Advances in Key Technologies for Steel Structure Operation and Maintenance Based on Machine Vision

CHEN Feiqi; XUE Jiang; LU Peng; WANG Jian; DING Daiwei

doi:10.3724/j.gyjzG25031702

Volume 55 Issue 7

Jul. 2025

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CHEN Feiqi, XUE Jiang, LU Peng, WANG Jian, DING Daiwei. Advances in Key Technologies for Steel Structure Operation and Maintenance Based on Machine Vision[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(7): 131-142. doi: 10.3724/j.gyjzG25031702

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CHEN Feiqi, XUE Jiang, LU Peng, WANG Jian, DING Daiwei. Advances in Key Technologies for Steel Structure Operation and Maintenance Based on Machine Vision[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(7): 131-142. doi: 10.3724/j.gyjzG25031702

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Advances in Key Technologies for Steel Structure Operation and Maintenance Based on Machine Vision

doi: 10.3724/j.gyjzG25031702

CHEN Feiqi¹,
XUE Jiang¹,
LU Peng^{2,3
,},
WANG Jian^1
,,
DING Daiwei⁴

1. Tianjin Chengjian University, Tianjin 300384, China;
2. Inspection and Certification Co., Ltd., MCC, Beijing 100088, China;
3. Central Research Institute of Building and Construction Co., Ltd., MCC Group, Beijing 100088, China;
4. North China Municipal Engineering Design & Research Institute Co., Ltd., Tianjin 300074, China

Received Date: 2025-03-17
Available Online: 2025-09-12

Abstract

Abstract

This paper systematically reviews advancements in bolt loosening and loss detection， weld defect identification， local deformation monitoring， and corrosion localization and severity assessment. To address the limitations of traditional manual-feature-based methods—such as poor environmental adaptability—deep learning and 3D reconstruction techniques have been employed to enable automatic feature extraction， multi-scale detection， and spatial deformation quantification， greatly enhancing detection accuracy and intelligence. To overcome challenges such as complex background interference and small-target omission， researchers have introduced attention mechanisms， feature pyramid networks， multimodal data fusion， and semi-supervised transfer learning to improve model robustness and generalization. Nevertheless， current technologies still face challenges including insufficient dataset diversity， limited algorithm robustness， and difficulties in real-time deployment. Future development directions will include strengthening multimodal collaborative sensing and 3D reconstruction integration， building continuously adaptive learning models， enabling real-time cloud-edge collaborative analysis， and advancing intelligent inspection systems integrated with digital twin frameworks. These advancements will drive the maintenance of steel structures towards full automation， intelligence， and systematization.
- steel structures,
- structural health monitoring,
- damage detection,
- automated detection systems,
- machine vision

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References(51)

References

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