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Application of soil parameter inversion method based on BP neural network in foundation pit deformation prediction

  • Engineering geophysics
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Abstract

When significant deformation occurs in a foundation pit, it is critical to have an accurate method for predicting this deformation. This is necessary for enacting timely safety measures. Unfortunately, finite element simulations, which are strongly aff ected by soil parameters, fail to reflect the dynamic deformation of foundation pits during excavation. To address this, we used the actual soil parameters of a deep foundation pit to design 64 representative combinations of soil parameters through orthogonal testing. Using a three-dimensional (3D) finite element model of the foundation pit, we obtained displacement values for each parameter combination. These included the maximum horizontal displacement of the support structure and the surface settlement value. Subsequently, we developed a backpropagation (BP) neural network model. We trained this model using the soil parameters of each combination as input and the deformation values obtained from the 3D finite element model as output. Once the model was trained, we inverted the soil parameters, reflecting the dynamic deformation of the foundation pit by using actual monitoring data. This process allowed us to obtain the deformation data for the next excavation stage. Results showed that the soil parameters obtained via the BP neural network model eff ectively reflected the stress state of the deep foundation pit. Moreover, the prediction of the foundation pit deformation aligned well with the monitoring data, which validates the accuracy and feasibility of our method.

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Acknowledgments

We would like to express our sincere thanks to the school and the enterprise for providing us with support and great help for the completion of this paper. Ma Haohao would like to thank the members of the research team for their help in writing this paper. Thanks also go to the editors and reviewers for comments. We appreciate the financial support from the following foundations:

1. The National Natural Science Foundation of China (grant number 52104157).

2. Natural Science Foundation of Henan Province (grant number: 222300420596).

3. NSFC-Henan Province Talent Training Joint Fund (grant number: U1204509).

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Authors and Affiliations

  1. Department of Architecture and Engineering, Zhongyuan University of Technology, Zhengzhou, 450007, China

    Hao-Hao Ma & Zhi-zheng Zhang

  2. Department of Geotechnical,Highway School, Chang’an University, Xi’An, 710064, China

    Shuai Yuan

  3. China Railway Construction Engineering Fifth Construction Co., Ltd, Guangzhou, 510000, China

    Ya-hui Tian

  4. Any Science & Technologies (Changzhou) Inc, Changzhou, 213000, China

    Sen-Sen Dong

Authors
  1. Hao-Hao Ma
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  2. Shuai Yuan
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  3. Zhi-zheng Zhang
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  4. Ya-hui Tian
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  5. Sen-Sen Dong
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Correspondence to Hao-Hao Ma.

Additional information

Ma Haohao is a lecturer with a Ph.D. degree. His research interests include subgrade settlement, geotechnical engineering monitoring, etc.

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Ma, HH., Yuan, S., Zhang, Zz. et al. Application of soil parameter inversion method based on BP neural network in foundation pit deformation prediction. Appl. Geophys. 20, 299–309 (2023). https://doi.org/10.1007/s11770-023-1029-8

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  • DOI: https://doi.org/10.1007/s11770-023-1029-8

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