Abstract
Stochastic medium (SM) theory is a practical method in ground settlement prediction, while its nonintegrable double integral form makes the solution process complicated. A simplified analytical solution based on the SM theory is developed to predict the ground movement in tunneling excavation. With the simplified solution, the ground movement for single tunnel and twin tunnels could be predicted based on the gap parameter G and influence angle β. A feasible approach is developed to estimate these two parameters using the maximum ground settlement Smax and tunnel design parameters, including tunnel depth H and diameter R. The proposed approach can be used to predict the ground movement curve for both circular and noncircular cross section tunnels. To validate its accuracy, the results predicted by the simplified procedure are compared with those obtained by the SM theory and measured in situ. The comparisons show that the current results agree well with those obtained by the SM theory and measured in situ. The comparison of five tunnels in literature illustrates that the simplified method can provide a more reasonable prediction for the ground movement induced by tunneling.
Highlights
In current urban areas, shallow-buried tunnel such as the subway has become much more popular as it can provide the largest traffic volume and the fastest transportation speed to solve the traffic congestion [1]
The ground movement for single tunnel and twin tunnels could be predicted based on the gap parameter G and influence angle
Comparing to the general approaches based on the stochastic medium theory, this study takes a simplified way to predict the tunneling-induced ground movement. e condition for this simplified approach application is the same as that for the SM method, which is for the shallow-buried tunnel condition
Summary
Shallow-buried tunnel such as the subway has become much more popular as it can provide the largest traffic volume and the fastest transportation speed to solve the traffic congestion [1]. The machine learning method provides new solutions for tunneling-induced ground settlement, including the artificial neural network [11,12,13], support vector machine [14,15,16], and random forest [17, 18]. Among these methods, the analytical solution method is widely used in practice to predict the ground surface. The ground movement for single tunnel and twin tunnels could be predicted based on the gap parameter G and influence angle. The predicted results using the simplified procedure are compared with the SM theory and in situ measured values
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