-scene conditions can be better achieved. Better feature extraction results can be obtained from open source data by using transfer learning.The improved ResNet-34 can overcome the problem of network degradation, and achieve better results for multiple tasks of concrete structure disease identification. Compared with the single disease identification of concrete structure, systematic detection of disease location, degree, and multiple categories can provide detailed information for structural state assessment, which is more suitable for the actual needs of the project.

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备注/Memo

备注/Memo:

收稿日期:2021-10-29
基金项目:国家自然科学基金青年科学基金项目(52108179); 浙江省科学技术厅重点研发计划项目(2021C01106)
作者简介:丁 威(1994-),男,湖北鄂州人,工学博士研究生,E-mail:weiding1029@zju.edu.cn。
通信作者:舒江鹏(1987-),男,浙江杭州人,教授,博士研究生导师,工学博士,E-mail:jpeshu@zju.edu.cn。

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更新日期/Last Update: 2022-07-10