Technical Papers
Sep 23, 2023

A Robot System for Rapid and Intelligent Bridge Damage Inspection Based on Deep-Learning Algorithms

Publication: Journal of Performance of Constructed Facilities
Volume 37, Issue 6

Abstract

Large numbers of bridges have already suffered various types of damage but still operate all year round without proper treatment. Conducted primarily manually, the routine bridge inspections are ineffective in detecting potential damage in time due to a lack of relevant instruments and equipment, particularly modern measures. In this study, a rapid and intelligent bridge inspection system that integrates multiple modules and deep learning algorithms was established. First, the robot inspection equipment is established. Then, the You Only Look Once version 3 (YOLOv3) object detection algorithm is employed to classify four types of defects from the acquired data. Finally, an image segmentation algorithm is used to identify crack defects at a pixel level. Experimental results reveal that the proposed system can be effectively applied to accurately locate defects (e.g., cracks, spalls, exposed tendons, and free lime) and identify cracks at a pixel level on various types of bridges without affecting traffic.

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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 03 Special and 5G Projects in Jiangxi Province (20224ABC03A03), and Jiangxi Natural Science Foundation General Project (20202babl2040058) for providing the funding that made this study possible.
Author contributions: conceptualization, Q. L., J. B., Z. Y., Y. Y., and J. B.; methodology, Q. L., and J. B.; writing—original draft preparation, Z. Y.; writing—review and editing, Y. Y.; and supervision, Q. L., and J. B.

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Information & Authors

Information

Published In

Go to Journal of Performance of Constructed Facilities
Journal of Performance of Constructed Facilities
Volume 37Issue 6December 2023

History

Received: Dec 3, 2022
Accepted: Apr 24, 2023
Published online: Sep 23, 2023
Published in print: Dec 1, 2023
Discussion open until: Feb 23, 2024

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Authors

Affiliations

Qingling Meng [email protected]
Lecturer, School of Civil Engineering, Tianjin Chengjian Univ., No. 26 Jinjing Rd., Xiqing District, Tianjin 300384, China. Email: [email protected]
Jiabing Yang [email protected]
Master’s Candidate, School of Civil Engineering, Tianjin Chengjian Univ., No. 26 Jinjing Rd., Xiqing District, Tianjin 300384, China. Email: [email protected]
Master’s Candidate, School of Civil Engineering, Tianjin Chengjian Univ., No. 26 Jinjing Rd., Xiqing District, Tianjin 300384, China. Email: [email protected]
Master’s Candidate, School of Civil Engineering, Tianjin Chengjian Univ., No. 26 Jinjing Rd., Xiqing District, Tianjin 300384, China (corresponding author). Email: [email protected]
Professor, School of Civil Engineering, Jiangxi Communications Vocational and Technical College, Nanchang 330013, China. Email: [email protected]
Researcher, Tianjin Licheng Technology Service Co., Ltd., Tianjin 300384, China. Email: [email protected]

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