Road and Bridge Expansion Joint Crack Detection and Disease Classification Based on Deep Learning and Morphology

DOI

10.2991/978-94-6463-398-6_45How to use a DOI?

Keywords

Deep learning; Morphological filtering; Crack detection and classification

Abstract

Roads and bridges play a pivotal role in China's land transportation. However, with the increase in operation time, the concrete in the anchorage area of road and bridge expansion joints will be subjected to fatigue loading for a long time cracking will occur, and the expansion joints will be bulging and other diseases. In turn, it may lead to a major accident. To quickly detect whether there are cracks on the surface around the road and bridge expansion joints and the type of cracks, a road and bridge expansion joint crack classification model was established. The experimental results show that the accuracy of the model reaches 98.97% after three iteration cycles, and the loss value is less than 0.06. Then, morphological filtering algorithms based on the computer vision framework OpenCV were used to calculate the maximum width, average width, and average angle of the cracks. This can be used to categorize the degree of crack damage in road and bridge expansion joints. Finally, through experimental testing, the relative error between the crack width value and the actual value measured by the reading microscope is within 3%.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yanhui Huang
AU  - Shengan Lu
AU  - Haoxuan Du
AU  - Weixian Qiu
AU  - Xuyan Cai
AU  - Shuai Xue
AU  - Jia He
PY  - 2024
DA  - 2024/04/24
TI  - Road and Bridge Expansion Joint Crack Detection and Disease Classification Based on Deep Learning and Morphology
BT  - Proceedings of the 2023 5th International Conference on Hydraulic, Civil and Construction Engineering (HCCE 2023)
PB  - Atlantis Press
SP  - 473
EP  - 481
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-398-6_45
DO  - 10.2991/978-94-6463-398-6_45
ID  - Huang2024
ER  -