Grouting Power Prediction Method Based on CEEMDAN-CNN-BiLSTM

Grouting power serves as a critical parameter reflecting real-time energy input during grouting operations, and its accurate prediction is essential for intelligent control and engineering safety. Existing prediction methods often struggle to handle the strong nonlinearity, noise interference, adaptability to varying conditions in grouting power data. To address these challenges, an intelligent grouting system that integrates real-time data collection and core control modules has been developed. Subsequently, a grouting power prediction model is then proposed, which combines Complete Ensemble Empirical Mode Decomposition and Adaptive Noise (CEEMDAN) with a Convolutional Neural Net-work-Bidirectional Long Short-Term Memory Neural Network (CNN-BiLSTM) is proposed. The approach employs CEEMDAN to decompose the nonlinear and non-stationary power sequence into multiple intrinsic mode functions (IMFs). Each IMF is then separated into linear and nonlinear components using a moving average method. The linear components are predicted using an Autoregressive Integrated Moving Average (ARIMA) model, while the nonlinear components are predicted using a CNN-BiLSTM model. The final prediction is obtained by reconstructing the results from both components. Experimental comparisons under both normal and heaving grouting conditions demonstrate that the proposed model significantly outperforms LSTM, CNN-LSTM, and CNN-BiLSTM models. With 80% of the dataset used for training, the RMSE for normal conditions is reduced by 95.69%, 85.11%, and 80.55%, respectively, and for heaving conditions by 94.91%, 90.71%, and 84.62%, respectively. This research provides high-precision predictive support for grouting regulation under complex working conditions, offering substantial engineering application value.