A Large-Scale Application of the Stochastic Approach for Estimating the Shear Strength of Natural Rock Discontinuities.

Reliable shear strength determination of large in situ discontinuities is still a challenge faced by the rock mechanics field. This is principally due to the limited availability of surface roughness and morphology information of in situ discontinuities and the unresolved management of the 'scale effect' phenomenon. Recently, a stochastic approach for predicting the shear strength of large-scale discontinuities was established, encompassing random field theory, a semi-analytical shear strength model, and a stochastic analysis framework. A key aspect of the new approach is the application at field scale, thereby minimising or bypassing the scale effect. The approach has been validated at laboratory scale and an initial large-scale deterministic-based validation showed promising results. However, to date, no large-scale experimental-based validation has been undertaken. This paper presents the first rigorous application of the employed semi-analytical shear strength model and the stochastic approach on a 2 m-by-2 m discontinuity surface, with comparison of prediction to experimental shear strength data. The shear strength model was found to generally produce peak and residual predictions within a ± 10% relative error range, with good agreement between predicted and observed damage areas. It was observed that, applying the stochastic approach to seed traces with gradient statistics equivalent to that of the surface, produced predictions that closely resemble the experimental results. Whereas, predicting shear strength from different seed traces results in more variability of predictions, with many falling within ± 20% of the experimental data. The predictions of residual shear strength tended to be more accurate than peak shear strength. Highlights: The stochastic approach for discontinuity shear strength was applied to a 2 m per 2 m rock surface replica made of mortar. A sensitivity analysis conducted on the analytical shear strength model highlights the parameter influencing the most the shear strength prediction. The predictions of peak and residual strengths fall within 20% of the experimental data. The accuracy of prediction depends on the trace used to define the input statistics. [ABSTRACT FROM AUTHOR]