Considering the strong nonstationarity and nonlinearity of the measured displacement, a displacement prediction model of concrete dams is proposed using variational mode decomposition (VMD), twin support vector regression (TSVR) and gated recurrent unit (GRU). Firstly, VMD is adopted to decompose the measured displacement into several signals according to the time series features, and sample entropy (SE) method is used to avoid over-decomposition. Secondly, K-means clustering algorithm is utilized to categorize the decomposed signals into trend, periodic, semi-periodic and stochastic signals, and the trend, periodic and semi-periodic signals are utilized to reconstruct the displacement. Thirdly, the reconstructed displacement is predicted with TSVR-based hydrostatic-seasonal-time (HST) model, and the stochastic signals are predicted with GRU network-based model. Considering the influence of parameters on modelling performance, seagull optimization algorithm (SOA) is employed to determine the optimal parameters of TSVR and GRU. Finally, a displacement prediction model is established by superimposing the predicted values of TSVR-based and GRU-based models. The measured displacement of a gravity dam is utilized to elaborate the implementation process and to test the validity of the proposed model. Results show that the model has excellent predictive performance, which demonstrates that the model is feasible and powerful for precisely predicting dam displacement.

考虑到实测位移具有很强的非平稳性和非线性，提出了采用变分模态分解（VMD）、孪生支持向量回归（TSVR）和门控循环单元（GRU）的混凝土大坝位移预测模型。首先，采用VMD将实测位移按照时间序列特征分解为若干个信号，并采用样本熵（SE）方法避免过度分解。其次，K利用-means聚类算法将分解后的信号分为趋势、周期、半周期和随机信号，利用趋势、周期和半周期信号重构位移。第三，重建位移采用基于TSVR的静水季节时间（HST）模型进行预测，随机信号采用基于GRU网络的模型进行预测。考虑参数对建模性能的影响，采用海鸥优化算法（SOA）确定TSVR和GRU的最优参数。最后，将基于TSVR和基于GRU的模型的预测值叠加，建立位移预测模型。重力坝的实测位移用于详细说明实施过程并测试所提出模型的有效性。