Abstract: The prediction of surface settlement induced by shield tunneling has many problems such as insufficient data samples and relatively rough data preprocessing. As a result, a case study is conducted on Beijing metro shield tunneling projects, and the surface settlement induced by shield tunneling of 32 cases are selected. Then, the synthetic minority oversampling technology is used to enlarge the database, and four machine learning models, namely back propagation, random forest, support vector machine, and knearest neighbor (KNN), are employed to predict the surface settlement. The comparative analysis shows the following: (1) The prediction ability of the preprocessed database is significantly enhanced, and KNN shows a smallest average absolute error of the test set, which is 1.60 mm. (2) The bestperforming model is applied to XibaheSanyuanqiao shield tunneling section of Beijing metro line 12, and the predicted results agree well with the monitored results. (3) The KNN model based on the data enhancement has a good surface settlement prediction effect.

Key words: shield tunnel, surface settlement, machine learning algorithm, data enhancement, surface settlement prediction