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  4. Vol 9, Issue 2, 2024

Technical Problems and Solutions for Highway Bridge Detection

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DOI: 10.23977/jemm.2024.090204 | Downloads: 3 | Views: 45

Author(s)

Wangming Wu 1, Gongxing Yan 2, Wenjie Yang 3,4, Xiaoping Zou 3, Yuhu Sun 1

Affiliation(s)

1 Aneng Third Bureau Chengdu Engineering Quality Testing Co., Ltd, Chengdu, 611130, China
2 School of Materials Science and Engineering, Wuhan Institute of Technology, Wuhan, 430205, China
3 Sichuan Jinghengxin Construction Engineering Testing Co., Ltd, Luzhou, 646000, China
4 School of Intelligent Construction, Luzhou Vocational and Technical College, Luzhou, 646000, China

Corresponding Author

Gongxing Yan

ABSTRACT

In recent years, the number of newly built bridges has been increasing, bringing great convenience to people's transportation. However, due to the sharp increase in traffic volume and load capacity, some existing bridges have gradually exposed a series of quality problems, threatening the safety of bridge operation. Therefore, it is necessary to scientifically increase the detection and evaluation of bridge quality diseases, take effective maintenance and reinforcement measures based on the specific disease situation of the bridge, comprehensively improve the bearing performance of the bridge, and ensure its service life. This article combines specific engineering practices to systematically analyze bridge detection and reinforcement technology, which can improve the level of bridge maintenance and reinforcement technology and promote the comprehensive development of bridge engineering. Firstly, the importance of highway bridge detection and the shortcomings of traditional inspection methods were introduced, and the research progress in this field worldwide was summarized. Next, the experimental methods were elaborated in detail, including data acquisition, data augmentation, model training, and image processing steps, and the experimental results were analyzed and discussed. The results indicate that the YOLOv4 (You Only Look Once Version 4) model performs the best on various indicators. Its accuracy reaches 0.96, recall rate is 0.94, F1 score is 0.95, providing scientific basis and technical support for the safe operation and maintenance of bridges.

KEYWORDS

Bridge Detection Technology; Crack Identification; Deep Learning Models; Data Augmentation; Structural Health Monitoring

CITE THIS PAPER

Wangming Wu, Gongxing Yan, Wenjie Yang, Xiaoping Zou, Yuhu Sun, Technical Problems and Solutions for Highway Bridge Detection. Journal of Engineering Mechanics and Machinery (2024) Vol. 9: 29-37. DOI: http://dx.doi.org/10.23977/jemm.2024.090204.

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