APPLICATION OF THE VARIATIONAL MODE DECOMPOSITION-BASED CNN-LSTM MODEL IN PREDICTING EXCAVATION DEFORMATION

Excavation deformation can have numerous adverse effects on construction projects, potentially trigger catastrophic incidents such as soil collapse and cracking of nearby roads or buildings. Therefore, predicting excavation deformation is a crucial aspect of excavation engineering. To enhance the accuracy of these predictions, we propose a variational mode decomposition convolutional neural network-long short-term memory (VMD-CNN-LSTM) prediction model, which takes time series of monitoring data as input. Using onsite monitoring data from Nanjing Jiangbei new district library, the VMD-CNN-LSTM model was applied to forecast the deep horizontal displacement of the continuous underground wall at monitoring point CX07. A comparative analysis of the deformation predictions obtained from the LSTM and CNN-LSTM models demonstrates that the VMD-CNN-LSTM model offers superior accuracy. Further validation of the model's predictive performance was conducted using monitoring data from two other points, confirming the applicability and stability of the VMD-CNN-LSTM model.