Abstract: During construction, the shield linings of tunnels often face the problem of local or overall upward movement after leaving the shield tail in soft soil areas or during some large diameter shield projects. Differential floating will increase the initial stress on the segments and bolts which is harmful to the service performance of the tunnel. In this study we used a random forest (RF) algorithm combined particle swarm optimization (PSO) and 5-fold cross-validation (5-fold CV) to predict the maximum upward displacement of tunnel linings induced by shield tunnel excavation. The mechanism and factors causing upward movement of the tunnel lining are comprehensively summarized. Twelve input variables were selected according to results from analysis of influencing factors. The prediction performance of two models, PSO-RF and RF (default) were compared. The Gini value was obtained to represent the relative importance of the influencing factors to the upward displacement of linings. The PSO-RF model successfully predicted the maximum upward displacement of the tunnel linings with a low error (mean absolute error (MAE)=4.04 mm, root mean square error (RMSE)=5.67 mm) and high correlation (R²=0.915). The thrust and depth of the tunnel were the most important factors in the prediction model influencing the upward displacement of the tunnel linings.

Key words: Random forest (RF); Particle swarm optimization (PSO); Upward displacement of lining; Machine learning prediction; Shield tunneling construction

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