Application of optical fiber sensing technology in traffic tunnel pavement disease monitoring

OuYang Lou; Yue Yang

doi:10.1117/12.3022708

18 March 2024 Application of optical fiber sensing technology in traffic tunnel pavement disease monitoring

OuYang Lou, Yue Yang

Proceedings Volume 13104, Advanced Fiber Laser Conference (AFL2023); 131041Z (2024) https://doi.org/10.1117/12.3022708
Event: Advanced Fiber Laser Conference (AFL2023), 2023, Shenzhen, China

Abstract

If road diseases are not dealt with in a timely manner, they will affect traffic safety, so monitoring is necessary. The existing manual on-site inspection methods can no longer meet the current requirements, while road disease detection systems based on image processing face the problem of large data processing volume, dim lighting inside tunnels, and increased difficulty in image analysis. This article is based on a distributed fiber optic sensing system to construct a road vibration detection network, collect and analyze the vibration generated by vehicles rolling over the road surface, evaluate whether there are abnormalities in the vehicle's driving vibration signals, and locate the abnormal positions. It can be used as an auxiliary judgment and detection method for road diseases in traffic tunnels. After simulation testing, the method proposed in this article has a recognition rate of ≥ 86% for road diseases and a positioning deviation of ≤ 10m. It can timely detect abnormal situations of road diseases and provide reliable reference for the safety management of traffic tunnels during operation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

OuYang Lou and Yue Yang "Application of optical fiber sensing technology in traffic tunnel pavement disease monitoring", Proc. SPIE 13104, Advanced Fiber Laser Conference (AFL2023), 131041Z (18 March 2024); https://doi.org/10.1117/12.3022708

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KEYWORDS

Vibration

Roads

Diseases and disorders

Optical fibers

Signal detection

Optical sensing

Image segmentation

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