An Information Management Method for High-Strength Bolts in Steel Bridges

By using computer vision and deep learning technologies, combined with an intelligent torque-controlled electric wrench, the information management of high-strength bolts for steel bridges has been realized, solving the problem of inconsistent bolt tightening sequence during construction and achieving the effect of quickly querying and accurately recording bolt construction information.

CN116433419BActive Publication Date: 2026-05-26HANGZHOU NAISHEN INTELLIGENT TECH CO LTD
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Patent Information

Application Number
CN202310219429.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-03-01
Publication Date
2026-05-26
Estimated Expiration
2043-03-01

AI Technical Summary

Technical Problem

In steel bridge construction, the inconsistent tightening sequence of high-strength bolts makes it impossible for the final tightening torque and rotation angle in the construction management system to correspond to each bolt being tightened, resulting in a lack of integrity, correlation, and construction traceability, and insufficient information management.

Method used

By employing computer vision and deep learning technologies, the system achieves bolt center point and hole recognition through image acquisition, preprocessing, bolt center point recognition model training, and intelligent torque-controlled electric wrench. Combined with QR codes and a high-strength bolt construction management system, it records and manages the construction information of each bolt.

Benefits of technology

Information management of high-strength bolts for steel bridges has been implemented, facilitating quick access to bolt construction information during operation and maintenance phases. This enhances the overall quality management and relevance of construction, ensuring accurate recording and traceability of information for each bolt.

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Abstract

An information management method for high-strength bolts in steel bridges is proposed. This method involves collecting a dataset, building a bolt center point recognition model based on a fully convolutional neural network, and mounting the trained model onto a small computing unit. Before final tightening, the overall image of the bolt node panel is input into the model to identify the bolt distribution on the node plate. A QR code is scanned to confirm the correspondence between the QR code, the scan result, and the model's recognition result. For each bolt being tightened, a corresponding image is collected, and the row and column number of the bolt being tightened are identified. The bolt image is then input into a high-strength bolt construction management system for individual mapping, along with its corresponding number, final tightening angle, final tightening torque, and node plate number. This invention addresses the problems of lack of overall management, lack of correlation, and poor traceability in on-site construction quality management, enabling rapid querying of bolt-by-bolt construction information for high-strength bolts in steel bridges during construction and operation phases.
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