Prediction of the intermediate block displacement of the dam crest using artificial neural network and support vector regression models
Abstract Concrete arch dams are three-dimensional structures which are statically indeterminate due to integrity and arching performance. Hence, the spatial and temporal temperature gradients in concrete arch dams affect the volume of the structures and generated internal stresses threaten stability...
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| Autor*in: |
Tabari, Mahmoud Mohammad Rezapour [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 23(2018), 19 vom: 12. Sept., Seite 9629-9645 |
|---|---|
| Übergeordnetes Werk: |
volume:23 ; year:2018 ; number:19 ; day:12 ; month:09 ; pages:9629-9645 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00500-018-3528-8 |
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OLC203490026X |
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| 520 | |a Abstract Concrete arch dams are three-dimensional structures which are statically indeterminate due to integrity and arching performance. Hence, the spatial and temporal temperature gradients in concrete arch dams affect the volume of the structures and generated internal stresses threaten stability of the structures. Accordingly, estimation of long-term thermal behavior of these structures for proper serviceability with considering dam crest displacement is necessary, and this issue requires the application of appropriate prediction models. The goal of this study is to implement the support vector regression (SVR) and artificial neural network (ANN) models for prediction of the intermediate block displacement of the dam crest. For this purpose, displacement of dam crest is investigated with ABAQUS simulation model over a period of 8 years, and then, the results of the simulation are used in the soft models (SVR and ANN) as the input data. The analysis of the results of two models with five error indicators shows that the error reduction in the SVR model is about 32% less than the ANN model in the testing stage. Also, investigation of the normal cumulative probability distribution related to the outputs of two models indicates high degree of deviation on cumulative probability distribution of the ANN model. This is due to the fact that the ANN model ignores fundamental errors in the training process. Therefore, based on the SVR model one can predict the dam stability in an acceptable accuracy range, only by measuring two different parameters including reservoir water level and the air temperature. | ||
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10.1007/s00500-018-3528-8 doi (DE-627)OLC203490026X (DE-He213)s00500-018-3528-8-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Tabari, Mahmoud Mohammad Rezapour verfasserin aut Prediction of the intermediate block displacement of the dam crest using artificial neural network and support vector regression models 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Concrete arch dams are three-dimensional structures which are statically indeterminate due to integrity and arching performance. Hence, the spatial and temporal temperature gradients in concrete arch dams affect the volume of the structures and generated internal stresses threaten stability of the structures. Accordingly, estimation of long-term thermal behavior of these structures for proper serviceability with considering dam crest displacement is necessary, and this issue requires the application of appropriate prediction models. The goal of this study is to implement the support vector regression (SVR) and artificial neural network (ANN) models for prediction of the intermediate block displacement of the dam crest. For this purpose, displacement of dam crest is investigated with ABAQUS simulation model over a period of 8 years, and then, the results of the simulation are used in the soft models (SVR and ANN) as the input data. The analysis of the results of two models with five error indicators shows that the error reduction in the SVR model is about 32% less than the ANN model in the testing stage. Also, investigation of the normal cumulative probability distribution related to the outputs of two models indicates high degree of deviation on cumulative probability distribution of the ANN model. This is due to the fact that the ANN model ignores fundamental errors in the training process. Therefore, based on the SVR model one can predict the dam stability in an acceptable accuracy range, only by measuring two different parameters including reservoir water level and the air temperature. Concrete arch dams Predicting displacement behavior Dam crest Support vector regression Artificial neural network Sanayei, Hamed Reza Zarif aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 23(2018), 19 vom: 12. Sept., Seite 9629-9645 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:23 year:2018 number:19 day:12 month:09 pages:9629-9645 https://doi.org/10.1007/s00500-018-3528-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 23 2018 19 12 09 9629-9645 |
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Tabari, Mahmoud Mohammad Rezapour Sanayei, Hamed Reza Zarif |
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prediction of the intermediate block displacement of the dam crest using artificial neural network and support vector regression models |
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Prediction of the intermediate block displacement of the dam crest using artificial neural network and support vector regression models |
| abstract |
Abstract Concrete arch dams are three-dimensional structures which are statically indeterminate due to integrity and arching performance. Hence, the spatial and temporal temperature gradients in concrete arch dams affect the volume of the structures and generated internal stresses threaten stability of the structures. Accordingly, estimation of long-term thermal behavior of these structures for proper serviceability with considering dam crest displacement is necessary, and this issue requires the application of appropriate prediction models. The goal of this study is to implement the support vector regression (SVR) and artificial neural network (ANN) models for prediction of the intermediate block displacement of the dam crest. For this purpose, displacement of dam crest is investigated with ABAQUS simulation model over a period of 8 years, and then, the results of the simulation are used in the soft models (SVR and ANN) as the input data. The analysis of the results of two models with five error indicators shows that the error reduction in the SVR model is about 32% less than the ANN model in the testing stage. Also, investigation of the normal cumulative probability distribution related to the outputs of two models indicates high degree of deviation on cumulative probability distribution of the ANN model. This is due to the fact that the ANN model ignores fundamental errors in the training process. Therefore, based on the SVR model one can predict the dam stability in an acceptable accuracy range, only by measuring two different parameters including reservoir water level and the air temperature. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
| abstractGer |
Abstract Concrete arch dams are three-dimensional structures which are statically indeterminate due to integrity and arching performance. Hence, the spatial and temporal temperature gradients in concrete arch dams affect the volume of the structures and generated internal stresses threaten stability of the structures. Accordingly, estimation of long-term thermal behavior of these structures for proper serviceability with considering dam crest displacement is necessary, and this issue requires the application of appropriate prediction models. The goal of this study is to implement the support vector regression (SVR) and artificial neural network (ANN) models for prediction of the intermediate block displacement of the dam crest. For this purpose, displacement of dam crest is investigated with ABAQUS simulation model over a period of 8 years, and then, the results of the simulation are used in the soft models (SVR and ANN) as the input data. The analysis of the results of two models with five error indicators shows that the error reduction in the SVR model is about 32% less than the ANN model in the testing stage. Also, investigation of the normal cumulative probability distribution related to the outputs of two models indicates high degree of deviation on cumulative probability distribution of the ANN model. This is due to the fact that the ANN model ignores fundamental errors in the training process. Therefore, based on the SVR model one can predict the dam stability in an acceptable accuracy range, only by measuring two different parameters including reservoir water level and the air temperature. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Concrete arch dams are three-dimensional structures which are statically indeterminate due to integrity and arching performance. Hence, the spatial and temporal temperature gradients in concrete arch dams affect the volume of the structures and generated internal stresses threaten stability of the structures. Accordingly, estimation of long-term thermal behavior of these structures for proper serviceability with considering dam crest displacement is necessary, and this issue requires the application of appropriate prediction models. The goal of this study is to implement the support vector regression (SVR) and artificial neural network (ANN) models for prediction of the intermediate block displacement of the dam crest. For this purpose, displacement of dam crest is investigated with ABAQUS simulation model over a period of 8 years, and then, the results of the simulation are used in the soft models (SVR and ANN) as the input data. The analysis of the results of two models with five error indicators shows that the error reduction in the SVR model is about 32% less than the ANN model in the testing stage. Also, investigation of the normal cumulative probability distribution related to the outputs of two models indicates high degree of deviation on cumulative probability distribution of the ANN model. This is due to the fact that the ANN model ignores fundamental errors in the training process. Therefore, based on the SVR model one can predict the dam stability in an acceptable accuracy range, only by measuring two different parameters including reservoir water level and the air temperature. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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Prediction of the intermediate block displacement of the dam crest using artificial neural network and support vector regression models |
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Sanayei, Hamed Reza Zarif |
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