Han Un Chol, a researcher at the Science Engineering Institute, has presented a new method of intelligent back analysis (IBA) using grey Verhulst model (GVM) to identify geotechnical parameters of rock mass surrounding a tunnel, and on this basis, he has validated the accuracy of this method via a test for the main openings of -600m level in a mine.
Displacement components used for back analysis are the crown settlement and sidewalls convergence monitored at the end of openings excavation, and the final closures predicted by GVM.
First, he obtained the nonlinear relation between displacements and back analysis parameters by artificial neural network (ANN) and Burger-creep viscoplastic (CVISC) model of FLAC3D.
Then, he determined the optimal parameters for rock mass surrounding a 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 mm and 2 mm, respectively.
Therefore, it was found that the proposed method could be successfully applied to determination of design parameters and stability for tunnels and underground cavities, as well as mine openings and stopes.
For more information, please refer to his paper “Intelligent back analysis of geotechnical parameters for time-dependent rock mass surrounding mine openings using grey Verhulst model” in “Journal of Central South University” (SCI).
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