Utilizing machine learning to predict the uplift of shield tunnel linings ahead of the cutterhead during construction enables timely adjustments of control parameters

mitigating lining uplift issues. Nevertheless

existing models exhibit limited performance in long sequence time-series forecasting (LSTF) and face challenges in accurately predicting the uplift of multiple lining rings ahead of the shield cutterhead. Considering the impact of shield control

attitude parameters

and geological condition

and utilizing the Boruta algorithm to determine model input features

a shield tunnel segment uplift prediction model based on LightGBM-Informer was proposed. This model incorporates a wavelet transform filter and a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to eliminate noise in time series data. The accuracy and applicability of the proposed model were validated using the monitoring data from subway shield tunnel projects in Nanjing and Xiamen. The results demonstrate that the model exhibits enhanced prediction accuracy in comparison to other models

including recurrent neural network (RNN)

long short-term memory (LSTM)

gated recurrent unit (GRU)

and Transformer. Additionally

it demonstrates robust generalization capabilities across diverse geological condition datasets. As the length of the prediction sequence increases

the performance advantages of the model become more pronounced

accurately predicting the uplift of 1−2 rings of linings ahead of the shield cutterhead. Feature importance analysis based on Shapley additive explanations (SHAP) method indicates that earth chamber pressure and vertical displacement at the shield head and tail have significant impacts on lining uplift. The model provides theoretical guidance for intelligent control of shield tunnel lining construction in complex

water-rich environments.