Neural network approaches for leakage flow quantification in masonry dam
Abstract Historically, one of the most common causes of dam failure has been overtopping, primarily in earthfill dam, accounting for approximately 34% in the United States, according to the Association of State Dam Safety Officials. There have been other causes which has also been contributed to dam...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bonet, E. [verfasserIn] Yubero, M. T. [verfasserIn] Sanmiquel, L. [verfasserIn] Bascompta, M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Nature Switzerland AG 2024. 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: Innovative infrastructure solutions - Springer International Publishing, 2016, 9(2024), 11 vom: 22. Okt. |
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| Übergeordnetes Werk: |
volume:9 ; year:2024 ; number:11 ; day:22 ; month:10 |
| Links: |
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| DOI / URN: |
10.1007/s41062-024-01744-7 |
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SPR057971404 |
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| 520 | |a Abstract Historically, one of the most common causes of dam failure has been overtopping, primarily in earthfill dam, accounting for approximately 34% in the United States, according to the Association of State Dam Safety Officials. There have been other causes which has also been contributed to dam failures throughout history, with a significant issue in masonry dams being water infiltration through the dam body, leading to erosion of the mortar that binds the rocks forming the dam body. As a result, quantifying the flow rate from these cracks in the mortar is an important parameter to monitor and control in dam maintenance and operation. In this article, a tool is developed using Neural Network methodologies for predicting water leakages in a masonry dam. The tool learns from historical data collected from the Santa Fe del Montseny Dam (Spain-Barcelona) over the past 12 years. The leakage flow prediction tool is developed in a MATLAB environment. The methodology used is an artificial neural network and different model options such as hold-out and k-folds were provided and tested. In this study, different layer sizes, different number of neurons, different k folds values are considered to minimize the leakage prediction error of the tool. The results indicate that the tool can predict infiltration flow with an accuracy close to 94%, making it a valuable tool for decision-making in the masonry dam maintenance and operation tasks. In that sense, the leakage flow prediction is also a useful tool for dam monitoring to evaluate the dam’s behavior. | ||
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| 700 | 1 | |a Sanmiquel, L. |e verfasserin |4 aut | |
| 700 | 1 | |a Bascompta, M. |e verfasserin |4 aut | |
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10.1007/s41062-024-01744-7 doi (DE-627)SPR057971404 (SPR)s41062-024-01744-7-e DE-627 ger DE-627 rakwb eng 000 VZ 550 620 VZ Bonet, E. verfasserin aut Neural network approaches for leakage flow quantification in masonry dam 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2024. 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 Historically, one of the most common causes of dam failure has been overtopping, primarily in earthfill dam, accounting for approximately 34% in the United States, according to the Association of State Dam Safety Officials. There have been other causes which has also been contributed to dam failures throughout history, with a significant issue in masonry dams being water infiltration through the dam body, leading to erosion of the mortar that binds the rocks forming the dam body. As a result, quantifying the flow rate from these cracks in the mortar is an important parameter to monitor and control in dam maintenance and operation. In this article, a tool is developed using Neural Network methodologies for predicting water leakages in a masonry dam. The tool learns from historical data collected from the Santa Fe del Montseny Dam (Spain-Barcelona) over the past 12 years. The leakage flow prediction tool is developed in a MATLAB environment. The methodology used is an artificial neural network and different model options such as hold-out and k-folds were provided and tested. In this study, different layer sizes, different number of neurons, different k folds values are considered to minimize the leakage prediction error of the tool. The results indicate that the tool can predict infiltration flow with an accuracy close to 94%, making it a valuable tool for decision-making in the masonry dam maintenance and operation tasks. In that sense, the leakage flow prediction is also a useful tool for dam monitoring to evaluate the dam’s behavior. Leakage flow (dpeaa)DE-He213 Dam (dpeaa)DE-He213 Water infrastructures (dpeaa)DE-He213 Dam safety (dpeaa)DE-He213 Mitigation actions (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Yubero, M. T. verfasserin aut Sanmiquel, L. verfasserin aut Bascompta, M. verfasserin aut Enthalten in Innovative infrastructure solutions Springer International Publishing, 2016 9(2024), 11 vom: 22. Okt. (DE-627)84438626X (DE-600)2843079-7 2364-4184 nnns volume:9 year:2024 number:11 day:22 month:10 https://dx.doi.org/10.1007/s41062-024-01744-7 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 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_266 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_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_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 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_4598 GBV_ILN_4700 AR 9 2024 11 22 10 |
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10.1007/s41062-024-01744-7 doi (DE-627)SPR057971404 (SPR)s41062-024-01744-7-e DE-627 ger DE-627 rakwb eng 000 VZ 550 620 VZ Bonet, E. verfasserin aut Neural network approaches for leakage flow quantification in masonry dam 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2024. 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 Historically, one of the most common causes of dam failure has been overtopping, primarily in earthfill dam, accounting for approximately 34% in the United States, according to the Association of State Dam Safety Officials. There have been other causes which has also been contributed to dam failures throughout history, with a significant issue in masonry dams being water infiltration through the dam body, leading to erosion of the mortar that binds the rocks forming the dam body. As a result, quantifying the flow rate from these cracks in the mortar is an important parameter to monitor and control in dam maintenance and operation. In this article, a tool is developed using Neural Network methodologies for predicting water leakages in a masonry dam. The tool learns from historical data collected from the Santa Fe del Montseny Dam (Spain-Barcelona) over the past 12 years. The leakage flow prediction tool is developed in a MATLAB environment. The methodology used is an artificial neural network and different model options such as hold-out and k-folds were provided and tested. In this study, different layer sizes, different number of neurons, different k folds values are considered to minimize the leakage prediction error of the tool. The results indicate that the tool can predict infiltration flow with an accuracy close to 94%, making it a valuable tool for decision-making in the masonry dam maintenance and operation tasks. In that sense, the leakage flow prediction is also a useful tool for dam monitoring to evaluate the dam’s behavior. Leakage flow (dpeaa)DE-He213 Dam (dpeaa)DE-He213 Water infrastructures (dpeaa)DE-He213 Dam safety (dpeaa)DE-He213 Mitigation actions (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Yubero, M. T. verfasserin aut Sanmiquel, L. verfasserin aut Bascompta, M. verfasserin aut Enthalten in Innovative infrastructure solutions Springer International Publishing, 2016 9(2024), 11 vom: 22. Okt. (DE-627)84438626X (DE-600)2843079-7 2364-4184 nnns volume:9 year:2024 number:11 day:22 month:10 https://dx.doi.org/10.1007/s41062-024-01744-7 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 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_266 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_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_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 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_4598 GBV_ILN_4700 AR 9 2024 11 22 10 |
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10.1007/s41062-024-01744-7 doi (DE-627)SPR057971404 (SPR)s41062-024-01744-7-e DE-627 ger DE-627 rakwb eng 000 VZ 550 620 VZ Bonet, E. verfasserin aut Neural network approaches for leakage flow quantification in masonry dam 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2024. 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 Historically, one of the most common causes of dam failure has been overtopping, primarily in earthfill dam, accounting for approximately 34% in the United States, according to the Association of State Dam Safety Officials. There have been other causes which has also been contributed to dam failures throughout history, with a significant issue in masonry dams being water infiltration through the dam body, leading to erosion of the mortar that binds the rocks forming the dam body. As a result, quantifying the flow rate from these cracks in the mortar is an important parameter to monitor and control in dam maintenance and operation. In this article, a tool is developed using Neural Network methodologies for predicting water leakages in a masonry dam. The tool learns from historical data collected from the Santa Fe del Montseny Dam (Spain-Barcelona) over the past 12 years. The leakage flow prediction tool is developed in a MATLAB environment. The methodology used is an artificial neural network and different model options such as hold-out and k-folds were provided and tested. In this study, different layer sizes, different number of neurons, different k folds values are considered to minimize the leakage prediction error of the tool. The results indicate that the tool can predict infiltration flow with an accuracy close to 94%, making it a valuable tool for decision-making in the masonry dam maintenance and operation tasks. In that sense, the leakage flow prediction is also a useful tool for dam monitoring to evaluate the dam’s behavior. Leakage flow (dpeaa)DE-He213 Dam (dpeaa)DE-He213 Water infrastructures (dpeaa)DE-He213 Dam safety (dpeaa)DE-He213 Mitigation actions (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Yubero, M. T. verfasserin aut Sanmiquel, L. verfasserin aut Bascompta, M. verfasserin aut Enthalten in Innovative infrastructure solutions Springer International Publishing, 2016 9(2024), 11 vom: 22. Okt. (DE-627)84438626X (DE-600)2843079-7 2364-4184 nnns volume:9 year:2024 number:11 day:22 month:10 https://dx.doi.org/10.1007/s41062-024-01744-7 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 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_266 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_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_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 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_4598 GBV_ILN_4700 AR 9 2024 11 22 10 |
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10.1007/s41062-024-01744-7 doi (DE-627)SPR057971404 (SPR)s41062-024-01744-7-e DE-627 ger DE-627 rakwb eng 000 VZ 550 620 VZ Bonet, E. verfasserin aut Neural network approaches for leakage flow quantification in masonry dam 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2024. 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 Historically, one of the most common causes of dam failure has been overtopping, primarily in earthfill dam, accounting for approximately 34% in the United States, according to the Association of State Dam Safety Officials. There have been other causes which has also been contributed to dam failures throughout history, with a significant issue in masonry dams being water infiltration through the dam body, leading to erosion of the mortar that binds the rocks forming the dam body. As a result, quantifying the flow rate from these cracks in the mortar is an important parameter to monitor and control in dam maintenance and operation. In this article, a tool is developed using Neural Network methodologies for predicting water leakages in a masonry dam. The tool learns from historical data collected from the Santa Fe del Montseny Dam (Spain-Barcelona) over the past 12 years. The leakage flow prediction tool is developed in a MATLAB environment. The methodology used is an artificial neural network and different model options such as hold-out and k-folds were provided and tested. In this study, different layer sizes, different number of neurons, different k folds values are considered to minimize the leakage prediction error of the tool. The results indicate that the tool can predict infiltration flow with an accuracy close to 94%, making it a valuable tool for decision-making in the masonry dam maintenance and operation tasks. In that sense, the leakage flow prediction is also a useful tool for dam monitoring to evaluate the dam’s behavior. Leakage flow (dpeaa)DE-He213 Dam (dpeaa)DE-He213 Water infrastructures (dpeaa)DE-He213 Dam safety (dpeaa)DE-He213 Mitigation actions (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Yubero, M. T. verfasserin aut Sanmiquel, L. verfasserin aut Bascompta, M. verfasserin aut Enthalten in Innovative infrastructure solutions Springer International Publishing, 2016 9(2024), 11 vom: 22. Okt. (DE-627)84438626X (DE-600)2843079-7 2364-4184 nnns volume:9 year:2024 number:11 day:22 month:10 https://dx.doi.org/10.1007/s41062-024-01744-7 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 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_266 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_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_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 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_4598 GBV_ILN_4700 AR 9 2024 11 22 10 |
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10.1007/s41062-024-01744-7 doi (DE-627)SPR057971404 (SPR)s41062-024-01744-7-e DE-627 ger DE-627 rakwb eng 000 VZ 550 620 VZ Bonet, E. verfasserin aut Neural network approaches for leakage flow quantification in masonry dam 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2024. 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 Historically, one of the most common causes of dam failure has been overtopping, primarily in earthfill dam, accounting for approximately 34% in the United States, according to the Association of State Dam Safety Officials. There have been other causes which has also been contributed to dam failures throughout history, with a significant issue in masonry dams being water infiltration through the dam body, leading to erosion of the mortar that binds the rocks forming the dam body. As a result, quantifying the flow rate from these cracks in the mortar is an important parameter to monitor and control in dam maintenance and operation. In this article, a tool is developed using Neural Network methodologies for predicting water leakages in a masonry dam. The tool learns from historical data collected from the Santa Fe del Montseny Dam (Spain-Barcelona) over the past 12 years. The leakage flow prediction tool is developed in a MATLAB environment. The methodology used is an artificial neural network and different model options such as hold-out and k-folds were provided and tested. In this study, different layer sizes, different number of neurons, different k folds values are considered to minimize the leakage prediction error of the tool. The results indicate that the tool can predict infiltration flow with an accuracy close to 94%, making it a valuable tool for decision-making in the masonry dam maintenance and operation tasks. In that sense, the leakage flow prediction is also a useful tool for dam monitoring to evaluate the dam’s behavior. Leakage flow (dpeaa)DE-He213 Dam (dpeaa)DE-He213 Water infrastructures (dpeaa)DE-He213 Dam safety (dpeaa)DE-He213 Mitigation actions (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Yubero, M. T. verfasserin aut Sanmiquel, L. verfasserin aut Bascompta, M. verfasserin aut Enthalten in Innovative infrastructure solutions Springer International Publishing, 2016 9(2024), 11 vom: 22. Okt. (DE-627)84438626X (DE-600)2843079-7 2364-4184 nnns volume:9 year:2024 number:11 day:22 month:10 https://dx.doi.org/10.1007/s41062-024-01744-7 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 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_266 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_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_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 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_4598 GBV_ILN_4700 AR 9 2024 11 22 10 |
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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 Historically, one of the most common causes of dam failure has been overtopping, primarily in earthfill dam, accounting for approximately 34% in the United States, according to the Association of State Dam Safety Officials. There have been other causes which has also been contributed to dam failures throughout history, with a significant issue in masonry dams being water infiltration through the dam body, leading to erosion of the mortar that binds the rocks forming the dam body. As a result, quantifying the flow rate from these cracks in the mortar is an important parameter to monitor and control in dam maintenance and operation. In this article, a tool is developed using Neural Network methodologies for predicting water leakages in a masonry dam. The tool learns from historical data collected from the Santa Fe del Montseny Dam (Spain-Barcelona) over the past 12 years. The leakage flow prediction tool is developed in a MATLAB environment. The methodology used is an artificial neural network and different model options such as hold-out and k-folds were provided and tested. In this study, different layer sizes, different number of neurons, different k folds values are considered to minimize the leakage prediction error of the tool. The results indicate that the tool can predict infiltration flow with an accuracy close to 94%, making it a valuable tool for decision-making in the masonry dam maintenance and operation tasks. In that sense, the leakage flow prediction is also a useful tool for dam monitoring to evaluate the dam’s behavior.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Leakage flow</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dam</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Water infrastructures</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dam safety</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mitigation actions</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural networks</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yubero, M. 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Okt.</subfield><subfield code="w">(DE-627)84438626X</subfield><subfield code="w">(DE-600)2843079-7</subfield><subfield code="x">2364-4184</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:9</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:11</subfield><subfield code="g">day:22</subfield><subfield code="g">month:10</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s41062-024-01744-7</subfield><subfield code="m">X:SPRINGER</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_0</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield 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neural network approaches for leakage flow quantification in masonry dam |
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Neural network approaches for leakage flow quantification in masonry dam |
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Abstract Historically, one of the most common causes of dam failure has been overtopping, primarily in earthfill dam, accounting for approximately 34% in the United States, according to the Association of State Dam Safety Officials. There have been other causes which has also been contributed to dam failures throughout history, with a significant issue in masonry dams being water infiltration through the dam body, leading to erosion of the mortar that binds the rocks forming the dam body. As a result, quantifying the flow rate from these cracks in the mortar is an important parameter to monitor and control in dam maintenance and operation. In this article, a tool is developed using Neural Network methodologies for predicting water leakages in a masonry dam. The tool learns from historical data collected from the Santa Fe del Montseny Dam (Spain-Barcelona) over the past 12 years. The leakage flow prediction tool is developed in a MATLAB environment. The methodology used is an artificial neural network and different model options such as hold-out and k-folds were provided and tested. In this study, different layer sizes, different number of neurons, different k folds values are considered to minimize the leakage prediction error of the tool. The results indicate that the tool can predict infiltration flow with an accuracy close to 94%, making it a valuable tool for decision-making in the masonry dam maintenance and operation tasks. In that sense, the leakage flow prediction is also a useful tool for dam monitoring to evaluate the dam’s behavior. © Springer Nature Switzerland AG 2024. 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 Historically, one of the most common causes of dam failure has been overtopping, primarily in earthfill dam, accounting for approximately 34% in the United States, according to the Association of State Dam Safety Officials. There have been other causes which has also been contributed to dam failures throughout history, with a significant issue in masonry dams being water infiltration through the dam body, leading to erosion of the mortar that binds the rocks forming the dam body. As a result, quantifying the flow rate from these cracks in the mortar is an important parameter to monitor and control in dam maintenance and operation. In this article, a tool is developed using Neural Network methodologies for predicting water leakages in a masonry dam. The tool learns from historical data collected from the Santa Fe del Montseny Dam (Spain-Barcelona) over the past 12 years. The leakage flow prediction tool is developed in a MATLAB environment. The methodology used is an artificial neural network and different model options such as hold-out and k-folds were provided and tested. In this study, different layer sizes, different number of neurons, different k folds values are considered to minimize the leakage prediction error of the tool. The results indicate that the tool can predict infiltration flow with an accuracy close to 94%, making it a valuable tool for decision-making in the masonry dam maintenance and operation tasks. In that sense, the leakage flow prediction is also a useful tool for dam monitoring to evaluate the dam’s behavior. © Springer Nature Switzerland AG 2024. 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 Historically, one of the most common causes of dam failure has been overtopping, primarily in earthfill dam, accounting for approximately 34% in the United States, according to the Association of State Dam Safety Officials. There have been other causes which has also been contributed to dam failures throughout history, with a significant issue in masonry dams being water infiltration through the dam body, leading to erosion of the mortar that binds the rocks forming the dam body. As a result, quantifying the flow rate from these cracks in the mortar is an important parameter to monitor and control in dam maintenance and operation. In this article, a tool is developed using Neural Network methodologies for predicting water leakages in a masonry dam. The tool learns from historical data collected from the Santa Fe del Montseny Dam (Spain-Barcelona) over the past 12 years. The leakage flow prediction tool is developed in a MATLAB environment. The methodology used is an artificial neural network and different model options such as hold-out and k-folds were provided and tested. In this study, different layer sizes, different number of neurons, different k folds values are considered to minimize the leakage prediction error of the tool. The results indicate that the tool can predict infiltration flow with an accuracy close to 94%, making it a valuable tool for decision-making in the masonry dam maintenance and operation tasks. In that sense, the leakage flow prediction is also a useful tool for dam monitoring to evaluate the dam’s behavior. © Springer Nature Switzerland AG 2024. 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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Neural network approaches for leakage flow quantification in masonry dam |
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| score |
7.401636 |