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Article

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

1
Department of Geosciences, Geotechnology and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita 010-8502, Japan
2
Division of Sustainable Resources Engineering, Faculty of Engineering, Hokkaido University, Kita 13, Nishi 8, Kita-ku, Sapporo 060-8628, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Daniel Dias
Appl. Sci. 2022, 12(5), 2410; https://doi.org/10.3390/app12052410
Received: 10 February 2022 / Revised: 24 February 2022 / Accepted: 24 February 2022 / Published: 25 February 2022
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% R2, 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. View Full-Text
Keywords: EPB TBM; tool wear; deep learning; soft ground tunnelling; cutter life; operational parameters; convolutional neural network EPB TBM; tool wear; deep learning; soft ground tunnelling; cutter life; operational parameters; convolutional neural network
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MDPI and ACS Style

Kilic, K.; Toriya, H.; Kosugi, Y.; Adachi, T.; Kawamura, Y. One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction. Appl. Sci. 2022, 12, 2410. https://doi.org/10.3390/app12052410

AMA Style

Kilic K, Toriya H, Kosugi Y, Adachi T, Kawamura Y. One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction. Applied Sciences. 2022; 12(5):2410. https://doi.org/10.3390/app12052410

Chicago/Turabian Style

Kilic, Kursat, Hisatoshi Toriya, Yoshino Kosugi, Tsuyoshi Adachi, and Youhei Kawamura. 2022. "One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction" Applied Sciences 12, no. 5: 2410. https://doi.org/10.3390/app12052410

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