Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction
Both numerical simulations and data-driven methods have been applied in dam’s displacement modeling. For monitored displacement data-driven methods, the physical mechanism and structural correlations were rarely discussed. In order to take the spatial and temporal correlations among all monitoring p...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/4961963 |
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| author | Chenfei Shao Chongshi Gu Zhenzhu Meng Yating Hu |
| author_facet | Chenfei Shao Chongshi Gu Zhenzhu Meng Yating Hu |
| author_sort | Chenfei Shao |
| collection | DOAJ |
| description | Both numerical simulations and data-driven methods have been applied in dam’s displacement modeling. For monitored displacement data-driven methods, the physical mechanism and structural correlations were rarely discussed. In order to take the spatial and temporal correlations among all monitoring points into account, we took the first step toward integrating the finite element method into a data-driven model. As the data-driven method, we selected the random coefficient model, which can make each explanatory variable coefficient of all monitoring points following one or several normal distributions. In this way, explanatory variables are constrained. Another contribution of the proposed model is that the actual elastic modulus at each monitoring point can be back-calculated. Moreover, with a Lagrange polynomial interpolation, we can obtain the distribution field of elastic modulus, rather than gaining one value for the whole dam in previous studies. The proposed model was validated by a case study of the concrete arch dam in Jinping-I hydropower station. It has a better prediction precision than the random coefficient model without the finite element method. |
| format | Article |
| id | doaj-art-80df3d9d128a4d8fa3fc18cb1ddf49e1 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-80df3d9d128a4d8fa3fc18cb1ddf49e12025-02-03T01:21:34ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/49619634961963Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement PredictionChenfei Shao0Chongshi Gu1Zhenzhu Meng2Yating Hu3College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, ChinaCollege of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, ChinaLaboratory of Environmental Hydraulics, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, SwitzerlandCollege of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, ChinaBoth numerical simulations and data-driven methods have been applied in dam’s displacement modeling. For monitored displacement data-driven methods, the physical mechanism and structural correlations were rarely discussed. In order to take the spatial and temporal correlations among all monitoring points into account, we took the first step toward integrating the finite element method into a data-driven model. As the data-driven method, we selected the random coefficient model, which can make each explanatory variable coefficient of all monitoring points following one or several normal distributions. In this way, explanatory variables are constrained. Another contribution of the proposed model is that the actual elastic modulus at each monitoring point can be back-calculated. Moreover, with a Lagrange polynomial interpolation, we can obtain the distribution field of elastic modulus, rather than gaining one value for the whole dam in previous studies. The proposed model was validated by a case study of the concrete arch dam in Jinping-I hydropower station. It has a better prediction precision than the random coefficient model without the finite element method.http://dx.doi.org/10.1155/2020/4961963 |
| spellingShingle | Chenfei Shao Chongshi Gu Zhenzhu Meng Yating Hu Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction Advances in Civil Engineering |
| title | Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction |
| title_full | Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction |
| title_fullStr | Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction |
| title_full_unstemmed | Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction |
| title_short | Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction |
| title_sort | integrating the finite element method with a data driven approach for dam displacement prediction |
| url | http://dx.doi.org/10.1155/2020/4961963 |
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