TBM tunneling parameter prediction method based on multi-target learning

Abstract

The invention provides a TBM tunneling parameter prediction method based on multi-target learning, which can solve the problems of subjectivity of artificial decision and singleness of decision rulesin a traditional control strategy. The method comprises: acquiring TBM tunneling parameters through an intelligent data acquisition system; performing noise reduction and enhancement processing on theTBM tunneling parameters to obtain data of each cyclic in a rising section anda stable section; extracting time domain characteristics of the rising section and the stable section in each cycle, andconstructing an evaluation index reflecting the health state of the TBM; utilizing an artificial intelligence algorithm to effectively represent a nonlinear mapping relation between the rising sectionTBM characteristic index and the stable section multi-target variable, and establishing a multi-target learning-based rock machine action model; carrying out self-adaptive adjustment on parameters ofthe rock machine action model through a nonlinear optimization method; according to the multi-target parameter information estimated by the rock machine action model, enabling the TBM driver to optimize the TBM control strategy by integrating the geological conditions of the surrounding rock and the particle distribution of the slag pieces. According to the method, TBM tunneling parameters can beestimated online, and meanwhile normal operation of the TBM is not interfered.

Description

technical field [0001] The invention belongs to the field of intelligent control of large-scale tunneling equipment, in particular to a method for predicting TBM tunneling parameters based on multi-objective learning, which can simultaneously predict multiple TBM tunneling parameters and optimize the control strategy in the TBM tunneling process. Background technique [0002] During the tunneling process of hard rock TBM, the driver needs to adjust the tunneling parameters of the equipment in real time according to the geological conditions of the rock mass. However, the traditional control strategy is mainly based on the driving experience of the TBM driver and the fluctuation of the tunneling parameters, but this method is easily affected by human subjectivity and the singleness of decision rules, resulting in a large deviation between the parameter adjustment and the actual value , leading to the decision-making error of the TBM control strategy, which seriously threatens...

AI Technical Summary

Problems solved by technology

However, the traditional control strategy is mainly based on the driving experience of the TBM driver and the fluctuation of the tunneling parameters, but this method is easily affected by human subjectivity and the singleness of decision rules, resulting in a large deviation between the parameter adjustment and the actual value. , which induces decision-making errors in the TBM control strategy, which seriously threatens the safety and reliability of man-machines

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