|Table of Contents|

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

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

Issue:
2022年04期
Page:
127-136
Research Field:
Publishing date:

Info

Title:
Research on Classification and Recognition of Concrete Structure Diseases Based on Residual Network
Author(s):
DING Wei1; 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;
Keywords:
concrete structure;  disease classification and recognition;  residual network;  multiattribute label;  transfer learning
PACS:
TU317
DOI:
10.19815/j.jace.2021.10112
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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Last Update: 2022-07-10