Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis
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| Publicado en: | Mathematical Problems in Engineering vol. 2018 (2018) |
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| Otros Autores: | , , , |
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John Wiley & Sons, Inc.
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| 024 | 7 | |a 10.1155/2018/1712653 |2 doi | |
| 035 | |a 2101282448 | ||
| 045 | 2 | |b d20180101 |b d20181231 | |
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| 100 | 1 | |a Chen, Siyu |u College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China; National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China | |
| 245 | 1 | |a Safety Monitoring Model of a Super-High Concrete Dam by Using RBF Neural Network Coupled with Kernel Principal Component Analysis | |
| 260 | |b John Wiley & Sons, Inc. |c 2018 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Effective deformation monitoring is vital for the structural safety of super-high concrete dams. The radial displacement of the dam body is an important index of dam deformation, which is mainly influenced by reservoir water level, temperature effect, and time effect. In general, the safety monitoring models of dams are built on the basis of statistical models. The temperature effect of dam safety monitoring models is interpreted using approximate functions or the temperature values of a few points of measurement. However, this technique confers difficulty in representing the nonlinear features of the temperature effect on super-high concrete dams. In this study, a safety monitoring model of super-high concrete dams is established through the radial basis neural network (RBF-NN) and kernel principal component analysis (KPCA). The RBF-NN with strong nonlinear fitting capacity is utilized as the framework of the model, and KPCA with different kernels is adopted to extract the temperature variables of the dam temperature dataset. The model is applied to a super-high arch dam in China, and results show that the Hybrid-KPCA -RBF-NN model has high fitting and prediction precision and thus has practical application value. | |
| 653 | |a Dams | ||
| 653 | |a Structural safety | ||
| 653 | |a Concrete | ||
| 653 | |a Deformation effects | ||
| 653 | |a Concrete dams | ||
| 653 | |a Dam safety | ||
| 653 | |a Deformation analysis | ||
| 653 | |a Monitoring | ||
| 653 | |a Mechanics | ||
| 653 | |a Monitoring systems | ||
| 653 | |a Mathematical problems | ||
| 653 | |a Water levels | ||
| 653 | |a Arch dams | ||
| 653 | |a Multivariate analysis | ||
| 653 | |a Principal components analysis | ||
| 653 | |a Neural networks | ||
| 653 | |a Temperature effects | ||
| 653 | |a Variables | ||
| 653 | |a Engineering | ||
| 653 | |a Algorithms | ||
| 653 | |a Statistical models | ||
| 653 | |a Economic models | ||
| 700 | 1 | |a Gu, Chongshi |u College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China; National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China | |
| 700 | 1 | |a Lin, Chaoning |u College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China | |
| 700 | 1 | |a Zhao, Erfeng |u College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China; National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China | |
| 700 | 1 | |a Song, Jintao |u College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China; National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China | |
| 773 | 0 | |t Mathematical Problems in Engineering |g vol. 2018 (2018) | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2101282448/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/2101282448/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2101282448/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |