Intelligent design method for support scheme of rock burst section in TBM tunnel

By combining TBM host data and surrounding rock feedback, using rock burst intensity evaluation and deep learning technology to build an intelligent design model, the problem that TBM tunnel support scheme relies on artificial experience is solved, and efficient and accurate rock burst prevention and control is achieved.

WO2025156554A1 Publication Date: 2025-07-31NORTHEASTERN UNIV CHINA
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Patent Information

Application Number
PCT/CN2024/100145
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-01-24
Filing Date
2024-06-19
Publication Date
2025-07-31

AI Technical Summary

Technical Problem

The existing TBM tunnel support scheme mainly relies on manual experience, resulting in low design efficiency and subjective influence, and cannot effectively prevent and control rock burst disasters.

Method used

Based on TBM host operation data and surrounding rock feedback, combined with tunnel engineering geological survey reports and topographic geological maps, the rock burst intensity assessment method and computer deep learning technology are used to build an intelligent design model, predict the rock burst level and determine the initial support plan.

Benefits of technology

An efficient and intelligent tunnel support solution design has been achieved, which reduces the influence of subjective factors and improves the accuracy and efficiency of rock burst prevention and control.

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Abstract

An intelligent design method for a support scheme of a rock burst section in a TBM tunnel, relating to the field of tunnel engineering. The intelligent design method for the support scheme of the rock burst section in the TBM tunnel comprises: determining a surrounding rock class on the basis of TBM mainframe operation data, surrounding rock feedback evaluation, and a tunnel engineering geological survey report, and using a rock burst intensity assessment method to predict a rock burst grade; and on the basis of the surrounding rock class and the rock burst grade, using a computer deep learning technology to determine an initial surrounding rock support method. The method effectively avoids the impact of subjective factors, actively utilizes TBM mainframe operation data and surrounding rock feedback evaluation, utilizes big data processing and an artificial intelligence technology to propose an intelligently designed support scheme for a rock burst section in a TBM tunnel, and can provide an efficient support scheme for field construction workers for a rock burst section in a tunnel.
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