Shield segment assembly error quantification and position prediction method, system and device

A shield segment assembly error quantification method based on laser target monitoring and formation parameter correction, combined with an artificial neural network prediction model and error warning mechanism, has solved the problem of large segment assembly errors in shield tunnel construction, achieved efficient and precise segment position control, and improved construction quality and safety.

CN120831050AActive Publication Date: 2025-10-24CHINA CONSTR THIRD ENG BUREAU GRP CO LTD
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Patent Information

Application Number
CN202511316011.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-16
Publication Date
2025-10-24
Estimated Expiration
2045-09-16

AI Technical Summary

Technical Problem

In the existing technology, the segment assembly errors in shield tunnel construction are large and lack real-time and precision, resulting in low construction efficiency and high costs. In particular, it is difficult to accurately control the position of the segments in complex formations, affecting the quality and safety of the tunnel.

Method used

Laser targets are used to monitor the shield posture in real time. Combined with the dynamic correction coefficient of the formation parameters, a segment position prediction model is established through an artificial neural network algorithm. The assembly position is monitored and optimized in real time, an error warning mechanism is established, and the plan is automatically adjusted.

Benefits of technology

It improves the accuracy and efficiency of shield tunnel construction, reduces error accumulation, ensures tunnel quality, reduces construction costs, and improves construction accuracy and safety through self-learning and optimization of prediction models.

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Abstract

The invention discloses a shield tunnel segment assembly error quantification and segment position prediction method, and relates to the technical field of shield subway tunnel construction. The position deviation of a point B is calculated through coordinate data of a central position A of a segment ring where a laser target is located, the distance of a central point B of a segment ring on the outermost side and the position of the laser target actually measured on site in combination with the horizontal and vertical coordinate orientation of the laser target, and then the measured data is compared with the data of the actual position of the segment. And central position data and error rules of the segment assembly rings under different tunneling stratums are obtained through calculation. Error correction is carried out by combining data comparison of a trial tunneling section, a segment position prediction model is established through an artificial neural network algorithm, and the relative position of a next segment is determined. According to the method, the relative position relation between the duct piece position and the tunnel line type can be judged in the tunneling process, the relative position of the duct piece of the next ring can be predicted, errors in the duct piece splicing process can be remarkably reduced, and the splicing quality and the forming quality of the shield tunnel duct pieces are guaranteed.
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