Stochastic analysis of progressive failure of fractured rock masses containing non-persistent joint sets using key block analysis. (January 2016) - Fu, G.Y.; Ma, G.W.; Qu, X.L.; Huang, D.
Graphical abstract: Highlights: The paper presents stability analysis with stochastic representation of rock mass. A robust block generation algorithm was developed to handle non-persistent joints. Scenarios with varying mean joint size show necessity of modelling size accurately. A case study application has also been carried out with validations of joints. The key block statistics is assessed to provide better guidance for support design. Abstract: A stochastic key block method is developed for the analysis of complex blocky rock masses containing non-persistent joint sets. A robust block generation program is developed to model the non-persistent discontinuities. Various uncertainties of geological and geometrical parameters of the discontinuities are considered and Monte Carlo simulations of key blocks are performed. Based on the present analysis, progressive failure of a rock mass can be evaluated in a stochastic manner and the statistics of the key blocks including the total number and volume, the maximum and mean volume, shape and failure mechanism, etc. can be assessed. This approach is applied to a hypothetical horseshoe shaped tunnel in a highly fractured rock mass. Three scenarios with varying mean discontinuity size are analyzed to consider size effect on the predicted blocks and key blocks. It is shown quantitatively that a persistent discontinuity network assumption causes over-fragmentation of predicted blocks, overestimation of key blocks, and underestimation of the largest key block volume compared with non-persistent ones. More realistic representation of the discontinuities by considering the non-persistence is important to give out more reliable failure estimation of fractured rock mass. In addition, a case study application to a slope at the right bank of the Jinping I hydropower station has been conducted. Key block statistics is also helpful in support design.