Research on deformation prediction of deep foundation pit excavation based on GWO-ELM model

Sanqiang Yang; Zhenyu Yang; Leifeng Zhang; Yapeng Guo; Ju Wang; Jingyong Huang; Sanqiang Yang; Zhenyu Yang; Leifeng Zhang; Yapeng Guo; Ju Wang; Jingyong Huang

doi:10.3934/era.2023288

Electronic Research Archive

2023, Volume 31, Issue 9: 5685-5700. doi: 10.3934/era.2023288

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Research on deformation prediction of deep foundation pit excavation based on GWO-ELM model

1.
College of Civil Engineering and Architecture, Hebei University, Baoding, China
2.
Beijing Municipal Road and Bridge Co., LTD, Beijing, China
3.
Huitong Construction Group Co., LTD, Baoding, China

Academic Editor: Siyu Chen

Received: 30 June 2023 Revised: 01 August 2023 Accepted: 08 August 2023 Published: 16 August 2023

Given the complex nonlinear problem between the control and prediction of the surrounding surface settlement deformation caused (GWO), the GWO-ELM deep foundation pit excavation deformation prediction model was proposed. Extreme learning machine and Grey Wolf optimization algorithm combining training and predicting land subsidence. Based on MIDAS GTS NX software, we established a finite element simplified model for deep foundation pit construction, conducted structural calculations, and utilized the Grey Wolf optimization algorithm to optimize the deep foundation pit excavation and its influencing factors, input weights, and hidden layer thresholds in the ELM neural network. Taking the deep foundation pit project of Baoding Automobile Science and Technology Industrial Park as an example, the actual monitoring value is compared with the simulated value, verifying the model's accuracy. The number of soil nails in the finite element model, the excavation depth, the settlement of surrounding buildings and other factors are taken as the input factors of the prediction model. The DB-2 surface settlement of the monitoring point in the finite element model is taken as the output factor of the prediction model. The predicted value of the GWO-ELM model was compared with that of the ELM model. We draw three main conclusions from the results. First, the surface settlement of a bottomless foundation pit can be predicted in advance by using finite element software and the distribution law of surface settlement and horizontal displacement is consistent with the measured values. Second, the Grey Wolf optimization algorithm optimizes the input weights and thresholds in the extreme learning machine neural network. The GWO-ELM prediction model has good generalization ability, can effectively reduce human errors and can improve the accuracy of the prediction model. Third, through practical engineering verification, the average absolute error of the GWO-ELM model is 0.26145, the mean square error is 0.31258 and the R² is 0.98725, all of which are superior to the ELM model and are an effective method for predicting deformation and settlement of deep foundation pit excavation.
- deep foundation pit,
- neural network,
- Grey Wolf optimization algorithm,
- numerical simulation,
- extreme learning machine,
- prediction of settleme
Citation: Sanqiang Yang, Zhenyu Yang, Leifeng Zhang, Yapeng Guo, Ju Wang, Jingyong Huang. Research on deformation prediction of deep foundation pit excavation based on GWO-ELM model[J]. Electronic Research Archive, 2023, 31(9): 5685-5700. doi: 10.3934/era.2023288

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Abstract

Given the complex nonlinear problem between the control and prediction of the surrounding surface settlement deformation caused (GWO), the GWO-ELM deep foundation pit excavation deformation prediction model was proposed. Extreme learning machine and Grey Wolf optimization algorithm combining training and predicting land subsidence. Based on MIDAS GTS NX software, we established a finite element simplified model for deep foundation pit construction, conducted structural calculations, and utilized the Grey Wolf optimization algorithm to optimize the deep foundation pit excavation and its influencing factors, input weights, and hidden layer thresholds in the ELM neural network. Taking the deep foundation pit project of Baoding Automobile Science and Technology Industrial Park as an example, the actual monitoring value is compared with the simulated value, verifying the model's accuracy. The number of soil nails in the finite element model, the excavation depth, the settlement of surrounding buildings and other factors are taken as the input factors of the prediction model. The DB-2 surface settlement of the monitoring point in the finite element model is taken as the output factor of the prediction model. The predicted value of the GWO-ELM model was compared with that of the ELM model. We draw three main conclusions from the results. First, the surface settlement of a bottomless foundation pit can be predicted in advance by using finite element software and the distribution law of surface settlement and horizontal displacement is consistent with the measured values. Second, the Grey Wolf optimization algorithm optimizes the input weights and thresholds in the extreme learning machine neural network. The GWO-ELM prediction model has good generalization ability, can effectively reduce human errors and can improve the accuracy of the prediction model. Third, through practical engineering verification, the average absolute error of the GWO-ELM model is 0.26145, the mean square error is 0.31258 and the R² is 0.98725, all of which are superior to the ELM model and are an effective method for predicting deformation and settlement of deep foundation pit excavation.

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