Abstract:

The settlement prediction precision of shield tunneling in complex ground should be improved. Hence, a settlement prediction model based on deep learning method is proposed by taking the shield tunneling project of Xiecun StationZhongcun Station Section on Guangzhou Metro Line No. 7 for example.Firstly, the distribution law of fractured zone on tunneling face is analyzed, and the characteristics of the fractured zone is described by the ratio of fractured zone. And then the correlation between tunneling parameters and area ratios of fractured zone is analyzed by correlation coefficient matrix, and the ground distribution characteristics are described by cutterhead torque. Finally, the deep learning model is well trained by taking the cutterhead torque, shield tail gap and grouting amount as input values and the ground settlement as output value, and the settlement is predicted by the trained model. The prediction effectiveness of the model is verified by comparing the prediction results with the actual settlement values.

Key words: deep learning model, fractured zone, shield tunnel, settlement prediction