Research on Machine Learning Prediction of Floating of Shield Tunnel with Quick Connectors
Due to the limited number of shield tunnels with quick connectors in China,the segment flotation law differs from that of traditional bolted tunnels.It is necessary to explore the reasons for segment flotation to facilitate construction control.Based on a quick connector tunnel project in Nanjing,the construction data are collected and sorted out.Various machine learning methods are employed to predict and fill missing values of segment floating.And the effectiveness of the model is evaluated by using the coefficient of determination(R2)and root mean square error(RMSE).The results indicate that in a quick connector tunnel in Nanjing,the pitch angle,total thrust force and shield tail gap(vertical)have a significant impact on the segment floating.The machine learning model can effectively predict the segment flotation and supplement missing floating values,which provides a basis for floating prediction in similar engineering constructions.
NETL
NSTL
万方数据
