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Prediction accuracy of Random Forest, XGBoost, LightGBM, and artificial neural network for shear resistance of post-installed anchors
http://hdl.handle.net/10258/0002000163
http://hdl.handle.net/10258/00020001633c9d99b4-2d83-4681-93ac-bacb418d15df
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
Machine_learning_231106.pdf (2.7 MB)
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| アイテムタイプ | 学術雑誌論文 / Journal Article.(1) | |||||||||||||||||||
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| 公開日 | 2023-12-21 | |||||||||||||||||||
| 書誌情報 |
en : Structures 巻 50, p. 1252-1263, ページ数 12, 発行日 2023-04 |
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| タイトル | ||||||||||||||||||||
| タイトル | Prediction accuracy of Random Forest, XGBoost, LightGBM, and artificial neural network for shear resistance of post-installed anchors | |||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 言語 | ||||||||||||||||||||
| 言語 | eng | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||||
| 主題 | Post installed anchor | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||||
| 主題 | Post installed reinforcing bar | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||||
| 主題 | Machine learning | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||||
| 主題 | Mechanical behavior | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||||
| 主題 | Dowel action | |||||||||||||||||||
| 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 | |||||||||||||||||||
| 著者 |
Suenaga D.
× Suenaga D.
× 髙瀬, 裕也
× Abe T.
× Orita G.
× Ando S.
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| 抄録 | ||||||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||||||
| 内容記述 | P ost installed anchors and reinforcing bars are used to connect equipment or to fasten strengthening members to re i nforced concrete ( structures For safety reasons , a ppropriate struc- tural design is critical . Recently, artificial intelligence (AI) and machine learning (ML) have been applied in various fields. According to previous studies , the bending strength of the RC beam and the bond strength of the surface can be predicted using ML. In this study, the mechanical behavior of post installed anchor s s ubjected to shear force were predicted using ML. F our algorithms were ap- plied in this study : R andom F orest (RF), XG B oost (XB), L ightGBM (LG), and an a rtificial neural network ( A NN). Moreover, the authors’ previous test results were used for the ML and testing . The number of specimens was t hirty two . The test parameters were the concrete compressive strength f c , diameter of the anchor bolt d d , type of adhesive, and tensile ratio r N . The values for f c and d d were set at 13.0 35.5 N/mm 2 and 1 3 25 mm, respectively. In this study, o ne epoxy adhesive and three cement based adhesive s were used . r N , which is the ratio o f the tensile stress to yield strength of the anchor bolt, was set to 0, 0.33, and 0.66. Consequently , the four algorithms could accurately predict the me- chanical behavior of the specimen when the parameters were within or close to the training data. However, the prediction agreements of RF, XB, and LG were not good for the behavior of specimen s whose parameters were not included i n the training data. Nevertheless , the A NN was able to rea- sonably predict the behavior of the se case s I t was concluded that the four algorithms can make good predictions when the parameters are within or close to the training data. However, when parameters outside the training data were used , the A NN was the best of the four algorithms used in this study |
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| 言語 | en | |||||||||||||||||||
| 出版者版へのリンク | ||||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| URL | https://doi.org/10.1016/j.istruc.2023.02.066 | |||||||||||||||||||
| DOI | ||||||||||||||||||||
| 関連タイプ | isVersionOf | |||||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||||||
| 関連識別子 | 10.1016/j.istruc.2023.02.066 | |||||||||||||||||||
| ISSN | ||||||||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||||||||
| 収録物識別子 | 23520124 | |||||||||||||||||||
| 権利 | ||||||||||||||||||||
| 権利情報 | © 2023 Institution of Structural Engineers | |||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 著者版フラグ | ||||||||||||||||||||
| 出版タイプ | AM | |||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||||||||||
