Real-Time Rock Mass Strength Estimation in TBM Tunneling
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Solution Overview
Problem
Current methods for rock strength measurement and grading in tunnel boring machines (TBMs) are not suitable for jointed rock masses and lack real-time capabilities, making it difficult to assess rock integrity and prevent sticking risks during TBM operations.
Innovation Solution
A method for real-time strength estimation and grading of rock mass using a general relation model between equivalent strength and field penetration index, allowing for online identification and early warning of rock mass conditions by calculating the integrity coefficient based on boring parameters and geological data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional field sampling testing is used after boring, then rock strength can be measured, but the measurement is not real-time and cannot provide online identification during TBM operation
Solution Approach 1:
The patent establishes a predictive model before actual measurement occurs, using boring parameters (cutterhead thrust, penetration rate) and geological data to forecast rock mass strength in advance. This preliminary action eliminates the need for post-boring sampling while providing real-time strength estimation during TBM operation.
Solution Approach 2:
The patent replaces the mechanical field sampling and laboratory testing system with an information-based predictive model. Instead of physically extracting rock samples for strength measurement, the system uses mathematical modeling based on boring parameters and geological data to calculate rock mass strength in real-time.
2Loss of time
If rock strength model based on boring parameters is used, then real-time estimation is possible, but the model ignores rock mass integrity and is not suitable for jointed rock masses
Solution Approach 1:
The patent introduces the integrity coefficient Kv as a local quality parameter that specifically characterizes rock mass integrity. Instead of using a single universal strength parameter, the model separately accounts for both rock strength (Rs) and rock mass integrity (Kv) to provide accurate predictions for jointed and fractured rock masses.
Solution Approach 2:
The patent creates a composite evaluation system that combines multiple factors: boring parameters (cutterhead thrust, penetration rate), geological data (lithology, structure), and the integrity coefficient. This composite approach integrates the strengths of individual parameters while capturing the complex behavior of jointed and fractured rock masses.
3Ease of manufacture
If traditional surrounding rock grading method is used, then grading can be performed, but it is not adapted to TBM construction and cannot provide online real-time intelligent grading
Solution Approach 1:
The patent transforms the static, manual grading process into a dynamic, automated real-time system. The grading method continuously updates rock mass classification based on real-time boring parameters and geological data, adapting to changing tunnel conditions during TBM operation rather than using fixed pre-boring classifications.
Solution Approach 2:
The patent implements a feedback mechanism where boring parameters and geological data continuously feed into the predictive model to update rock mass strength and integrity assessments. This feedback loop enables real-time adjustment of grading and support design based on actual tunneling conditions, improving both accuracy and adaptability.
Data Source
AI summary
A method for real-time strength estimation, grading, and early warning of rock mass in tunnel boring machine (TBM) tunneling, and belongs to the technical field of TBM tunnel construction. The method includes the following steps: establishing a general relation model of equivalent strength Rec of the TBM boring rock mass and a field penetration index (FPI); and applying the model to TBM boring construction, estimating an integrity coefficient Kv of the TBM boring rock mass in real time according to boring parameters acquired by a TBM in real time, and performing grading and early warning on the TBM boring rock mass according to a given grading standard and early warning values.