Journal of Shandong University(Engineering Science) ›› 2025, Vol. 55 ›› Issue (3): 141-148.doi: 10.6040/j.issn.1672-3961.0.2024.118

• Civil Engineering • Previous Articles    

Deformation prediction method and engineering application of deep foundation pit based on optimized LSTM method

ZHU Ming1, SHI Chenglong1, LÜ Pan1, LIU Xianrong1, SUN Chi1, CHEN Jiancheng1, FAN Hongyun2*   

  1. 1. China Construction Science and Industry Co., Ltd., Shenzhen 518054, Guangdong, China;
    2. School of Qilu Transportation, Shandong University, Jinan 250002, Shandong, China
  • Published:2025-06-05

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Abstract: To more accurately predict the deformation of support structures induced by excavation of foundation pits. The research constructed a deformation prediction model for deep foundation pits adjacent to tunnels. The grey wolf optimizer(GWO)algorithm was used to automatically optimize the hyperparameters in the long-short term memory network(LSTM), which improved the accuracy of the original LSTM model's prediction results. Taking a deep excavation project adjacent to a tunnel in the urban area of Nanjing as the engineering background, a comparative analysis was conducted on the prediction results of excavation deformation using BP neural network, original LSTM and GWO-LSTM models. The R2 of the three models were 0.992, 0.967, and 0.999, respectively, indicating the advantages and accuracy of the GWO-LSTM model in predicting deep excavation deformation. Finally, the GWO-LSTM model was used to predict and analyze the deformation of D14 monitoring point and the predicted results were basically consistent with the measured values. The research results could provide technical support for the safe construction of deep foundation pits adjacent to tunnels.

Key words: deep foundation pit, deformation prediction, machine learning, neural network, optimization algorithm

CLC Number: 

  • TU46+3
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