A high-performance displacement prediction model of concrete dams integrating signal processing and multiple machine learning techniques

Abstract

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.