Based on the hybrid improved Genetic Algorithm-Error Backpropagation Multilayer Feedforward Neural Network (GA-BP Neural Network) algorithm, stress component-strain development law under the stress path of different medium to principal stress ratios (0, 0.25, 0.5, 0.75, and 1) and angles of deflection of the principal stress axis (0°, 15°, 30°, 45°, 60°, 75°, and 90°). By comparing the predicted data with the measured data, the practicality of the data regression prediction model in shear property prediction has been verified through multiple rounds. Moreover, this network is utilized to inversely analyze the development regularities of torsional shear stress-strain under different working conditions. The influence of training set data on network recognition accuracy is discussed, demonstrating that the improved GA-BP neural network generally achieves higher prediction accuracy compared to the original BP neural network and conventional GA-BP neural network. Finally, the feasibility and accuracy of the model are validated through two numerical examples.
Zhang Dongyu
,
Wang Sui
,
Cao Xuenan
. Hollow Cylinder Test Prediction Study Based on Improved GA-BP Neural Network[J]. Chinese Journal of Underground Space and Engineering, 2025
, 21(S2)
: 750
-760
.
DOI: 10.20174/j.JUSE.2025.S2.26
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