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

Journal of Rock Mechanics and Geotechnical Engineering 2020年第1期12卷 21-31页

作者： Rohola Hasanpour Jamal Rostami Jürgen Schmitt Yilmaz Ozcelik babak sohrabian Institute for Tunneling and Construction Management Ruhr-University BochumBochumGermany Department of Mining Engineering Colorado School of MinesGoldenUSA Department of Civil Engineering Darmstadt University of Applied ScienceDarmstadtGermany Department of Mining Engineering Hacettepe UniversityAnkaraTurkey Faculty of Mining and Metallurgical Engineering Urmia University of TechnologyUrmiaIran

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 *** analysis is based on database of tunneling cases by numerical modeling to evaluate the ground convergence and possibility of machine *** 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 *** 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 *** the dataset,an ANN was trained to predict the contact pressures from a series of ground properties and machine ***,the continuous and discretized BNs were used to analyze the risk of shield *** results of these two different BN methods are compared to the field observations and summarized in this *** developed risk models can estimate the required thrust force in both *** BN models can also be used in the cases with incomplete geological and geomechanical properties.

关键词： Bayesian network(BN) Artificial neural network(ANN) Shielded tunnel boring machine(TBM) Jamming risk Numerical simulation Squeezing ground

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