Exploiting the adaptive neural fuzzy inference system for predicting the effect of notch depth on elastic new strain-concentration factor under combined loading
Abstract In this paper, a novel machine-learning based models are presented to predict the effect of notch depth on elastic new strain-concentration factor of rectangular bars with single edge U-notch under combined loading of static tension and pure bending. Regarding the importance of this study,...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Al-Jarrah, Rami [verfasserIn] Tlilan, Hitham [verfasserIn] Khreishah, Abdallah [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. corrected publication 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Cluster computing - Springer US, 1998, 27(2023), 3 vom: 11. Sept., Seite 3055-3073 |
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| Übergeordnetes Werk: |
volume:27 ; year:2023 ; number:3 ; day:11 ; month:09 ; pages:3055-3073 |
| Links: |
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| DOI / URN: |
10.1007/s10586-023-04131-6 |
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| Katalog-ID: |
SPR056033915 |
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| 520 | |a Abstract In this paper, a novel machine-learning based models are presented to predict the effect of notch depth on elastic new strain-concentration factor of rectangular bars with single edge U-notch under combined loading of static tension and pure bending. Regarding the importance of this study, the database with 162 samples is utilized to develop the new methodology of machine learning based models. The database includes the notch radius, the Poisson’s ratio, and the thick ratio that represent the influential inputs. The predicted key feature is the elastic new strain-concentration factor under combined loading of static tension and pure bending. These samples were tested with high precision and the predicted values of SNCF were obtained. For comparison, adaptive neural fuzzy inference system, artificial neural network, fine tree, ensemble boosted tree, and ensemble optimized bagged tree were designed and developed in this study. To evaluate and compare the performance of the models, four statistical indices of MAE, MSE, root mean square error (RMSE)and determination coefficient (R) were utilized. Based on the results, all models can predict the SNCF appropriately. However, the Ensemble optimized Bagged tree model had a better performance than other models and it had a significant priority in term of prediction accuracy. Finally, the results indicated that the elastic SNCF increased with increasing notch depth from 0.2 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.7 and sharply decreases with increasing notch depth for shallow notches (0.8 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.95). | ||
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| 700 | 1 | |a Khreishah, Abdallah |e verfasserin |4 aut | |
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10.1007/s10586-023-04131-6 doi (DE-627)SPR056033915 (SPR)s10586-023-04131-6-e DE-627 ger DE-627 rakwb eng 004 VZ 54.50 bkl 54.32 bkl 54.25 bkl Al-Jarrah, Rami verfasserin aut Exploiting the adaptive neural fuzzy inference system for predicting the effect of notch depth on elastic new strain-concentration factor under combined loading 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. corrected publication 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this paper, a novel machine-learning based models are presented to predict the effect of notch depth on elastic new strain-concentration factor of rectangular bars with single edge U-notch under combined loading of static tension and pure bending. Regarding the importance of this study, the database with 162 samples is utilized to develop the new methodology of machine learning based models. The database includes the notch radius, the Poisson’s ratio, and the thick ratio that represent the influential inputs. The predicted key feature is the elastic new strain-concentration factor under combined loading of static tension and pure bending. These samples were tested with high precision and the predicted values of SNCF were obtained. For comparison, adaptive neural fuzzy inference system, artificial neural network, fine tree, ensemble boosted tree, and ensemble optimized bagged tree were designed and developed in this study. To evaluate and compare the performance of the models, four statistical indices of MAE, MSE, root mean square error (RMSE)and determination coefficient (R) were utilized. Based on the results, all models can predict the SNCF appropriately. However, the Ensemble optimized Bagged tree model had a better performance than other models and it had a significant priority in term of prediction accuracy. Finally, the results indicated that the elastic SNCF increased with increasing notch depth from 0.2 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.7 and sharply decreases with increasing notch depth for shallow notches (0.8 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.95). Machine learning (dpeaa)DE-He213 Elastic new strain-concentration factor (dpeaa)DE-He213 Predictive modeling (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Tlilan, Hitham verfasserin aut Khreishah, Abdallah verfasserin aut Enthalten in Cluster computing Springer US, 1998 27(2023), 3 vom: 11. Sept., Seite 3055-3073 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:27 year:2023 number:3 day:11 month:09 pages:3055-3073 https://dx.doi.org/10.1007/s10586-023-04131-6 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 VZ 54.32 VZ 54.25 VZ AR 27 2023 3 11 09 3055-3073 |
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10.1007/s10586-023-04131-6 doi (DE-627)SPR056033915 (SPR)s10586-023-04131-6-e DE-627 ger DE-627 rakwb eng 004 VZ 54.50 bkl 54.32 bkl 54.25 bkl Al-Jarrah, Rami verfasserin aut Exploiting the adaptive neural fuzzy inference system for predicting the effect of notch depth on elastic new strain-concentration factor under combined loading 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. corrected publication 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this paper, a novel machine-learning based models are presented to predict the effect of notch depth on elastic new strain-concentration factor of rectangular bars with single edge U-notch under combined loading of static tension and pure bending. Regarding the importance of this study, the database with 162 samples is utilized to develop the new methodology of machine learning based models. The database includes the notch radius, the Poisson’s ratio, and the thick ratio that represent the influential inputs. The predicted key feature is the elastic new strain-concentration factor under combined loading of static tension and pure bending. These samples were tested with high precision and the predicted values of SNCF were obtained. For comparison, adaptive neural fuzzy inference system, artificial neural network, fine tree, ensemble boosted tree, and ensemble optimized bagged tree were designed and developed in this study. To evaluate and compare the performance of the models, four statistical indices of MAE, MSE, root mean square error (RMSE)and determination coefficient (R) were utilized. Based on the results, all models can predict the SNCF appropriately. However, the Ensemble optimized Bagged tree model had a better performance than other models and it had a significant priority in term of prediction accuracy. Finally, the results indicated that the elastic SNCF increased with increasing notch depth from 0.2 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.7 and sharply decreases with increasing notch depth for shallow notches (0.8 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.95). Machine learning (dpeaa)DE-He213 Elastic new strain-concentration factor (dpeaa)DE-He213 Predictive modeling (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Tlilan, Hitham verfasserin aut Khreishah, Abdallah verfasserin aut Enthalten in Cluster computing Springer US, 1998 27(2023), 3 vom: 11. Sept., Seite 3055-3073 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:27 year:2023 number:3 day:11 month:09 pages:3055-3073 https://dx.doi.org/10.1007/s10586-023-04131-6 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 VZ 54.32 VZ 54.25 VZ AR 27 2023 3 11 09 3055-3073 |
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10.1007/s10586-023-04131-6 doi (DE-627)SPR056033915 (SPR)s10586-023-04131-6-e DE-627 ger DE-627 rakwb eng 004 VZ 54.50 bkl 54.32 bkl 54.25 bkl Al-Jarrah, Rami verfasserin aut Exploiting the adaptive neural fuzzy inference system for predicting the effect of notch depth on elastic new strain-concentration factor under combined loading 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. corrected publication 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this paper, a novel machine-learning based models are presented to predict the effect of notch depth on elastic new strain-concentration factor of rectangular bars with single edge U-notch under combined loading of static tension and pure bending. Regarding the importance of this study, the database with 162 samples is utilized to develop the new methodology of machine learning based models. The database includes the notch radius, the Poisson’s ratio, and the thick ratio that represent the influential inputs. The predicted key feature is the elastic new strain-concentration factor under combined loading of static tension and pure bending. These samples were tested with high precision and the predicted values of SNCF were obtained. For comparison, adaptive neural fuzzy inference system, artificial neural network, fine tree, ensemble boosted tree, and ensemble optimized bagged tree were designed and developed in this study. To evaluate and compare the performance of the models, four statistical indices of MAE, MSE, root mean square error (RMSE)and determination coefficient (R) were utilized. Based on the results, all models can predict the SNCF appropriately. However, the Ensemble optimized Bagged tree model had a better performance than other models and it had a significant priority in term of prediction accuracy. Finally, the results indicated that the elastic SNCF increased with increasing notch depth from 0.2 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.7 and sharply decreases with increasing notch depth for shallow notches (0.8 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.95). Machine learning (dpeaa)DE-He213 Elastic new strain-concentration factor (dpeaa)DE-He213 Predictive modeling (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Tlilan, Hitham verfasserin aut Khreishah, Abdallah verfasserin aut Enthalten in Cluster computing Springer US, 1998 27(2023), 3 vom: 11. Sept., Seite 3055-3073 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:27 year:2023 number:3 day:11 month:09 pages:3055-3073 https://dx.doi.org/10.1007/s10586-023-04131-6 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 VZ 54.32 VZ 54.25 VZ AR 27 2023 3 11 09 3055-3073 |
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10.1007/s10586-023-04131-6 doi (DE-627)SPR056033915 (SPR)s10586-023-04131-6-e DE-627 ger DE-627 rakwb eng 004 VZ 54.50 bkl 54.32 bkl 54.25 bkl Al-Jarrah, Rami verfasserin aut Exploiting the adaptive neural fuzzy inference system for predicting the effect of notch depth on elastic new strain-concentration factor under combined loading 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. corrected publication 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this paper, a novel machine-learning based models are presented to predict the effect of notch depth on elastic new strain-concentration factor of rectangular bars with single edge U-notch under combined loading of static tension and pure bending. Regarding the importance of this study, the database with 162 samples is utilized to develop the new methodology of machine learning based models. The database includes the notch radius, the Poisson’s ratio, and the thick ratio that represent the influential inputs. The predicted key feature is the elastic new strain-concentration factor under combined loading of static tension and pure bending. These samples were tested with high precision and the predicted values of SNCF were obtained. For comparison, adaptive neural fuzzy inference system, artificial neural network, fine tree, ensemble boosted tree, and ensemble optimized bagged tree were designed and developed in this study. To evaluate and compare the performance of the models, four statistical indices of MAE, MSE, root mean square error (RMSE)and determination coefficient (R) were utilized. Based on the results, all models can predict the SNCF appropriately. However, the Ensemble optimized Bagged tree model had a better performance than other models and it had a significant priority in term of prediction accuracy. Finally, the results indicated that the elastic SNCF increased with increasing notch depth from 0.2 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.7 and sharply decreases with increasing notch depth for shallow notches (0.8 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.95). Machine learning (dpeaa)DE-He213 Elastic new strain-concentration factor (dpeaa)DE-He213 Predictive modeling (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Tlilan, Hitham verfasserin aut Khreishah, Abdallah verfasserin aut Enthalten in Cluster computing Springer US, 1998 27(2023), 3 vom: 11. Sept., Seite 3055-3073 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:27 year:2023 number:3 day:11 month:09 pages:3055-3073 https://dx.doi.org/10.1007/s10586-023-04131-6 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 VZ 54.32 VZ 54.25 VZ AR 27 2023 3 11 09 3055-3073 |
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10.1007/s10586-023-04131-6 doi (DE-627)SPR056033915 (SPR)s10586-023-04131-6-e DE-627 ger DE-627 rakwb eng 004 VZ 54.50 bkl 54.32 bkl 54.25 bkl Al-Jarrah, Rami verfasserin aut Exploiting the adaptive neural fuzzy inference system for predicting the effect of notch depth on elastic new strain-concentration factor under combined loading 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. corrected publication 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In this paper, a novel machine-learning based models are presented to predict the effect of notch depth on elastic new strain-concentration factor of rectangular bars with single edge U-notch under combined loading of static tension and pure bending. Regarding the importance of this study, the database with 162 samples is utilized to develop the new methodology of machine learning based models. The database includes the notch radius, the Poisson’s ratio, and the thick ratio that represent the influential inputs. The predicted key feature is the elastic new strain-concentration factor under combined loading of static tension and pure bending. These samples were tested with high precision and the predicted values of SNCF were obtained. For comparison, adaptive neural fuzzy inference system, artificial neural network, fine tree, ensemble boosted tree, and ensemble optimized bagged tree were designed and developed in this study. To evaluate and compare the performance of the models, four statistical indices of MAE, MSE, root mean square error (RMSE)and determination coefficient (R) were utilized. Based on the results, all models can predict the SNCF appropriately. However, the Ensemble optimized Bagged tree model had a better performance than other models and it had a significant priority in term of prediction accuracy. Finally, the results indicated that the elastic SNCF increased with increasing notch depth from 0.2 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.7 and sharply decreases with increasing notch depth for shallow notches (0.8 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.95). Machine learning (dpeaa)DE-He213 Elastic new strain-concentration factor (dpeaa)DE-He213 Predictive modeling (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 Tlilan, Hitham verfasserin aut Khreishah, Abdallah verfasserin aut Enthalten in Cluster computing Springer US, 1998 27(2023), 3 vom: 11. Sept., Seite 3055-3073 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:27 year:2023 number:3 day:11 month:09 pages:3055-3073 https://dx.doi.org/10.1007/s10586-023-04131-6 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 VZ 54.32 VZ 54.25 VZ AR 27 2023 3 11 09 3055-3073 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR056033915</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240530064735.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240530s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10586-023-04131-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR056033915</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10586-023-04131-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.50</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.32</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.25</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Al-Jarrah, Rami</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Exploiting the adaptive neural fuzzy inference system for predicting the effect of notch depth on elastic new strain-concentration factor under combined loading</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. corrected publication 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this paper, a novel machine-learning based models are presented to predict the effect of notch depth on elastic new strain-concentration factor of rectangular bars with single edge U-notch under combined loading of static tension and pure bending. Regarding the importance of this study, the database with 162 samples is utilized to develop the new methodology of machine learning based models. The database includes the notch radius, the Poisson’s ratio, and the thick ratio that represent the influential inputs. The predicted key feature is the elastic new strain-concentration factor under combined loading of static tension and pure bending. These samples were tested with high precision and the predicted values of SNCF were obtained. For comparison, adaptive neural fuzzy inference system, artificial neural network, fine tree, ensemble boosted tree, and ensemble optimized bagged tree were designed and developed in this study. To evaluate and compare the performance of the models, four statistical indices of MAE, MSE, root mean square error (RMSE)and determination coefficient (R) were utilized. Based on the results, all models can predict the SNCF appropriately. However, the Ensemble optimized Bagged tree model had a better performance than other models and it had a significant priority in term of prediction accuracy. Finally, the results indicated that the elastic SNCF increased with increasing notch depth from 0.2 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.7 and sharply decreases with increasing notch depth for shallow notches (0.8 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.95).</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Elastic new strain-concentration factor</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Predictive modeling</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mechanical properties</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tlilan, Hitham</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Khreishah, Abdallah</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Cluster computing</subfield><subfield code="d">Springer US, 1998</subfield><subfield code="g">27(2023), 3 vom: 11. 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Al-Jarrah, Rami |
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004 VZ 54.50 bkl 54.32 bkl 54.25 bkl Exploiting the adaptive neural fuzzy inference system for predicting the effect of notch depth on elastic new strain-concentration factor under combined loading Machine learning (dpeaa)DE-He213 Elastic new strain-concentration factor (dpeaa)DE-He213 Predictive modeling (dpeaa)DE-He213 Mechanical properties (dpeaa)DE-He213 |
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exploiting the adaptive neural fuzzy inference system for predicting the effect of notch depth on elastic new strain-concentration factor under combined loading |
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Exploiting the adaptive neural fuzzy inference system for predicting the effect of notch depth on elastic new strain-concentration factor under combined loading |
| abstract |
Abstract In this paper, a novel machine-learning based models are presented to predict the effect of notch depth on elastic new strain-concentration factor of rectangular bars with single edge U-notch under combined loading of static tension and pure bending. Regarding the importance of this study, the database with 162 samples is utilized to develop the new methodology of machine learning based models. The database includes the notch radius, the Poisson’s ratio, and the thick ratio that represent the influential inputs. The predicted key feature is the elastic new strain-concentration factor under combined loading of static tension and pure bending. These samples were tested with high precision and the predicted values of SNCF were obtained. For comparison, adaptive neural fuzzy inference system, artificial neural network, fine tree, ensemble boosted tree, and ensemble optimized bagged tree were designed and developed in this study. To evaluate and compare the performance of the models, four statistical indices of MAE, MSE, root mean square error (RMSE)and determination coefficient (R) were utilized. Based on the results, all models can predict the SNCF appropriately. However, the Ensemble optimized Bagged tree model had a better performance than other models and it had a significant priority in term of prediction accuracy. Finally, the results indicated that the elastic SNCF increased with increasing notch depth from 0.2 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.7 and sharply decreases with increasing notch depth for shallow notches (0.8 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.95). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. corrected publication 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract In this paper, a novel machine-learning based models are presented to predict the effect of notch depth on elastic new strain-concentration factor of rectangular bars with single edge U-notch under combined loading of static tension and pure bending. Regarding the importance of this study, the database with 162 samples is utilized to develop the new methodology of machine learning based models. The database includes the notch radius, the Poisson’s ratio, and the thick ratio that represent the influential inputs. The predicted key feature is the elastic new strain-concentration factor under combined loading of static tension and pure bending. These samples were tested with high precision and the predicted values of SNCF were obtained. For comparison, adaptive neural fuzzy inference system, artificial neural network, fine tree, ensemble boosted tree, and ensemble optimized bagged tree were designed and developed in this study. To evaluate and compare the performance of the models, four statistical indices of MAE, MSE, root mean square error (RMSE)and determination coefficient (R) were utilized. Based on the results, all models can predict the SNCF appropriately. However, the Ensemble optimized Bagged tree model had a better performance than other models and it had a significant priority in term of prediction accuracy. Finally, the results indicated that the elastic SNCF increased with increasing notch depth from 0.2 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.7 and sharply decreases with increasing notch depth for shallow notches (0.8 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.95). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. corrected publication 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract In this paper, a novel machine-learning based models are presented to predict the effect of notch depth on elastic new strain-concentration factor of rectangular bars with single edge U-notch under combined loading of static tension and pure bending. Regarding the importance of this study, the database with 162 samples is utilized to develop the new methodology of machine learning based models. The database includes the notch radius, the Poisson’s ratio, and the thick ratio that represent the influential inputs. The predicted key feature is the elastic new strain-concentration factor under combined loading of static tension and pure bending. These samples were tested with high precision and the predicted values of SNCF were obtained. For comparison, adaptive neural fuzzy inference system, artificial neural network, fine tree, ensemble boosted tree, and ensemble optimized bagged tree were designed and developed in this study. To evaluate and compare the performance of the models, four statistical indices of MAE, MSE, root mean square error (RMSE)and determination coefficient (R) were utilized. Based on the results, all models can predict the SNCF appropriately. However, the Ensemble optimized Bagged tree model had a better performance than other models and it had a significant priority in term of prediction accuracy. Finally, the results indicated that the elastic SNCF increased with increasing notch depth from 0.2 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.7 and sharply decreases with increasing notch depth for shallow notches (0.8 ≤ $ h_{o} $/$ H_{o} $ ≤ 0.95). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. corrected publication 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Exploiting the adaptive neural fuzzy inference system for predicting the effect of notch depth on elastic new strain-concentration factor under combined loading |
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https://dx.doi.org/10.1007/s10586-023-04131-6 |
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Tlilan, Hitham Khreishah, Abdallah |
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Tlilan, Hitham Khreishah, Abdallah |
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10.1007/s10586-023-04131-6 |
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| score |
7.3995867 |