Modeling and predictive analyses related to piezometric level in an earth dam using a back propagation neural network in comparison on non-linear regression
Abstract The objective of the dam safety guidelines and rules is to provide design engineers with information on dam planning, design, construction, operation and maintenance activities, procedures and requirements. However, the hydraulic and mechanical properties may react differently than expected...
Ausführliche Beschreibung
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| Autor*in: |
Leyla, Harbi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor 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: Modeling earth systems and environment - Berlin : Springer, 2015, 9(2022), 1 vom: 10. Okt., Seite 1169-1180 |
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| Übergeordnetes Werk: |
volume:9 ; year:2022 ; number:1 ; day:10 ; month:10 ; pages:1169-1180 |
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| DOI / URN: |
10.1007/s40808-022-01558-5 |
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| Katalog-ID: |
SPR049441582 |
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| 520 | |a Abstract The objective of the dam safety guidelines and rules is to provide design engineers with information on dam planning, design, construction, operation and maintenance activities, procedures and requirements. However, the hydraulic and mechanical properties may react differently than expected based on the design or in rare case dam fail. The purpose of the dam monitoring is to illustrate the dam response face different solicitations. Furthermore, the results of monitoring dam are used to determine the kind and timing of maintenance or repair procedures while also keeping tabs on how the structure's behavior evolves over time. This can make it possible to compare the performance of the structure to project forecasts. In this paper, a typical zoned dam with central core El-Izdihar dam, located in the northwest of Algeria was considered as an example. The non-linear regression models combined with the theory of Back Propagation Neural Network (BPNN) were adopted to ascertain the optimum model for the prediction of the piezometric levels in the dam. The performance of the model has been judged using the correlation coefficient R, and Root Mean Square Error (RMSE). The achieved result shows that the BPNN model has the ability to predict piezometric level, and make that more accurate and reasonable even in case of nonlinear analyses. | ||
| 650 | 4 | |a El Izdihar dam |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Artificial neural network |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Regression analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Piezometry |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Nadia, Smail |4 aut | |
| 700 | 1 | |a Bouchrit, Rouissat |4 aut | |
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10.1007/s40808-022-01558-5 doi (DE-627)SPR049441582 (SPR)s40808-022-01558-5-e DE-627 ger DE-627 rakwb eng Leyla, Harbi verfasserin (orcid)0000-0002-4009-9952 aut Modeling and predictive analyses related to piezometric level in an earth dam using a back propagation neural network in comparison on non-linear regression 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor 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 The objective of the dam safety guidelines and rules is to provide design engineers with information on dam planning, design, construction, operation and maintenance activities, procedures and requirements. However, the hydraulic and mechanical properties may react differently than expected based on the design or in rare case dam fail. The purpose of the dam monitoring is to illustrate the dam response face different solicitations. Furthermore, the results of monitoring dam are used to determine the kind and timing of maintenance or repair procedures while also keeping tabs on how the structure's behavior evolves over time. This can make it possible to compare the performance of the structure to project forecasts. In this paper, a typical zoned dam with central core El-Izdihar dam, located in the northwest of Algeria was considered as an example. The non-linear regression models combined with the theory of Back Propagation Neural Network (BPNN) were adopted to ascertain the optimum model for the prediction of the piezometric levels in the dam. The performance of the model has been judged using the correlation coefficient R, and Root Mean Square Error (RMSE). The achieved result shows that the BPNN model has the ability to predict piezometric level, and make that more accurate and reasonable even in case of nonlinear analyses. El Izdihar dam (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Piezometry (dpeaa)DE-He213 Nadia, Smail aut Bouchrit, Rouissat aut Enthalten in Modeling earth systems and environment Berlin : Springer, 2015 9(2022), 1 vom: 10. Okt., Seite 1169-1180 (DE-627)825736587 (DE-600)2821317-8 2363-6211 nnns volume:9 year:2022 number:1 day:10 month:10 pages:1169-1180 https://dx.doi.org/10.1007/s40808-022-01558-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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 AR 9 2022 1 10 10 1169-1180 |
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10.1007/s40808-022-01558-5 doi (DE-627)SPR049441582 (SPR)s40808-022-01558-5-e DE-627 ger DE-627 rakwb eng Leyla, Harbi verfasserin (orcid)0000-0002-4009-9952 aut Modeling and predictive analyses related to piezometric level in an earth dam using a back propagation neural network in comparison on non-linear regression 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor 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 The objective of the dam safety guidelines and rules is to provide design engineers with information on dam planning, design, construction, operation and maintenance activities, procedures and requirements. However, the hydraulic and mechanical properties may react differently than expected based on the design or in rare case dam fail. The purpose of the dam monitoring is to illustrate the dam response face different solicitations. Furthermore, the results of monitoring dam are used to determine the kind and timing of maintenance or repair procedures while also keeping tabs on how the structure's behavior evolves over time. This can make it possible to compare the performance of the structure to project forecasts. In this paper, a typical zoned dam with central core El-Izdihar dam, located in the northwest of Algeria was considered as an example. The non-linear regression models combined with the theory of Back Propagation Neural Network (BPNN) were adopted to ascertain the optimum model for the prediction of the piezometric levels in the dam. The performance of the model has been judged using the correlation coefficient R, and Root Mean Square Error (RMSE). The achieved result shows that the BPNN model has the ability to predict piezometric level, and make that more accurate and reasonable even in case of nonlinear analyses. El Izdihar dam (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Piezometry (dpeaa)DE-He213 Nadia, Smail aut Bouchrit, Rouissat aut Enthalten in Modeling earth systems and environment Berlin : Springer, 2015 9(2022), 1 vom: 10. Okt., Seite 1169-1180 (DE-627)825736587 (DE-600)2821317-8 2363-6211 nnns volume:9 year:2022 number:1 day:10 month:10 pages:1169-1180 https://dx.doi.org/10.1007/s40808-022-01558-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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 AR 9 2022 1 10 10 1169-1180 |
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10.1007/s40808-022-01558-5 doi (DE-627)SPR049441582 (SPR)s40808-022-01558-5-e DE-627 ger DE-627 rakwb eng Leyla, Harbi verfasserin (orcid)0000-0002-4009-9952 aut Modeling and predictive analyses related to piezometric level in an earth dam using a back propagation neural network in comparison on non-linear regression 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor 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 The objective of the dam safety guidelines and rules is to provide design engineers with information on dam planning, design, construction, operation and maintenance activities, procedures and requirements. However, the hydraulic and mechanical properties may react differently than expected based on the design or in rare case dam fail. The purpose of the dam monitoring is to illustrate the dam response face different solicitations. Furthermore, the results of monitoring dam are used to determine the kind and timing of maintenance or repair procedures while also keeping tabs on how the structure's behavior evolves over time. This can make it possible to compare the performance of the structure to project forecasts. In this paper, a typical zoned dam with central core El-Izdihar dam, located in the northwest of Algeria was considered as an example. The non-linear regression models combined with the theory of Back Propagation Neural Network (BPNN) were adopted to ascertain the optimum model for the prediction of the piezometric levels in the dam. The performance of the model has been judged using the correlation coefficient R, and Root Mean Square Error (RMSE). The achieved result shows that the BPNN model has the ability to predict piezometric level, and make that more accurate and reasonable even in case of nonlinear analyses. El Izdihar dam (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Piezometry (dpeaa)DE-He213 Nadia, Smail aut Bouchrit, Rouissat aut Enthalten in Modeling earth systems and environment Berlin : Springer, 2015 9(2022), 1 vom: 10. Okt., Seite 1169-1180 (DE-627)825736587 (DE-600)2821317-8 2363-6211 nnns volume:9 year:2022 number:1 day:10 month:10 pages:1169-1180 https://dx.doi.org/10.1007/s40808-022-01558-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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 AR 9 2022 1 10 10 1169-1180 |
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10.1007/s40808-022-01558-5 doi (DE-627)SPR049441582 (SPR)s40808-022-01558-5-e DE-627 ger DE-627 rakwb eng Leyla, Harbi verfasserin (orcid)0000-0002-4009-9952 aut Modeling and predictive analyses related to piezometric level in an earth dam using a back propagation neural network in comparison on non-linear regression 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor 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 The objective of the dam safety guidelines and rules is to provide design engineers with information on dam planning, design, construction, operation and maintenance activities, procedures and requirements. However, the hydraulic and mechanical properties may react differently than expected based on the design or in rare case dam fail. The purpose of the dam monitoring is to illustrate the dam response face different solicitations. Furthermore, the results of monitoring dam are used to determine the kind and timing of maintenance or repair procedures while also keeping tabs on how the structure's behavior evolves over time. This can make it possible to compare the performance of the structure to project forecasts. In this paper, a typical zoned dam with central core El-Izdihar dam, located in the northwest of Algeria was considered as an example. The non-linear regression models combined with the theory of Back Propagation Neural Network (BPNN) were adopted to ascertain the optimum model for the prediction of the piezometric levels in the dam. The performance of the model has been judged using the correlation coefficient R, and Root Mean Square Error (RMSE). The achieved result shows that the BPNN model has the ability to predict piezometric level, and make that more accurate and reasonable even in case of nonlinear analyses. El Izdihar dam (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Piezometry (dpeaa)DE-He213 Nadia, Smail aut Bouchrit, Rouissat aut Enthalten in Modeling earth systems and environment Berlin : Springer, 2015 9(2022), 1 vom: 10. Okt., Seite 1169-1180 (DE-627)825736587 (DE-600)2821317-8 2363-6211 nnns volume:9 year:2022 number:1 day:10 month:10 pages:1169-1180 https://dx.doi.org/10.1007/s40808-022-01558-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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 AR 9 2022 1 10 10 1169-1180 |
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10.1007/s40808-022-01558-5 doi (DE-627)SPR049441582 (SPR)s40808-022-01558-5-e DE-627 ger DE-627 rakwb eng Leyla, Harbi verfasserin (orcid)0000-0002-4009-9952 aut Modeling and predictive analyses related to piezometric level in an earth dam using a back propagation neural network in comparison on non-linear regression 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor 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 The objective of the dam safety guidelines and rules is to provide design engineers with information on dam planning, design, construction, operation and maintenance activities, procedures and requirements. However, the hydraulic and mechanical properties may react differently than expected based on the design or in rare case dam fail. The purpose of the dam monitoring is to illustrate the dam response face different solicitations. Furthermore, the results of monitoring dam are used to determine the kind and timing of maintenance or repair procedures while also keeping tabs on how the structure's behavior evolves over time. This can make it possible to compare the performance of the structure to project forecasts. In this paper, a typical zoned dam with central core El-Izdihar dam, located in the northwest of Algeria was considered as an example. The non-linear regression models combined with the theory of Back Propagation Neural Network (BPNN) were adopted to ascertain the optimum model for the prediction of the piezometric levels in the dam. The performance of the model has been judged using the correlation coefficient R, and Root Mean Square Error (RMSE). The achieved result shows that the BPNN model has the ability to predict piezometric level, and make that more accurate and reasonable even in case of nonlinear analyses. El Izdihar dam (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Piezometry (dpeaa)DE-He213 Nadia, Smail aut Bouchrit, Rouissat aut Enthalten in Modeling earth systems and environment Berlin : Springer, 2015 9(2022), 1 vom: 10. Okt., Seite 1169-1180 (DE-627)825736587 (DE-600)2821317-8 2363-6211 nnns volume:9 year:2022 number:1 day:10 month:10 pages:1169-1180 https://dx.doi.org/10.1007/s40808-022-01558-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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 AR 9 2022 1 10 10 1169-1180 |
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Modeling and predictive analyses related to piezometric level in an earth dam using a back propagation neural network in comparison on non-linear regression El Izdihar dam (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Piezometry (dpeaa)DE-He213 |
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Modeling and predictive analyses related to piezometric level in an earth dam using a back propagation neural network in comparison on non-linear regression |
| abstract |
Abstract The objective of the dam safety guidelines and rules is to provide design engineers with information on dam planning, design, construction, operation and maintenance activities, procedures and requirements. However, the hydraulic and mechanical properties may react differently than expected based on the design or in rare case dam fail. The purpose of the dam monitoring is to illustrate the dam response face different solicitations. Furthermore, the results of monitoring dam are used to determine the kind and timing of maintenance or repair procedures while also keeping tabs on how the structure's behavior evolves over time. This can make it possible to compare the performance of the structure to project forecasts. In this paper, a typical zoned dam with central core El-Izdihar dam, located in the northwest of Algeria was considered as an example. The non-linear regression models combined with the theory of Back Propagation Neural Network (BPNN) were adopted to ascertain the optimum model for the prediction of the piezometric levels in the dam. The performance of the model has been judged using the correlation coefficient R, and Root Mean Square Error (RMSE). The achieved result shows that the BPNN model has the ability to predict piezometric level, and make that more accurate and reasonable even in case of nonlinear analyses. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor 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 The objective of the dam safety guidelines and rules is to provide design engineers with information on dam planning, design, construction, operation and maintenance activities, procedures and requirements. However, the hydraulic and mechanical properties may react differently than expected based on the design or in rare case dam fail. The purpose of the dam monitoring is to illustrate the dam response face different solicitations. Furthermore, the results of monitoring dam are used to determine the kind and timing of maintenance or repair procedures while also keeping tabs on how the structure's behavior evolves over time. This can make it possible to compare the performance of the structure to project forecasts. In this paper, a typical zoned dam with central core El-Izdihar dam, located in the northwest of Algeria was considered as an example. The non-linear regression models combined with the theory of Back Propagation Neural Network (BPNN) were adopted to ascertain the optimum model for the prediction of the piezometric levels in the dam. The performance of the model has been judged using the correlation coefficient R, and Root Mean Square Error (RMSE). The achieved result shows that the BPNN model has the ability to predict piezometric level, and make that more accurate and reasonable even in case of nonlinear analyses. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor 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 The objective of the dam safety guidelines and rules is to provide design engineers with information on dam planning, design, construction, operation and maintenance activities, procedures and requirements. However, the hydraulic and mechanical properties may react differently than expected based on the design or in rare case dam fail. The purpose of the dam monitoring is to illustrate the dam response face different solicitations. Furthermore, the results of monitoring dam are used to determine the kind and timing of maintenance or repair procedures while also keeping tabs on how the structure's behavior evolves over time. This can make it possible to compare the performance of the structure to project forecasts. In this paper, a typical zoned dam with central core El-Izdihar dam, located in the northwest of Algeria was considered as an example. The non-linear regression models combined with the theory of Back Propagation Neural Network (BPNN) were adopted to ascertain the optimum model for the prediction of the piezometric levels in the dam. The performance of the model has been judged using the correlation coefficient R, and Root Mean Square Error (RMSE). The achieved result shows that the BPNN model has the ability to predict piezometric level, and make that more accurate and reasonable even in case of nonlinear analyses. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor 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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Modeling and predictive analyses related to piezometric level in an earth dam using a back propagation neural network in comparison on non-linear regression |
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https://dx.doi.org/10.1007/s40808-022-01558-5 |
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Nadia, Smail Bouchrit, Rouissat |
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Nadia, Smail Bouchrit, Rouissat |
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10.1007/s40808-022-01558-5 |
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2024-07-04T00:49:35.932Z |
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| score |
7.401101 |