Shield tunneling can cause surface deformation, which can lead to property damage and casualties when the surface deformation is severe. Conducting research on surface deformation caused by shield tunneling can provide early awareness of shield tunnel construction risks and guarantee the safety of shield tunnel construction. This article uses the artificial jellyfish algorithm to optimize the standard BP neural network algorithm, establishes an AJSO-BP neural network prediction model, and compares the AJSO-BP, GA-BP, and BP neural network prediction models to select the most accurate prediction model for practical engineering, and verifies the engineering applicability of the prediction model. The research results indicate that the standard BP neural network model has the lowest prediction accuracy, with a relative error of 43.6% between predicted and measured values. The AJSO-BP neural network model has the highest prediction accuracy, with a relative error of 6% between predicted and measured values. The artificial jellyfish search algorithm can significantly optimize the standard BP neural network. The engineering case analysis shows that the predicted values of the AJSO-BP neural network model are relatively close to the measured values, proving that the AJSO-BP neural network model has a good effect in practical engineering applications.
Xiong Wen
,
Li Hongyang
,
Fu Helin
,
Cao Guiqian
,
Yu Nenggen
. Prediction of Surface Deformation Caused by Shield Tunneling Based on AJSO-BP[J]. Chinese Journal of Underground Space and Engineering, 2024
, 20(S2)
: 949
-955
.
DOI: 10.20174/j.JUSE.2024.S2.51
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