基于优化VMD与GRU的混凝土坝变形预测模型

为提高大坝变形预测精度，基于“分解-重构”思想，采用变形信号处理技术对实测变形加以时频分解，并结合深度学习网络对分解信号分项预测再重构，提出一种基于优化变分模态分解(VMD)与门控循环单元(GRU)的混凝土坝变形预测模型。该模型使用灰狼优化算法(GWO)优化的VMD把原始数据分解为一组最优本征模态分量(IMF),利用GWO优化的GRU网络对每个IMF分量进行滚动预测，通过叠加各个分量的预测结果得到位移序列预测结果，解决了VMD人工选择参数导致分解效果差及GRU人工选择参数影响训练速度、使用效果及鲁棒性等问题。工程实例预测结果表明，该模型的预测误差小，具有良好的预测精度与稳健性。

Deformation prediction model of concrete dams based on optimized VMD and GRU

In order to improve the accuracy of dam deformation prediction, a concrete dam deformation prediction model with optimized variational mode decomposition (VMD) and gated recurrent unit (GRU) was proposed based on the idea of decomposition-reconstruction, in which the deformation signal processing technology was used to perform time-frequency decomposition on the measured deformation and the deep learning networks was combined to predict and reconstruct the decomposed signals. Grey wolf optimization (GWO) optimized VMD was used to decompose the raw data into a set of optimal intrinsic mode components (IMF), and GWO optimized GRU network was used to perform rolling prediction on each IMF component. By overlaying the prediction results of each component, displacement sequence prediction results were obtained, solving the problems of poor decomposition effect caused by VMD manual parameter selection and the impact of GRU manual parameter selection on training speed, usage effect, and robustness. The prediction results of engineering examples show that the model has low prediction error and good prediction accuracy and robustness.