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Simulation of 3D Effect of Excavation Face Advancement Using an ANN Trained by Numerical Models
Assoc. Prof., Faculty of Civil and Environmental Eng, Amirkabir University, Tehran, Iran Roozbeh Foroozan PhD Student, International Institute of Earthquake Engineering and Seismology (IIEES) Tehran, Iran r.foroozan@iiees.ac.ir Pooyan Asadollahi M. Sc. Mahab Construction Engineers, Tehran, Iran | |
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ABSTRACT |
By the use of convergence-confinement method, the three-dimensional problem of tunnel excavation is simulated by an equivalent two-dimensional plane strain analysis. In this method, the evaluation of the convergence occurred before the time support starts interacting with ground is the critical point. The aim of this paper is to assess this convergence for deep tunnels excavated in elastoplastic continuum and anisotropic stress conditions with the aid of an artificial neural network (ANN) approach. Numerical 3D FE models supply data sets required for the training process of the network. About 170 circular tunnels between 100 and 1000 meters deep, excavated in fair to good rock masses (according to RMR classification) are analyzed. The trained network will be capable to evaluate the convergence values for different distances to the excavation face with regard to rock specifications and stress conditions and is used for a sensitivity analysis of the parameters involved.
Keywords: Confinement-Convergence method, Tunnel excavation face, Confinement loss, Artificial Neural Networks.
