Abstract: Shield tail segment uplift is a common phenomenon encountered in largediameter shield tunneling through rivers. As a result, a case study is conducted on a shield tunnel crossing the Yellow river, and a segment uplift calculation framework based on XGboost algorithm is proposed for largediameter tunnel bored by a slurry shield. The dimension of formation parameters is reduced by principal component analysis, and RreliefF algorithm is used to extract the features and preprocess the factors affecting segment uplift, so as to establish a data set for segment uplift analysis. Furthermore, the XGBoost algorithm is used to calculate the uplift of largediameter tunnel segments, and the results are compared with those of random forest algorithm. The results show that the calculation framework used in this study can better reflect the uplift characteristics of tunnel segments during construction, and the XGBoost algorithm has a better prediction effect than random forest for the uplift process of tunnel segments. The research results have a good guiding significance for the prediction and control of segment deformation in the construction process of largediameter tunnel.

Key words: largediameter tunnel, shield construction, segment uplift, machine learning, XGBoost algorithm, forecast analysis