基于因子融合的混凝土面板堆石坝变形预测模型

Deformation prediction model of concrete faced rockfill dams based on factor fusion

混凝土面板堆石坝变形测值具有高度的非线性和复杂性,变形影响因素众多且因素间存在多重共线性。针对此类坝型的变形预测分析问题,本文提出一种基于因子融合的混凝土面板堆石坝变形预测模型。首先,利用变分模态分解对变形时间序列进行分解,有效降低变形时间序列的复杂程度,提升特征提取效果。随后,借助偏最小二乘回归对变形影响因子进行降维融合,降低自变量间多重共线性对构建模型的影响,提高模型可解释性。最后,通过一维卷积网络融合门控循环单元神经网络对子序列进行重构预测。根据实际工程分析结果,本模型可以在效率和精度上有效提升混凝土面板堆石坝变形预测效果,对类似坝型的变形监测分析具有一定的参考意义。

The measured deformation of a concrete faced rockfill dam is highly nonlinear and complicated,owing to a variety of influential factors and the collinearity among them.To improve the deformation prediction in dam analysis,this paper develops a deformation prediction model of concrete faced rockfill dams based on the factor fusion.First,we use the variational mode decomposition to decompose a deformation time series so as to effectively reduce its complexity and enhances feature extraction.Next,we employ the partial least square regression to reduce and fuse the influential factors of deformation,reducing the impact of multicollinearity between independent variables on model construction and enhancing model interpretability.Finally,we reconstruct and predict the subsequences using a one-dimensional convolutional network fused with a gated recurrent unit neural network.Analyses of certain real projects show our model greatly improves the efficiency and accuracy of deformation prediction for concrete faced rockfill dams,and is also useful for deformation monitoring and analysis of similar dams.