Prediction of TBM jamming risk in squeezing grounds using Bayesian and artificial neural networks

Abstract This study presents an application of artificial neural network (ANN) and Bayesian network (BN) for evaluation of jamming risk of the shielded tunnel boring machines (TBMs) in adverse ground conditions such as squeezing grounds. The analysis is based on database of tunneling cases by numerical modeling to evaluate the ground convergence and possibility of machine entrapment. The results of initial numerical analysis were verified in comparison with some case studies. A dataset was established by performing additional numerical modeling of various scenarios based on variation of the most critical parameters affecting shield jamming. This includes compressive strength and deformation modulus of rock mass, tunnel radius, shield length, shield thickness, in situ stresses, depth of over-excavation, and skin friction between shield and rock. Using the dataset, an ANN was trained to predict the contact pressures from a series of ground properties and machine parameters. Furthermore, the continuous and discretized BNs were used to analyze the risk of shield jamming. The results of these two different BN methods are compared to the field observations and summarized in this paper. The developed risk models can estimate the required thrust force in both cases. The BN models can also be used in the cases with incomplete geological and geomechanical properties.