One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction

Kilic, Kursat; Toriya, Hisatoshi; Kosugi, Yoshino; Adachi, Tsuyoshi; Kawamura, Youhei

One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction

Kilic, Kursat; Toriya, Hisatoshi; Kosugi, Yoshino et al.

Applied sciences, 2022, 12(5), 2410

Permalink : https://hdl.handle.net/2115/85145

このアイテムのアクセス数:292件（2026-02-11 09:23 集計）

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論文情報

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アクセス権	metadata only access
URI	https://hdl.handle.net/2115/85145
タイトル	One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
著者	Kilic, Kursat ; Toriya, Hisatoshi ; Kosugi, Yoshino ; Adachi, Tsuyoshi ; Kawamura, Youhei
著者名 Kilic, Kursat
著者名 Toriya, Hisatoshi
著者名 Kosugi, Yoshino
著者名 Adachi, Tsuyoshi
e-Rad_Researcher 40361323 著者名 Kawamura, Youhei
言語	英語
キーワード	EPB TBM
	tool wear
	deep learning
	soft ground tunnelling
	cutter life
	operational parameters
	convolutional neural network
発行日	2022-02-25
出版者	MDPI
収録物名	Applied sciences
巻(号)	12(5)
ページ	2410
抄録	An earth pressure balance (EPB) TBM is used in soft ground conditions, and these conditions lead to the fluctuation and instability of machine parameters. Machine parameters influence cutter wear and tunnel excavation. For this reason, to evaluate and predict the cutter wear of an EPB TBM, a 1D CNN model was used to provide machine-parameter-based cutter wear prediction using an EPB TBM operational dataset. The machine parameters were split into 80% training and 20% test datasets. Compared to traditional machine learning applications and two deep neural network models, the proposed model provided reliable results with a reasonable computational time. The correlation coefficient was 89.6% R-2, the mean squared error (MSE) was 57.6, the mean absolute error (MAE) was 1.6, and the computational wall time was 3 min 22 s.
資源タイプ	学術雑誌論文
出版タイプ	Version of Record
権利情報	© 2022 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
PISSN	2076-3417
関連情報 (isIdenticalTo)	出版社版DOI https://doi.org/10.3390/app12052410