Ultimate strength prediction of I-core sandwich plate based on BP neural network
Objectives In view of the incomplete evaluation of the ultimate strength of I-core sandwich panels in the past, a BP artificial neural network method is proposed to quantitatively determine the influence of relevant parameters on the ultimate strength of I-core sandwich panels. MethodsFirst, the ult...
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Gespeichert in:
| Autor*in: |
Yuwen WEI [verfasserIn] Qiang ZHONG [verfasserIn] Deyu WANG [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch ; Chinesisch |
| Erschienen: |
2022 |
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| Übergeordnetes Werk: |
In: Zhongguo Jianchuan Yanjiu - Editorial Office of Chinese Journal of Ship Research, 2017, 17(2022), 2, Seite 125-134 |
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| Übergeordnetes Werk: |
volume:17 ; year:2022 ; number:2 ; pages:125-134 |
| Links: |
|---|
| DOI / URN: |
10.19693/j.issn.1673-3185.02335 |
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DOAJ040887162 |
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| 520 | |a Objectives In view of the incomplete evaluation of the ultimate strength of I-core sandwich panels in the past, a BP artificial neural network method is proposed to quantitatively determine the influence of relevant parameters on the ultimate strength of I-core sandwich panels. MethodsFirst, the ultimate strength of I-core sandwich panels under axial compression are investigated using the nonlinear finite element method. Second, a BP neural network is constructed to predict the ultimate strength of I-core sandwich panels with different plate slenderness ratios between longitudinal webs, plate slenderness ratios of webs and column slenderness ratio of one longitudinal web. Finally, a formula for predicting the ultimate strength of I-core sandwich panels using the artificial neural network weight and bias method is proposed. ResultsThe mean square error MSE and correlation coefficient R of ultimate strength prediction using the BP neural network method are 0.001 2 and 0.981 8 respectively. The proposed neural network model has good prediction accuracy, and the maximum error is less than 10%. ConclusionsThis study can provide references for the application of I-core sandwich panels in hull structures. | ||
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| 700 | 0 | |a Deyu WANG |e verfasserin |4 aut | |
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10.19693/j.issn.1673-3185.02335 doi (DE-627)DOAJ040887162 (DE-599)DOAJ68819db2ee194d06a1db7168ee24bbe7 DE-627 ger DE-627 rakwb eng chi VM1-989 Yuwen WEI verfasserin aut Ultimate strength prediction of I-core sandwich plate based on BP neural network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives In view of the incomplete evaluation of the ultimate strength of I-core sandwich panels in the past, a BP artificial neural network method is proposed to quantitatively determine the influence of relevant parameters on the ultimate strength of I-core sandwich panels. MethodsFirst, the ultimate strength of I-core sandwich panels under axial compression are investigated using the nonlinear finite element method. Second, a BP neural network is constructed to predict the ultimate strength of I-core sandwich panels with different plate slenderness ratios between longitudinal webs, plate slenderness ratios of webs and column slenderness ratio of one longitudinal web. Finally, a formula for predicting the ultimate strength of I-core sandwich panels using the artificial neural network weight and bias method is proposed. ResultsThe mean square error MSE and correlation coefficient R of ultimate strength prediction using the BP neural network method are 0.001 2 and 0.981 8 respectively. The proposed neural network model has good prediction accuracy, and the maximum error is less than 10%. ConclusionsThis study can provide references for the application of I-core sandwich panels in hull structures. i-core sandwich panels bp neural network ultimate strength nonlinear finite element method prediction Naval architecture. Shipbuilding. Marine engineering Qiang ZHONG verfasserin aut Deyu WANG verfasserin aut In Zhongguo Jianchuan Yanjiu Editorial Office of Chinese Journal of Ship Research, 2017 17(2022), 2, Seite 125-134 (DE-627)1680976788 16733185 nnns volume:17 year:2022 number:2 pages:125-134 https://doi.org/10.19693/j.issn.1673-3185.02335 kostenfrei https://doaj.org/article/68819db2ee194d06a1db7168ee24bbe7 kostenfrei http://www.ship-research.com/cn/article/doi/10.19693/j.issn.1673-3185.02335 kostenfrei https://doaj.org/toc/1673-3185 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 17 2022 2 125-134 |
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10.19693/j.issn.1673-3185.02335 doi (DE-627)DOAJ040887162 (DE-599)DOAJ68819db2ee194d06a1db7168ee24bbe7 DE-627 ger DE-627 rakwb eng chi VM1-989 Yuwen WEI verfasserin aut Ultimate strength prediction of I-core sandwich plate based on BP neural network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives In view of the incomplete evaluation of the ultimate strength of I-core sandwich panels in the past, a BP artificial neural network method is proposed to quantitatively determine the influence of relevant parameters on the ultimate strength of I-core sandwich panels. MethodsFirst, the ultimate strength of I-core sandwich panels under axial compression are investigated using the nonlinear finite element method. Second, a BP neural network is constructed to predict the ultimate strength of I-core sandwich panels with different plate slenderness ratios between longitudinal webs, plate slenderness ratios of webs and column slenderness ratio of one longitudinal web. Finally, a formula for predicting the ultimate strength of I-core sandwich panels using the artificial neural network weight and bias method is proposed. ResultsThe mean square error MSE and correlation coefficient R of ultimate strength prediction using the BP neural network method are 0.001 2 and 0.981 8 respectively. The proposed neural network model has good prediction accuracy, and the maximum error is less than 10%. ConclusionsThis study can provide references for the application of I-core sandwich panels in hull structures. i-core sandwich panels bp neural network ultimate strength nonlinear finite element method prediction Naval architecture. Shipbuilding. Marine engineering Qiang ZHONG verfasserin aut Deyu WANG verfasserin aut In Zhongguo Jianchuan Yanjiu Editorial Office of Chinese Journal of Ship Research, 2017 17(2022), 2, Seite 125-134 (DE-627)1680976788 16733185 nnns volume:17 year:2022 number:2 pages:125-134 https://doi.org/10.19693/j.issn.1673-3185.02335 kostenfrei https://doaj.org/article/68819db2ee194d06a1db7168ee24bbe7 kostenfrei http://www.ship-research.com/cn/article/doi/10.19693/j.issn.1673-3185.02335 kostenfrei https://doaj.org/toc/1673-3185 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 17 2022 2 125-134 |
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10.19693/j.issn.1673-3185.02335 doi (DE-627)DOAJ040887162 (DE-599)DOAJ68819db2ee194d06a1db7168ee24bbe7 DE-627 ger DE-627 rakwb eng chi VM1-989 Yuwen WEI verfasserin aut Ultimate strength prediction of I-core sandwich plate based on BP neural network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives In view of the incomplete evaluation of the ultimate strength of I-core sandwich panels in the past, a BP artificial neural network method is proposed to quantitatively determine the influence of relevant parameters on the ultimate strength of I-core sandwich panels. MethodsFirst, the ultimate strength of I-core sandwich panels under axial compression are investigated using the nonlinear finite element method. Second, a BP neural network is constructed to predict the ultimate strength of I-core sandwich panels with different plate slenderness ratios between longitudinal webs, plate slenderness ratios of webs and column slenderness ratio of one longitudinal web. Finally, a formula for predicting the ultimate strength of I-core sandwich panels using the artificial neural network weight and bias method is proposed. ResultsThe mean square error MSE and correlation coefficient R of ultimate strength prediction using the BP neural network method are 0.001 2 and 0.981 8 respectively. The proposed neural network model has good prediction accuracy, and the maximum error is less than 10%. ConclusionsThis study can provide references for the application of I-core sandwich panels in hull structures. i-core sandwich panels bp neural network ultimate strength nonlinear finite element method prediction Naval architecture. Shipbuilding. Marine engineering Qiang ZHONG verfasserin aut Deyu WANG verfasserin aut In Zhongguo Jianchuan Yanjiu Editorial Office of Chinese Journal of Ship Research, 2017 17(2022), 2, Seite 125-134 (DE-627)1680976788 16733185 nnns volume:17 year:2022 number:2 pages:125-134 https://doi.org/10.19693/j.issn.1673-3185.02335 kostenfrei https://doaj.org/article/68819db2ee194d06a1db7168ee24bbe7 kostenfrei http://www.ship-research.com/cn/article/doi/10.19693/j.issn.1673-3185.02335 kostenfrei https://doaj.org/toc/1673-3185 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 17 2022 2 125-134 |
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10.19693/j.issn.1673-3185.02335 doi (DE-627)DOAJ040887162 (DE-599)DOAJ68819db2ee194d06a1db7168ee24bbe7 DE-627 ger DE-627 rakwb eng chi VM1-989 Yuwen WEI verfasserin aut Ultimate strength prediction of I-core sandwich plate based on BP neural network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives In view of the incomplete evaluation of the ultimate strength of I-core sandwich panels in the past, a BP artificial neural network method is proposed to quantitatively determine the influence of relevant parameters on the ultimate strength of I-core sandwich panels. MethodsFirst, the ultimate strength of I-core sandwich panels under axial compression are investigated using the nonlinear finite element method. Second, a BP neural network is constructed to predict the ultimate strength of I-core sandwich panels with different plate slenderness ratios between longitudinal webs, plate slenderness ratios of webs and column slenderness ratio of one longitudinal web. Finally, a formula for predicting the ultimate strength of I-core sandwich panels using the artificial neural network weight and bias method is proposed. ResultsThe mean square error MSE and correlation coefficient R of ultimate strength prediction using the BP neural network method are 0.001 2 and 0.981 8 respectively. The proposed neural network model has good prediction accuracy, and the maximum error is less than 10%. ConclusionsThis study can provide references for the application of I-core sandwich panels in hull structures. i-core sandwich panels bp neural network ultimate strength nonlinear finite element method prediction Naval architecture. Shipbuilding. Marine engineering Qiang ZHONG verfasserin aut Deyu WANG verfasserin aut In Zhongguo Jianchuan Yanjiu Editorial Office of Chinese Journal of Ship Research, 2017 17(2022), 2, Seite 125-134 (DE-627)1680976788 16733185 nnns volume:17 year:2022 number:2 pages:125-134 https://doi.org/10.19693/j.issn.1673-3185.02335 kostenfrei https://doaj.org/article/68819db2ee194d06a1db7168ee24bbe7 kostenfrei http://www.ship-research.com/cn/article/doi/10.19693/j.issn.1673-3185.02335 kostenfrei https://doaj.org/toc/1673-3185 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 17 2022 2 125-134 |
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10.19693/j.issn.1673-3185.02335 doi (DE-627)DOAJ040887162 (DE-599)DOAJ68819db2ee194d06a1db7168ee24bbe7 DE-627 ger DE-627 rakwb eng chi VM1-989 Yuwen WEI verfasserin aut Ultimate strength prediction of I-core sandwich plate based on BP neural network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives In view of the incomplete evaluation of the ultimate strength of I-core sandwich panels in the past, a BP artificial neural network method is proposed to quantitatively determine the influence of relevant parameters on the ultimate strength of I-core sandwich panels. MethodsFirst, the ultimate strength of I-core sandwich panels under axial compression are investigated using the nonlinear finite element method. Second, a BP neural network is constructed to predict the ultimate strength of I-core sandwich panels with different plate slenderness ratios between longitudinal webs, plate slenderness ratios of webs and column slenderness ratio of one longitudinal web. Finally, a formula for predicting the ultimate strength of I-core sandwich panels using the artificial neural network weight and bias method is proposed. ResultsThe mean square error MSE and correlation coefficient R of ultimate strength prediction using the BP neural network method are 0.001 2 and 0.981 8 respectively. The proposed neural network model has good prediction accuracy, and the maximum error is less than 10%. ConclusionsThis study can provide references for the application of I-core sandwich panels in hull structures. i-core sandwich panels bp neural network ultimate strength nonlinear finite element method prediction Naval architecture. Shipbuilding. Marine engineering Qiang ZHONG verfasserin aut Deyu WANG verfasserin aut In Zhongguo Jianchuan Yanjiu Editorial Office of Chinese Journal of Ship Research, 2017 17(2022), 2, Seite 125-134 (DE-627)1680976788 16733185 nnns volume:17 year:2022 number:2 pages:125-134 https://doi.org/10.19693/j.issn.1673-3185.02335 kostenfrei https://doaj.org/article/68819db2ee194d06a1db7168ee24bbe7 kostenfrei http://www.ship-research.com/cn/article/doi/10.19693/j.issn.1673-3185.02335 kostenfrei https://doaj.org/toc/1673-3185 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 17 2022 2 125-134 |
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MethodsFirst, the ultimate strength of I-core sandwich panels under axial compression are investigated using the nonlinear finite element method. Second, a BP neural network is constructed to predict the ultimate strength of I-core sandwich panels with different plate slenderness ratios between longitudinal webs, plate slenderness ratios of webs and column slenderness ratio of one longitudinal web. Finally, a formula for predicting the ultimate strength of I-core sandwich panels using the artificial neural network weight and bias method is proposed. ResultsThe mean square error MSE and correlation coefficient R of ultimate strength prediction using the BP neural network method are 0.001 2 and 0.981 8 respectively. The proposed neural network model has good prediction accuracy, and the maximum error is less than 10%. 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Ultimate strength prediction of I-core sandwich plate based on BP neural network |
| abstract |
Objectives In view of the incomplete evaluation of the ultimate strength of I-core sandwich panels in the past, a BP artificial neural network method is proposed to quantitatively determine the influence of relevant parameters on the ultimate strength of I-core sandwich panels. MethodsFirst, the ultimate strength of I-core sandwich panels under axial compression are investigated using the nonlinear finite element method. Second, a BP neural network is constructed to predict the ultimate strength of I-core sandwich panels with different plate slenderness ratios between longitudinal webs, plate slenderness ratios of webs and column slenderness ratio of one longitudinal web. Finally, a formula for predicting the ultimate strength of I-core sandwich panels using the artificial neural network weight and bias method is proposed. ResultsThe mean square error MSE and correlation coefficient R of ultimate strength prediction using the BP neural network method are 0.001 2 and 0.981 8 respectively. The proposed neural network model has good prediction accuracy, and the maximum error is less than 10%. ConclusionsThis study can provide references for the application of I-core sandwich panels in hull structures. |
| abstractGer |
Objectives In view of the incomplete evaluation of the ultimate strength of I-core sandwich panels in the past, a BP artificial neural network method is proposed to quantitatively determine the influence of relevant parameters on the ultimate strength of I-core sandwich panels. MethodsFirst, the ultimate strength of I-core sandwich panels under axial compression are investigated using the nonlinear finite element method. Second, a BP neural network is constructed to predict the ultimate strength of I-core sandwich panels with different plate slenderness ratios between longitudinal webs, plate slenderness ratios of webs and column slenderness ratio of one longitudinal web. Finally, a formula for predicting the ultimate strength of I-core sandwich panels using the artificial neural network weight and bias method is proposed. ResultsThe mean square error MSE and correlation coefficient R of ultimate strength prediction using the BP neural network method are 0.001 2 and 0.981 8 respectively. The proposed neural network model has good prediction accuracy, and the maximum error is less than 10%. ConclusionsThis study can provide references for the application of I-core sandwich panels in hull structures. |
| abstract_unstemmed |
Objectives In view of the incomplete evaluation of the ultimate strength of I-core sandwich panels in the past, a BP artificial neural network method is proposed to quantitatively determine the influence of relevant parameters on the ultimate strength of I-core sandwich panels. MethodsFirst, the ultimate strength of I-core sandwich panels under axial compression are investigated using the nonlinear finite element method. Second, a BP neural network is constructed to predict the ultimate strength of I-core sandwich panels with different plate slenderness ratios between longitudinal webs, plate slenderness ratios of webs and column slenderness ratio of one longitudinal web. Finally, a formula for predicting the ultimate strength of I-core sandwich panels using the artificial neural network weight and bias method is proposed. ResultsThe mean square error MSE and correlation coefficient R of ultimate strength prediction using the BP neural network method are 0.001 2 and 0.981 8 respectively. The proposed neural network model has good prediction accuracy, and the maximum error is less than 10%. ConclusionsThis study can provide references for the application of I-core sandwich panels in hull structures. |
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GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 |
| container_issue |
2 |
| title_short |
Ultimate strength prediction of I-core sandwich plate based on BP neural network |
| url |
https://doi.org/10.19693/j.issn.1673-3185.02335 https://doaj.org/article/68819db2ee194d06a1db7168ee24bbe7 http://www.ship-research.com/cn/article/doi/10.19693/j.issn.1673-3185.02335 https://doaj.org/toc/1673-3185 |
| remote_bool |
true |
| author2 |
Qiang ZHONG Deyu WANG |
| author2Str |
Qiang ZHONG Deyu WANG |
| ppnlink |
1680976788 |
| callnumber-subject |
VM - Naval Architecture, Shipbuilding, Marine Engineering |
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c |
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false |
| doi_str |
10.19693/j.issn.1673-3185.02335 |
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VM1-989 |
| up_date |
2024-07-03T17:17:43.088Z |
| _version_ |
1803579097705611264 |
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7.4007006 |