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.

可靠地确定大型原位不连续面的抗剪强度仍然是岩石力学领域面临的挑战。这主要是由于原位不连续的表面粗糙度和形态信息的可用性有限以及“规模效应”现象的未解决管理。最近，建立了一种预测大尺度不连续面剪切强度的随机方法，包括随机场理论、半解析剪切强度模型和随机分析框架。新方法的一个关键方面是在现场规模的应用，从而最大限度地减少或绕过规模效应。该方法已在实验室规模上得到验证，初步的大规模确定性验证显示出有希望的结果。然而，迄今为止，没有进行大规模的基于实验的验证。本文介绍了所采用的半解析抗剪强度模型和随机方法在 2 m×2 m 不连续表面上的首次严格应用，并将预测与实验抗剪强度数据进行了比较。发现剪切强度模型通常在 ± 10% 的相对误差范围内产生峰值和残差预测，预测和观察到的损伤区域之间具有良好的一致性。据观察，将随机方法应用于具有与表面梯度统计等效的梯度统计的种子轨迹，产生的预测与实验结果非常相似。然而，从不同的种子痕迹预测剪切强度会导致预测的更多可变性，其中许多落在实验数据的 ± 20% 以内。