Technical Papers
Sep 26, 2022

A Detection Method for Bridge Cables Based on Intelligent Image Recognition and Magnetic-Memory Technology

Publication: Journal of Performance of Constructed Facilities
Volume 36, Issue 6

Abstract

In recent years, more and more disasters caused by the fracture of bridge cables have been reported. Brittle fracture of cables is mainly caused by corrosion fatigue, and thus it is crucial to find cable defects early. The present study developed a cable inspection method embedded with a lightweight deep-learning model and equipped with micromagnetic sensors, based on intelligent image recognition and magnetic memory technology. After four cable-stayed bridges were found with defects, five types of defects and features on the surface of cables were identified by the SqueezeNet network model with the image denoising algorithm and transfer-learning method, with accuracy of 97.18%. The corrosion along the cable was positioned with micromagnetic sensors. Four alerting levels were proposed and corresponding remedial measures were suggested to be implemented. The novelty of this work lies in the intelligent detection of bridge defects, as well as accurate evaluation of long-term performance of bridge cables.

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Data Availability Statement

All data, models, and code generated or used during the study appear in the published article.

Acknowledgments

The authors gratefully acknowledge the National Natural Science Foundation of China (No. 52108163), the Major Special Project of Tianjin Rail Transit (No. 18ZXGDGX00050), the Tianjin Education Commission (No. 2017KJ047) for providing the funding that made this study possible, and the 03 special and 5G projects in Jiangxi Province (20203ABC03B05).

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Go to Journal of Performance of Constructed Facilities
Journal of Performance of Constructed Facilities
Volume 36Issue 6December 2022

History

Received: Mar 11, 2022
Accepted: Jul 14, 2022
Published online: Sep 26, 2022
Published in print: Dec 1, 2022
Discussion open until: Feb 26, 2023

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Qingling Meng [email protected]
Lecturer, College of Civil Engineering, Tianjin Chengjian Univ., No. 26 Jinjing Rd., Xiqing District, Tianjin 300384, China. Email: [email protected]
Master’s Candidate, College of Civil Engineering, Tianjin Chengjian Univ., No. 26 Jinjing Rd., Xiqing District, Tianjin 300384, China. Email: [email protected]
Hailiang Wang [email protected]
Professor, College of Civil Engineering, Tianjin Chengjian Univ., No. 26 Jinjing Rd., Xiqing District, Tianjin 300384, China (corresponding author). Email: [email protected]
Engineer, China Railway 18th Bureau Group Co., Ltd., Da-Gu-Nan Rd., Jinnan District, Tianjin 300000, China. Email: [email protected]
Zhenyu Wang [email protected]
Engineer, Tianjin Transportation Research Institutions, Dongli Development Zone, Tianjin 300384, China. Email: [email protected]

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