No abstract

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.

The shear strength of rock discontinuities is an important design parameter that is noted to be scale dependent. Historically, discontinuity shear strength and scale dependency studies have used small to medium size laboratory shear devices that accommodate specimens with a relatively small area (0.01 to 1 m2) compared to real discontinuities. In the literature, there is a limited amount of shear strength studies available on discontinuity surfaces with areas of several square meters. This is inherently linked to the challenging nature of performing large shear tests. As shear strength scale dependency is still a timely topic, there is still a need for conducting large scale direct shear tests to support research. To facilitate investigations into the shear response of large discontinuities, a very large shear device was designed and built at the University of Newcastle, Australia. The device is designed to accommodate 2 m by 2 m specimens and restrict rotation and translations of the top surface during shearing. This article presents the design and characteristics of the shear device, the process undertaken to create 2 m by 2 m mortar discontinuity replicas and the experimental results of the first series of direct shear tests conducted. The experimental results confirm the systems design for restricting undesired translations and rotations of the tested specimen surfaces and test repeatability.

The scale effect is known to hinder reliable shear strength estimation of large-scale discontinuities. Recently, a stochastic approach was proposed to predict shear strength of large discontinuities directly at problem scale, thereby bypassing the scale effect. One aspect of the stochastic approach seeks to use the available roughness information from the 1D profile of a discontinuity to create a series of statistically representative 3D synthetic rock surfaces, via a rigorous random field model. The application procedure for producing such synthetic surfaces was validated at small scale; however, preliminary large-scale applications were not quite satisfactory. It was found that the absence of consideration for the multiscale nature of discontinuity roughness contributed to the issues encountered. This paper presents the details of the revised multiscale-based 2D LAS approach for producing representative large-scale synthetic surfaces with an emphasis on the effect of sampling interval, called segment length, on the statistics of the synthetic surfaces.