Automatic Quantitative Identification of Bridge Surface Cracks Based on Deep Learning
Publication: Journal of Performance of Constructed Facilities
Volume 37, Issue 1
Abstract
To quickly obtain bridge surface crack information to assist bridge condition assessment or maintenance, computer vision technology is used in this paper to quantitatively identify bridge surface cracks. The features of the bridge surface in different parts of the bridge may be quite different. To carry out crack identification research in a targeted manner, first, a classification model is trained based on deep learning to discriminate the surface pattern type of bridges. Then, for the images of bridge surfaces with different features, classification and segmentation models are used to identify the location and profile of the crack in the image; in other words, the existence of the crack is first judged by the trained classification models and pixel-level segmentation is performed by the trained segmentation models on the local region where the crack exists to determine the profile of the crack. Finally, based on the trained models of three types, crack identification is realized in combination with the improved sliding window in high-resolution bridge surface images with multiclass features. According to the identification results, the unconnected cracks are separated by the connected domain. From the results, a general algorithm for calculating the length and width of each crack is established, realizing the quantification of crack identification results regardless of the surface crack form.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors thank the organizations and institutions that provided the raw open-source datasets. In addition, this research work was jointly supported by the National Natural Science Foundation of China (Grants Nos. 51978128 and 12002224), and the Fundamental Research Funds for the Central Universities (Grant No. DUT22ZD213).
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Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 37 • Issue 1 • February 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: May 30, 2022
Accepted: Aug 31, 2022
Published online: Nov 16, 2022
Published in print: Feb 1, 2023
Discussion open until: Apr 16, 2023
ASCE Technical Topics:
- Architectural engineering
- Artificial intelligence and machine learning
- Automatic identification systems
- Bridge engineering
- Bridge management
- Bridge tests
- Bridge-vehicle interaction
- Bridges
- Building management
- Computer programming
- Computing in civil engineering
- Continuum mechanics
- Cracking
- Detection methods
- Engineering fundamentals
- Engineering mechanics
- Field tests
- Fracture mechanics
- Maintenance and operation
- Methodology (by type)
- Neural networks
- Solid mechanics
- Structural engineering
- Tests (by type)
Authors
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Cited by
- Hai-En Tang, Ting-Hua Yi, Song-Han Zhang, Chong Li, Multidamage Identification in High-Resolution Concrete Bridge Component Imagery Based on Deep Learning, Journal of Performance of Constructed Facilities, 10.1061/JPCFEV.CFENG-4671, 38, 5, (2024).