Optimized prediction model for concrete dam displacement based on signal residual amendment |
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| Affiliation: | 2. EDF DTG, 21 avenue de l’Europe, BP41, 38000 Grenoble, France;3. EDF DTG, 62 Bis rue Raymond IV, BP875, 31685 Toulouse Cedex 6, France;1. Department for Applied Mechanics and Automatic Control, Faculty of Engineering, University of Kragujevac, Sestre Janji? 6, 34000 Kragujevac, Serbia;2. Institute for Development of Water Resources “Jaroslav ?erni”, 80 Jaroslava ?ernog St., 11226 Beli Potok, Belgrade, Serbia;1. International Center for Numerical Methods in Engineering (CIMNE), Campus Norte UPC, Gran Capitán s/n, 08034 Barcelona, Spain;2. Technical University of Madrid (UPM), Civil Engineering Department: Hydraulics, Energy and Environment, Profesor Aranguren s/n, 28040 Madrid, Spain;1. Institute of Water Resources and Hydro-electric Engineering, Xi''an University of Technology, Xi''an 710048, China;2. Sinohydro Engineering Bureau 15 Co., Ltd., Xi''an 710016, China;1. State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;2. National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China;3. School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia;1. Univ. Grenoble Alpes, 3SR, F-38000 Grenoble, France;2. CNRS, 3SR, F-38000 Grenoble, France;3. EDF DTG, 21 avenue de l’Europe, BP41, 38000 Grenoble, France;4. EDF DTG, 62 Bis rue Raymond IV, BP875, 31685 Toulouse Cedex 6, France |
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| Abstract: | The traditional statistical model of concrete dam's displacement monitoring is used widely in hydraulic engineering. However, the forecasting precision of the conventional calculation model is poor due to the antiquated method of information mining and weak generalization capacity. Furthermore, the uncertain chaos effect implied in residual sequence is also intractable for modeling. In consideration of the nonlinearity, time variation, and unsteadiness of the chaotic characteristics of a dam time series, multiscale wavelet technology is used to decompose and reconstruct the residuals of multiple regression models. The fitting prediction of the low-frequency autocorrelation part is completed through the linear training ability of the autoregressive integrated moving average (ARIMA) model, and the support vector machine (SVM) regression model is constructed to optimize and process the nonlinear high-frequency signal. Then, a combined forecasting model for concrete dam's displacement based on signal residual amendment is established. The analysis of an engineering example indicates that the combined model built in this study can identify the time–frequency nonlinear characteristics of the prototype monitoring signal well, thus improving its fitting precision, antinoise ability, and robustness. In addition, the combined mathematical model established in this study is improved and developed for application to the prediction analysis of the effect quantities of other hydraulic structures. |
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