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One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
| Title: | One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction |
| Authors: | Kilic, Kursat Browse this author | | Toriya, Hisatoshi Browse this author | | Kosugi, Yoshino Browse this author | | Adachi, Tsuyoshi Browse this author | | Kawamura, Youhei Browse this author →KAKEN DB |
| Keywords: | EPB TBM | | tool wear | | deep learning | | soft ground tunnelling | | cutter life | | operational parameters | | convolutional neural network |
| Issue Date: | 25-Feb-2022 |
| Publisher: | MDPI |
| Journal Title: | Applied sciences |
| Volume: | 12 |
| Issue: | 5 |
| Start Page: | 2410 |
| Publisher DOI: | 10.3390/app12052410 |
| Abstract: | 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. |
| Rights: | © 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/). |
| Type: | article |
| URI: | http://hdl.handle.net/2115/85145 |
| Appears in Collections: | 工学院・工学研究院 (Graduate School of Engineering / Faculty of Engineering) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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