Research of Highway Bridge Settlement Monitoring Technology based on Machine Vision

Authors

  • Qian Zhao Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry ＆Food Machinery and Equipment, College of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, China
  • Chunhao Hu Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry ＆Food Machinery and Equipment, College of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, China
  • Guoqing Xia Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry ＆Food Machinery and Equipment, College of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, China
  • Yun Chen Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry ＆Food Machinery and Equipment, College of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, China

DOI:

https://doi.org/10.53469/jrse.2024.06(07).06

Keywords:

Highway bridge, Machine vision, Settlement monitoring, Target recognition

Abstract

In view of the significant impact of deep foundation pit excavation on the surface of surrounding roads and bridges, the widely used monitoring technology still relies on manual detection means, which leads to the consumption of a large number of human and material resources, and the efficiency is relatively low. Therefore, this paper provides a method and system of highway bridge pile foundation displacement monitoring based on machine vision. Through real-time automatic monitoring of highway bridge pile foundation settlement changes, it provides targeted suggestions and guidance for highway bridge maintenance during foundation pit excavation. At the same time, a new type of marker module is provided to enhance the accuracy of feature point recognition in image processing. The results show that the highway bridge settlement monitoring system based on machine vision method can automatically monitor the highway bridge pile foundation settlement in real time with high accuracy, and improve the safety and stability of highway bridges during construction.

References

Li B, Sun W, Li K G. Analysis of influence of subway station deep foundation pit excavation on adjacent bridge piles [J]. IOP Conference Series Materials Science and Engineering,2020,789(1):012033.

Oliveira M V J, Larocca C P A, Neto A O J, et al. Vibration monitoring of a small concrete bridge using wavelet transforms on GPS data [J]. Journal of Civil Structural Health Monitoring,2019,9(3):397-409.

Kobayoshi M, Chui Q S H. The positioning influence of dial gauges on their calibration results [J]. Measurement, 2005,38(1):67-76.

Acevedo G M G, Rodriguez Q A J, Becerra V E G, et al. A reliable methodology to estimate cable tension force in cable-stayed bridges using Unmanned Aerial Vehicle (UAV) [J]. Measurement, 2024,229,114498.

Gao B, Zhao H, Miao X. Multi-modal cascade detection of pipeline defects based on deep transfer metric learning [J]. Engineering Failure Analysis, 2024,160,108216.

Astrid T, Bart K D, Jiaqi H, et al. An introduction to artificial intelligence in machine vision for postharvest detection of disorders in horticultural products [J]. Postharvest Biology and Technology, 2023,206:112576.

Downloads

Published

2024-07-28

How to Cite

Zhao, Q., Hu, C., Xia, G., & Chen, Y. (2024). Research of Highway Bridge Settlement Monitoring Technology based on Machine Vision. Journal of Research in Science and Engineering, 6(7), 29–32. https://doi.org/10.53469/jrse.2024.06(07).06

Issue

Vol. 6 No. 7 (2024): JRSE-Volume 6 Issue 7

Section

Articles

License

Copyright (c) 2024 Qian Zhao, Chunhao Hu, Guoqing Xia, Yun Chen

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.