Evaluation of Applicability of 1D-CNN and LSTM to Predict Horizontal Displacement of Retaining Wall According to Excavation Work

During excavation works in downtown, stability and safety considerations of such excavations and constructions are crucial for which continuous wall structures with varying structural components are commonly used. Most of the current models used for this purpose are often complex, where the accepted parameters do not have a clear physical meaning. Moreover, accurate ground movement forecasts are challenging due to nonlinear and inelastic soil behavior. Therefore, this study proposes a method to predict the lateral displacement of the braced wall at each stage of excavation by using all the basic information necessary for braced wall design, including ground information of the excavation site, support methods such as the type of brace, location and stiffness, information about the neighboring buildings, and the results of numerical analysis. One-dimensional convolutional neural network and long short-term memory network are used for estimation and prediction to develop an optimal prediction model based on well-refined but limited data. The applicability of the braced wall was confirmed for safety management by predicting the horizontal displacement of the braced wall for each stage of excavation. The proposed model can be used to predict the stability of the horizontal wall for each excavation step and reduce accident risks, such as collapse of the retaining wall, which may occur during construction.

@article{Seo2022,
  title     = {Evaluation of Applicability of 1D-CNN and LSTM to Predict Horizontal Displacement of Retaining Wall According to Excavation Work},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {2},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Seunghwan Seo and Moonkyung Chung},
  doi       = {10.14569/IJACSA.2022.0130210},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130210}
}