Prediction of Surface Settlement in Underground Tunnels Based on PLAXIS-BP Neural Network
To study the impact of multiple factors on surface settlement during the construction process of shallow buried and underground excavated tunnels,and accurately evaluate construction risks.Based on the measured da-ta from centrifugal model experiments,a prediction model for surface settlement during tunnel excavation in satu-rated clay layers is established by combining the finite element software PLAXIS3D with the BP neural network learning algorithm.By adjusting the number of hidden layers and nodes in the BP neural network,the optimal neural network structure is obtained,and secondary validation is conducted by increasing the validation set and sensitivity analysis.Then,feature importance analysis is performed to quantify the impact of each factor on the maximum surface subsidence.The research results indicate that by adjusting the hyperparameters of the BP neu-ral network,the error of the constructed model is less than 5%,which meets the engineering accuracy require-ments,and the predicted settlement trend is in line with the actual engineering situation.
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