| Item type |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2023-02-23 |
| タイトル |
|
|
タイトル |
One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
EPB TBM |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
tool wear |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
deep learning |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
soft ground tunnelling |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
cutter life |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
operational parameters |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
convolutional neural network |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 作成者 |
Kilic, Kursat
Toriya, Hisatoshi
Kosugi, Yoshino
Adachi, Tsuyoshi
Kawamura, Youhei
|
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
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. |
|
言語 |
en |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 書誌情報 |
en : APPLIED SCIENCES-BASEL
巻 12,
号 5,
発行日 2022
|
| 収録物識別子 |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
2076-3417 |
| 出版者 |
|
|
出版者 |
MDPI |
|
言語 |
en |
| 関連情報 |
|
|
関連タイプ |
isIdenticalTo |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.3390/app12052410 |
| 権利情報 |
|
|
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
(CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
kokuA_2022_17.pdf (8.0 MB)
