Strain Reconstruction of TBM Cutterhead at Key Positions Based on BP Neural Network

doi:10.12068/j.issn.1005-3026.2023.10.012

Abstract

Abstract: To solve the problem of difficult and inaccurate strain monitoring of the key main load-bearing structures of the full face hard rock tunnel boring machine(TBM)under harsh working conditions， a strain reconstruction method for key positions of the TBM cutterhead based on BP neural network and finite element analysis was proposed. Firstly， the key vulnerable positions of the TBM cutterhead were determined through static and dynamic finite element analysis， and the typical vulnerable feature substructures of the cutterhead were extracted. Secondly， the static finite element analysis of standard parts and feature substructures under multiple loads was carried out based on the design of experiments(DOE)， and the load-strain database was constructed.Finally， a strain reconstruction model for the standard sample and cutterhead feature substructure was established using BP neural network， and experimental verification of the standard samples were conducted.The results showed that the average error of the reconstructed strains is 10%， which verifies the feasibility of the method and provides a feasible method for strain reconstruction of complex TBM cutterhead.

Key words: TBM cutterhead; feature substructures; strain reconstruction; BP neural network; design of experiments

CLC Number:

U467.4

HUO Jun-zhou， GE Li-han， ZHANG Zhan-ge， LI Gao-rui. Strain Reconstruction of TBM Cutterhead at Key Positions Based on BP Neural Network[J]. Journal of Northeastern University(Natural Science), 2023, 44(10): 1455-1463.

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