Search

Summary: The traditional statistical models for concrete dam deformation do not consider the effective contribution to residual sequence and thus results in the low accuracy of deformation prediction of the monitoring model and in the insufficient prediction of dam deformation behavior. This study proposes a combined model for concrete dam deformation, considering residual correction by frequency division. The horizontal displacement of concrete dams is driven by water pressure, temperature, and aging factors. In this study, an accurate value of water pressure deformation is obtained by finite element calculation, and then the hybrid model is constructed. However, the temperature and aging components hardly obtain accurate information values from the traditional model. Therefore, a combined model is constructed to extract additional detailed information that characterizes the dam deformation from the residual sequence. Ensemble empirical mode decomposition is selected to analyze and denoise the residual sequence adaptively, and the consecutive mean square error is used to determine the boundary points of high‐ and low‐frequency components. Then, the combined model based on the extreme learning machine method optimized by particle swarm optimization and seasonal autoregressive integrated moving average is constructed for high‐ and low‐frequency sequences. Lastly, the prediction results of different subitems are organically integrated. Results of a case study in engineering demonstrate that the accuracy of the combined model for concrete dam deformation is higher than that of the classical numerical model, and the model can effectively overcome noise interference in the monitoring sequence. The proposed combined model for deformation is reliable for analyzing and judging the dam monitoring time series, which can provide new ideas and reference for the data processing of dam deformation monitoring. [ABSTRACT FROM AUTHOR]