Prediction of rock mass classification in tunnel boring machine tunneling using the principal component analysis (PCA)–gated recurrent unit (GRU) neural network

Ke Man; Liwen Wu; Xiaoli Liu; Zhifei Song; Kena Li; Nawnit Kumar

doi:10.1002/dug2.12084

Deep Underground Science and Engineering ›› 2024, Vol. 3 ›› Issue (4) :413 -425. DOI: 10.1002/dug2.12084

RESEARCH ARTICLE

Prediction of rock mass classification in tunnel boring machine tunneling using the principal component analysis (PCA)–gated recurrent unit (GRU) neural network

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Abstract

	•A neural network combining principal component analysis (PCA) and gated recurrent unit (GRU) is proposed to provide accurate prediction of rock mass classification in tunnel boring machine (TBM) tunneling.
	•The PCA–GRU model runs in approximately 20 s, which enables quick prediction of rock mass classification in TBM tunneling.
	•The PCA–GRU model shows stronger generalization, making it more suitable in conditions where the distribution of various rock mass classes and lithologies change in percentage.

Keywords

gated recurrent unit (GRU) / prediction of rock mass classification / principal component analysis (PCA) / TBM tunneling

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Ke Man, Liwen Wu, Xiaoli Liu, Zhifei Song, Kena Li, Nawnit Kumar. Prediction of rock mass classification in tunnel boring machine tunneling using the principal component analysis (PCA)–gated recurrent unit (GRU) neural network. Deep Underground Science and Engineering, 2024, 3(4): 413-425 DOI:10.1002/dug2.12084

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2024 The Authors. Deep Underground Science and Engineering published by John Wiley & Sons Australia, Ltd on behalf of China University of Mining and Technology.