Research on Surface Settlement Prediction Method of Shield Construction Based on Machine Learning
The network structure of the long-term and short-term memory(LSTM)model is optimized and improved.The feature is extracted by multi-source data such as geometric parameters,geological parameters and driving pa-rameters.An in-depth analysis is conducted on the surface settlement caused by tunnel shield construction.The prediction accuracy of BP neural network model and LSTM model was compared and analyzed.The analysis results show that the LSTM model has better prediction ability than the BP neural network model,and is more consistent with the actual engineering monitoring data;During the construction process,surface settlement & deformation can be pre-warned by the model prediction data.The surface deformation control can be realized by adjusting the shield tunneling parameters.The relevant research conclusions can provide reference for the prediction of surface settle-ment in similar shield construction.
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