Fang Qing, Chen Sheng, Liu Xuezhu, et al. application of the variational mode decomposition-based CNN-LSTM model in predicting excavation deformation. Mechanics in Engineering, 2024, 46(5): 1015-1022. DOI: 10.6052/1000-0879-24-018
Citation: Fang Qing, Chen Sheng, Liu Xuezhu, et al. application of the variational mode decomposition-based CNN-LSTM model in predicting excavation deformation. Mechanics in Engineering, 2024, 46(5): 1015-1022. DOI: 10.6052/1000-0879-24-018

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.
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