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TBM tunneling control parameter intelligent prediction and optimization decision-making method

A technology for controlling parameters and intelligent prediction, applied in data processing applications, instruments, biological neural network models, etc., to achieve the effects of reducing engineering costs, improving accuracy and generalization performance, and reducing equipment energy consumption

Active Publication Date: 2021-01-05
INST OF ROCK AND SOIL MECHANICS - CHINESE ACAD OF SCI +1
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to overcome the defects of the prior art, provide an intelligent prediction and optimization decision-making method for TBM tunneling control parameters, and solve the problem of safe, efficient and intelligent tunneling of full-section tunnel boring machines in complex strata

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  • TBM tunneling control parameter intelligent prediction and optimization decision-making method

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Embodiment 1

[0050] A method for intelligent prediction and optimal decision-making of TBM tunneling control parameters, the steps of which are:

[0051] Step 1. Preprocess the TBM tunneling parameters and the vibration acceleration of the cutter head to obtain the full sample data set, and divide the full sample data set into the first sample data set and the second sample data set. The specific steps are:

[0052]Select all TBM tunneling parameters (cutter thrust, cutter torque, cutter speed, TBM penetration) and cutter vibration in a certain period of time (5-30s) before the predicted time or at a certain interval. Acceleration (acceleration along the tunnel axis direction, cutter head radial vibration acceleration, cutter head circumferential vibration acceleration), where cutter head thrust and cutter head torque are TBM tunneling operation parameters, TBM penetration and cutter head speed are TBM tunneling control parameter,

[0053] The TBM tunneling parameters and cutter head vibr...

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Abstract

The invention discloses a TBM tunneling control parameter intelligent prediction and optimization decision-making method, and the method comprises the steps: carrying out the preprocessing of TBM tunneling parameters and the vibration acceleration of a cutter, and carrying out the training of an LSTM model through cross validation, and obtaining an optimal model hyper-parameter of the LSTM model;training the LSTM model of the optimal model hyper-parameter; training the secondary learner through cross validation and an improved loss function to obtain an optimal model hyper-parameter of the secondary learner; training the secondary learner to obtain a final stacking integration model; and based on a stacking model prediction result, generating an optimal tunneling control parameter by adopting a multi-target particle swarm algorithm. The invention provides a real-time prediction and optimization decision-making method for tunneling control parameters in the excavation process of a full-face tunnel boring machine, solves the problems of automatic selection and adjustment of TBM tunneling process parameters in complex stratums, and has important significance for safe, efficient and intelligent tunneling of the full-face tunnel boring machine.

Description

technical field [0001] The invention relates to the technical field of tunnel construction, and more particularly relates to an intelligent prediction and optimal decision-making method for TBM driving control parameters. Background technique [0002] Full-face tunnel boring machine (TBM) has been more and more widely used in tunnel engineering. However, the surrounding rock conditions of the excavated strata are relatively sensitive to changes. When the rock mass conditions in front of the excavation face change, it is necessary to adjust the corresponding TBM excavation Control parameters (cutterhead thrust, TBM penetration, cutterhead speed, etc.) to achieve the best tunneling state of TBM. At present, the adjustment of tunneling parameters in the TBM construction process mainly has the following deficiencies: [0003] (1) The geological conditions of tunnel excavation are complex, and the TBM operator mainly adjusts the excavation parameters based on experience, and the...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/08G06N3/04
CPCG06Q10/06314G06Q50/08G06N3/049G06N3/044
Inventor 黄兴王心语刘泉声刘滨殷欣张全太
Owner INST OF ROCK AND SOIL MECHANICS - CHINESE ACAD OF SCI
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