基于双阶段注意力机制的大坝变形深度学习预测模型

为提升大坝结构变形预测精度，采用完全自适应噪声集合经验模态分解(CEEMDAN)法将变形实测序列解耦为一系列具有不同时频特征的本征模态函数，使用小波阈值消噪对高频分量平稳化处理后进行重构，利用基于双阶段注意力机制的长短期记忆网络(DA-LSTM)模型对重构变形序列进行预测。实例验证结果表明，联合CEEMDAN算法和小波阈值消噪方法能够有效识别并清洗实测数据中的异常值，提升了测值对大坝运行性态的表征能力，同时DA-LSTM模型可以充分挖掘大坝变形的滞后性和增强网络的可解释性，据此构建的变形预测模型具有优良的稳健性。

Deep learning model for deformation prediction of dam based on dual-stage attention mechanism

To improve the prediction accuracy on the deformation of dam structure, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm was adopted in this work to decouple the measured sequence into a series of intrinsic mode functions with different time-frequency characteristics, and then the wavelet threshold denoising was used to stabilize the high frequency component for the reconstruction. Afterwards, a dual-stage attention-based long short-term memory network (DA-LSTM) model was introduced to predict the reconstructed deformation sequence. The results show that the denoising processing method combining CEEMDAN algorithm and wavelet threshold denoising can effectively identify and deal with the outliers in the measured data to improve the representation capacity on the dam performance. Moreover, the established model can exploit the hysteresis of dam deformation and enhance the interpretability of the DA-LSTM network, indicating the strong robustness.