Gaussian process regression-based forecasting model of dam deformation
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| Publicado en: | Neural Computing & Applications vol. 31, no. 12 (Dec 2019), p. 8503 |
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| Autor principal: | |
| Otros Autores: | , , , , |
| Publicado: |
Springer Nature B.V.
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 2268837021 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0941-0643 | ||
| 022 | |a 1433-3058 | ||
| 024 | 7 | |a 10.1007/s00521-019-04375-7 |2 doi | |
| 035 | |a 2268837021 | ||
| 045 | 2 | |b d20191201 |b d20191231 | |
| 100 | 1 | |a Lin, Chaoning |u College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, Jiangsu, China | |
| 245 | 1 | |a Gaussian process regression-based forecasting model of dam deformation | |
| 260 | |b Springer Nature B.V. |c Dec 2019 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The displacement at various measurement points is a critical indicator that can intuitively reflect the operational properties of a dam. It is important to analyse displacement monitoring data in a timely manner and make reliable predictions of dam safety. This paper proposes a GPR-based model for dam displacement forecasting. The input variables of the monitoring model consider hydraulic factors, thermal factors and irreversible factors, and the output variables are the observed displacements of the dam. An example analysis based on the proposed method is performed on a prototype gravity dam, and the performance of different simple/combined covariance functions is investigated to obtain the optimal choice. Compared to multiple linear regression, radial basis function network (RBFN) and support vector machine (SVM) methods, the results indicate that the GPR-based model with a combined covariance function significantly improves the prediction accuracy. The proposed model can effectively overcome the over-learning and poor robustness issues of approaches such as RBFN and SVM. In addition, the GPR-based forecasting model has the advantages of simplicity in the training process and the capacity to provide a probabilistic output. | |
| 653 | |a Dams | ||
| 653 | |a Model accuracy | ||
| 653 | |a Regression analysis | ||
| 653 | |a Radial basis function | ||
| 653 | |a Support vector machines | ||
| 653 | |a Regression models | ||
| 653 | |a Covariance | ||
| 653 | |a Gravity dams | ||
| 653 | |a Displacement | ||
| 653 | |a Gaussian process | ||
| 653 | |a Dam safety | ||
| 653 | |a Monitoring | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Forecasting | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Li, Tongchun |u College of Agricultural Engineering, Hohai University, Nanjing, Jiangsu, China; National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing, Jiangsu, China | |
| 700 | 1 | |a Chen, Siyu |u College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, Jiangsu, China; College of Agricultural Engineering, Hohai University, Nanjing, Jiangsu, China | |
| 700 | 1 | |a Liu, Xiaoqing |u College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, Jiangsu, China | |
| 700 | 1 | |a Lin, Chuan |u College of Civil Engineering, Fuzhou University, Fuzhou, Fujian, China | |
| 700 | 1 | |a Liang, Siling |u College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, Jiangsu, China | |
| 773 | 0 | |t Neural Computing & Applications |g vol. 31, no. 12 (Dec 2019), p. 8503 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2268837021/abstract/embedded/XH47U3ESDU1O47K5?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2268837021/fulltextPDF/embedded/XH47U3ESDU1O47K5?source=fedsrch |