Tunnel Boring Machine Parameter Optimization via Ground Interaction Models
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Solution Overview
Problem
Tunnel boring machines face challenges in optimizing their characteristics in real-time to adapt to varying ground conditions, requiring significant expertise and often resulting in inefficient operations due to non-homogeneous ground natures.
Innovation Solution
A method is developed to determine a ground/machine interaction model using a combination of non-supervised and supervised classification algorithms, which allows for real-time optimization of tunnel boring machine characteristics based on specific boring parameters, independent of the machine type or ground nature, by segmenting and classifying data from sensors to identify optimal operational settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the pilot systematically deduces ground conditions from sensor values and adjusts machine characteristics, then the boring process can be optimized, but this requires considerable expertise and is time-consuming
Solution Approach 1:
The system performs self-service by automatically analyzing sensor data and adjusting machine parameters without requiring pilot expertise. The automated classification and optimization system serves itself, eliminating the need for human deduction and adjustment while maintaining optimal boring efficiency.
Solution Approach 2:
The system implements feedback by continuously monitoring sensor values, comparing them against classification models, and automatically adjusting machine characteristics based on the analysis results. This closed-loop feedback mechanism replaces manual pilot deduction with automated real-time optimization.
2Measurement precision
If sensors are placed on the cutting head or shaft to measure boring parameters, then more precise ground nature information can be obtained, but the measurements are affected by deformations of the shaft or support structures
Solution Approach 1:
The solution extracts the measurement function from the deforming structures (shaft and cutting head) by placing sensors on the cutting disks themselves. This separates the measurement system from the deforming support structures, eliminating the interference of structural deformations on measurement accuracy and reliability.
Solution Approach 2:
The system applies local quality by placing sensors directly on the cutting disks where the actual ground interaction occurs. This localized measurement approach captures true ground conditions at the point of contact rather than measuring indirect signals from deforming support structures.
3Adaptability or versatility
If the ground is non-homogeneous, then diverse ground conditions must be handled, but the sensor values obtained may not be representative of the true nature of the ground at the tunnel face
Solution Approach 1:
The system segments the ground classification into distinct categories using classification algorithms. By dividing the continuous spectrum of ground conditions into discrete, well-defined classes, the system can accurately represent and handle non-homogeneous ground conditions, with each sensor value clearly indicating a specific ground type.
Solution Approach 2:
The system transforms raw sensor values into classified ground categories through parameter changes. By converting continuous sensor measurements into discrete classification results, the system improves the representativeness of ground nature data, making it clearer and more actionable for optimization decisions.
Data Source
AI summary
The invention relates to a method (S10) for optimizing the characteristics of a tunnel boring machine, particularly a tunnel boring machine of the slurry pressure or VD type, said method comprising the following steps:S0: determining a ground/machine interaction model,S11: instantaneous measurement of the set of specific boring parameters of the tunnel boring machine,S13: determining the group of individuals corresponding to the boring parameters measured in step S11 by means of the ground/machine interaction model,S14: optimizing the characteristics of the tunnel boring machine as a function of the group of individuals thus determined.