Grouting flow hybrid prediction model based on CEEMDAN-Transformer

被引：0
|
作者：
Li K. [1 ]
Ren B. [1 ]
Wang J. [1 ]
Guan T. [1 ]
Yu J. [1 ]
机构：
[1] State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin
来源：
Shuili Xuebao/Journal of Hydraulic Engineering | 2023年 / 54卷 / 07期
关键词：
attention algorithm; complete ensemble empirical mode decomposition with adaptive noise; grouting flow prediction; sequence to sequence; Transformer algorithm;
D O I：
10.13243/j.cnki.slxb.20220628
中图分类号：
学科分类号：
摘要：
Grouting flow is one of the most important grouting parameters of hydraulie engineering. The abnormal construction condition can be found by the effective grouting flow prediction to guarantee the construction quality and safety. However, the geological condition is complex and grouting flow data has the features of strong nonlinearity and volatility, therefore the prediction precision is unsatisfied. The shortcomings of the existing grouting flow prediction are as follows: the traditional neural network model is insufficient in feature extraction, resulting in unsatisfied prediction precision; the traditional neural network model calculates one result by one calculation, multiple time step prediction requires complex multiple calculations; the prediction time of one point is short and the prediction result can not reflect the total trend of grouting flow sequence, therefore it is not beneficial to control grouting flow and guarantee construction quality. For those problems, this research proposes the grouting flow hybrid prediction model based on CEEMDAN-Transformer. The grouting flow is decomposed to eigenmode function and residual signal based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the problems of strong nonlinearity and volatility are settled. The sequence prediction of Intrinsic Mode Function (IMF) is realized using multi-head attention Transformer, and the total dependency between input data and output data is established using multi-head attention method. This method is effective in extracting dynamic temporal features and improving the extracting quality. Finally, the grouting flow prediction model with multi—input and multi-output is established to improve the calculation efficiency, providing the reference for grouting flow control. The proposed CEEMDAN-Transformer model has better calculation accuracy and efficiency in grouting flow prediction. © 2023 China Water Power Press. All rights reserved.
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页码：806 / 817
页数：11
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