{"created":"2023-07-25T10:25:32.611759+00:00","id":5930,"links":{},"metadata":{"_buckets":{"deposit":"5e48dff7-52ad-40fd-a701-7786be45afa3"},"_deposit":{"created_by":15,"id":"5930","owners":[15],"pid":{"revision_id":0,"type":"depid","value":"5930"},"status":"published"},"_oai":{"id":"oai:air.repo.nii.ac.jp:00005930","sets":["1194:1462:1463"]},"author_link":[],"item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2022","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicVolumeNumber":"12","bibliographic_titles":[{"bibliographic_title":"APPLIED SCIENCES-BASEL","bibliographic_titleLang":"en"}]}]},"item_10001_description_5":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"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.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_10001_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"MDPI","subitem_publisher_language":"en"}]},"item_10001_relation_25":{"attribute_name":"関連情報","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.3390/app12052410","subitem_relation_type_select":"DOI"}}]},"item_10001_rights_15":{"attribute_name":"権利情報","attribute_value_mlt":[{"subitem_rights":"© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access\narticle distributed under the terms and conditions of the Creative Commons Attribution\n(CC BY) license (http://creativecommons.org/licenses/by/4.0/)."}]},"item_10001_version_type_20":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_1724099666130":{"attribute_name":"収録物識別子","attribute_value_mlt":[{"subitem_source_identifier":"2076-3417","subitem_source_identifier_type":"ISSN"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_creator":{"attribute_name":"作成者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kilic, Kursat","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Toriya, Hisatoshi","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Kosugi, Yoshino","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Adachi, Tsuyoshi","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Kawamura, Youhei","creatorNameLang":"en"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2023-02-23"}],"displaytype":"detail","filename":"kokuA_2022_17.pdf","filesize":[{"value":"8.0 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"kokuA_2022_17.pdf","url":"https://air.repo.nii.ac.jp/record/5930/files/kokuA_2022_17.pdf"},"version_id":"925ba0a2-5da0-42e6-bc78-ce457774bc97"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"EPB TBM","subitem_subject_scheme":"Other"},{"subitem_subject":" tool wear","subitem_subject_scheme":"Other"},{"subitem_subject":" deep learning","subitem_subject_scheme":"Other"},{"subitem_subject":" soft ground tunnelling","subitem_subject_scheme":"Other"},{"subitem_subject":" cutter life","subitem_subject_scheme":"Other"},{"subitem_subject":" operational parameters","subitem_subject_scheme":"Other"},{"subitem_subject":" convolutional neural network","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction","subitem_title_language":"en"}]},"item_type_id":"10001","owner":"15","path":["1463"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2023-02-23"},"publish_date":"2023-02-23","publish_status":"0","recid":"5930","relation_version_is_last":true,"title":["One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction"],"weko_creator_id":"15","weko_shared_id":-1},"updated":"2024-08-22T23:14:24.680424+00:00"}