Analysis of the interaction between bolt-reinforced rock and surface support in tunnels based on convergence-confinement method

To investigate the interaction of the bolt-reinforced rock and the surface support,an analytical model of the convergence-confinement type is proposed,considering the sequential installation of the fully grouted rockbolts and the surface support.The rock mass is assumed to be elastic-brittle-plastic material,obeying the linear Mohr-Coulomb criterion or the non-linear Hoek-Brown criterion.According to the strain states of the tunnel wall at bolt and surface support installation and the relative magnitude between the bolt length and the plastic depth during the whole process,six cases are categorized upon solving the problem.Each case is divided into three stages due to the different effects of the active rockbolts and the passive surface support.The fictitious pressure is introduced to quantify the threedimensional(3D)effect of the tunnel face,and thus,the actual physical location along the tunnel axis of the analytical section can be considered.By using the bolt-rock strain compatibility and the rocksurface support displacement compatibility conditions,the solutions of longitudinal tunnel displacement and the reaction pressure of surface support along the tunnel axis are obtained.The proposed analytical solutions are validated by a series of 3D numerical simulations.Extensive parametric studies are conducted to examine the effect of the typical parameters of rockbolts and surface support on the tunnel displacement and the reaction pressure of the surface support under different rock conditions.The results show that the rockbolts are more effective in controlling the tunnel displacement than the surface support,which should be installed as soon as possible with a suitable length.For tunnels excavated in weak rocks or with restricted displacement control requirements,the surface support should also be installed or closed timely with a certain stiffness.The proposed method provides a convenient alternative approach for the optimization of rockbolts and surface support at the preliminary stage of tunnel design.

Influence of adjacent shield tunneling construction on existing tunnel settlement: field monitoring and intelligent prediction

Urban subway tunnel construction inevitably disturbs the surrounding rock and causes the deformation of existing subway structures. Dynamic predictions of the tunnel horizontal displacement, tunnel ballast settlement, and tunnel differential settlement are important for ensuring the safety of buildings and tunnels. First, based on the Hangzhou Metro project, we analyzed the influence of construction on the deformation of existing subway structures and the difficulties and key points in monitoring. Then, a deformation prediction model, based on a back propagation(BP) neural network, was established with massive monitoring data. In particular, we analyzed the influence of four structures of the BP neural network on prediction performance, i.e., single input–single hidden layer–single output, multiple inputs–single hidden layer–single output, single input–double hidden layers–single output, and multiple inputs–double hidden layers–single output, and verified them using measured data.

Multi-objective optimization-based prediction of excavation-induced tunnel displacement

This paper proposes an inverse method for improving the prediction of tunnel displacements during adjacent excavation.In this framework,staged data assimilation and parameter identification are conducted using the multi-objective particle swarm optimization algorithm.Recent monitoring data are assumed to be more informative and assigned more weights in the multi-objective optimization to improve the prediction accuracy.Then,an empirical formula is applied to correct the time effect of tunnel displacement.The Kriging method is introduced to surrogate the finite element model to reduce computational cost.The proposed framework is verified using a typical staged“excavation nearing tunnel”case.The predictions using the updated parameters are in good agreement with the measurements.The identified values of underlying soil parameters are within the typical ranges for the unloading condition.The updated time effect indicates that tunnel displacements may develop excessively in the three months after the region S1-B is excavated to the bottom.The maximum vertical tunnel displacement may increase from the currently measured 12 mm to the calculated 26 mm if the later construction is suspended long enough.Subsequent constructions need to be timely conducted to restrain the time effect and control tunnel displacements.

A practical ANN model for predicting the excavation-induced tunnel horizontal displacement in soft soils

The objective of this study is to propose an artificial neural network(ANN)model to predict the excavation-induced tunnel horizontal displacement in soft soils.For this purpose,a series of finite element data sets from rigorously verified numerical models were collected to be utilized for the development of the ANN model.The excavation width,the excavation depth,the retaining wall thickness,the ratio of the average shear strength to the vertical effective stress,the ratio of the average unloading/reloading Young’s modulus to the vertical effective stress,the horizontal distance between the tunnel and retaining wall,and the ratio of the buried depth of the tunnel crown to the excavation depth were chosen as the input variables,while the excavation-induced tunnel horizontal displacement was considered as an output variable.The results demonstrated the feasibility of the developed ANN model to predict the excavation-induced tunnel horizontal displacement.The proposed ANN model in this study can be applied to predict the excavation-induced tunnel horizontal displacement in soft soils for practical risk assessment and mitigation decision.