It is of great importance to determine peak shear strength (PSS) of rock fractures, and data-driven criteria have showed advances in fitting capability in recent years. However, the generalization ability of existing data-driven criteria is limited by dataset size and fracture roughness characterization, which is negative to predictive power and robustness of models. Here we proposed a novel data-driven criterion to predict PSS of rock fractures, with high generalization ability on real experimental data. We first created large-scale low-fidelity dataset by discrete-element modeling, and small-scale high-fidelity dataset by laboratory direct shear tests. The numeric features include normal stress, mechanical properties (including PSS of intact and flat-fracture rock specimens), secondary properties (including internal friction angle, cohesion strength and basic friction angle), and the matrixed feature is topography data. We then established domain adaptation (DA) models for cross-domain knowledge transfer between the low- and high-fidelity datasets, and roughness features were automatically extracted by convolution kernels. The best DA-based model is weighting adversarial neural network, outranking other models by error indicator, and the average relative error on experimental data of new rock types is within 10.0 %. Finally, the sensitivity of input features is investigated, which further proves the promising potential of the developed data-driven PSS criterion of rock fractures in engineering practice.

中文翻译：

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岩石裂隙峰值剪切强度预测中的监督域自适应


确定岩石裂缝的峰值剪切强度 （PSS） 非常重要，近年来数据驱动的标准表明拟合能力取得了进步。然而，现有数据驱动标准的泛化能力受到数据集大小和裂缝粗糙度表征的限制，这对模型的预测能力和稳健性不利。在这里，我们提出了一种新的数据驱动标准来预测岩石裂隙的 PSS，在真实实验数据上具有很高的泛化能力。我们首先通过离散元建模创建了大规模低保真数据集，并通过实验室直接剪切测试创建了小规模高保真数据集。数值特征包括法向应力、力学性能（包括完整和平坦裂隙岩石试件的 PSS）、次要性能（包括内摩擦角、内聚强度和基本摩擦角），矩阵化特征是地形数据。然后，我们建立了域适应 （DA） 模型，用于低保真和高保真数据集之间的跨域知识转移，并通过卷积核自动提取粗糙度特征。最好的基于 DA 的模型是对对抗神经网络进行加权，在误差指标上优于其他模型，新岩石类型实验数据的平均相对误差在 10.0 % 以内。最后，研究了输入特征的敏感性，进一步证明了所开发的数据驱动岩石裂隙 PSS 准则在工程实践中的潜力。