高拱坝施工仿真参数EMD-P-ILSTM动态更新模型研究

施工仿真参数更新是确保建设期施工进度仿真准确性的关键。但现有的参数更新方法难以对参数局部非线性和波动性变化特征进行学习和提取，更新精度有待进一步提高。为此，本文利用深度学习模型能够深度挖掘参数序列隐含信息的优势，采用分解-预测-集成的建模新思路，提出高拱坝施工仿真参数EMD-P-ILSTM动态更新模型。该模型利用基于自适应步长改进的天牛须算法对长短期记忆网络模型的超参数进行自动寻优，以提高建模效率。采用经验模态分解法将参数序列分解为多个平稳的子序列，并利用偏自相关函数自动选取各子序列的时间窗口。工程实例表明，相比于未改进的LSTM、BPNN、SVM和贝叶斯更新方法，本文模型能有效跟踪施工参数的复杂变化，具有更高的预测精度。

EMD-P-ILSTM dynamic updating model for simulation parameters of high arch dam construction

For construction schedule simulation, parameters updating is the key to ensuring its accuracy. However, it is difficult for its existing methods to learn and extract the local nonlinear fluctuation characteristics of the parameters, and the updating accuracy needs further improvement. This paper takes advantage of the deep learning model's capability of mining more hidden information from parameter sequences and adopts a new modeling idea of decomposition-predict-integration. We construct a new dynamic updating model of construction simulation parameters using the techniques of empirical mode decomposition, and improved long short-term memory (EMD-P-ILSTM). To improve modeling efficiency, we use an improved Beetle Antennae algorithm based on an adaptive step factor to automatically optimize the hyperparameters of the LSTM network model. The EMD method is used to decompose a parameter sequence into several stationary sub-sequences; a partial autocorrelation function is used to select a time window for each sub-sequence automatically. A case study shows that, compared with the unimproved LSTM, BPNN, SVM, or Bayesian update method, our new model can track effectively the complicated changes in construction parameters and achieve a higher prediction accuracy.