Intelligent back analysis of geotechnical parameters for time-dependent rock mass surrounding mine openings using grey Verhulst model

Abstract

In this paper, we present a new method of intelligent back analysis (IBA) using grey Verhulst model (GVM) to identify geotechnical parameters of rock mass surrounding tunnel, and validate it via a test for a main openings of −600 m level in Coal Mine “6.13”, Democratic People’s Republic of Korea. The displacement components used for back analysis are the crown settlement and sidewalls convergence monitored at the end of the openings excavation, and the final closures predicted by GVM. The non-linear relation between displacements and back analysis parameters was obtained by artificial neural network (ANN) and Burger-creep viscoplastic (CVISC) model of FLAC^3D. Then, the optimal parameters were determined for rock mass surrounding tunnel by genetic algorithm (GA) with both groups of measured displacements at the end of the final excavation and closures predicted by GVM. The maximum absolute error (MAE) and standard deviation (Std) between calculated displacements by numerical simulation with back analysis parameters and in situ ones were less than 6 and 2 mm, respectively. Therefore, it was found that the proposed method could be successfully applied to determining design parameters and stability for tunnels and underground cavities, as well as mine openings and stopes.

Keywords

intelligent back analysis (IBA) / grey Verhulst model (GVM) / closure prediction / mine openings / burger-creep viscoplastic (CVISC) model

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