In this study, surface roughness is analyzed within a supervised pattern recognition framework using machine learning methods to present a robust technique for the quantitative determination of surface roughness of rocks. To reach this goal, rock surfaces are classified based on statistical, fractal, geostatistical, directional, and spectral features obtained from the surface profiles as well as the results of direct shear tests. In this way, after the calculation of the features in more than 9000 profiles collected from 84 natural rock samples, a representative vector containing representative features of the surface profiles was introduced for each surface, and joint roughness coefficient (JRC) was back-calculated from 84 direct shear tests on the rock samples. Then, principal component analysis and linear discriminant analysis of the representative vectors were performed in order to prepare the inputs for the classification. Multilayer perceptron (MLP) and radial basis function (RBF) artificial neural networks and support vector machine (SVM) were trained for the classification of rock surfaces. On the other hand, convolutional neural network (CNN) in which features are automatically extracted from the image data of representative surface profiles was the other method for the classification of rock surfaces. The comparison of the results shows RBF is a robust and reliable classifier yielding the least classification error among the investigated methods. Furthermore, comparing the results of machine learning methods with those of traditional equations proves that the SVM, MLP, and RBF yield superior performance.

在这项研究中，使用机器学习方法在监督模式识别框架内分析表面粗糙度，以提出一种定量测定岩石表面粗糙度的稳健技术。为了实现这一目标，根据从表面剖面获得的统计、分形、地质统计、方向和光谱特征以及直剪试验的结果对岩石表面进行分类。这样，对84个天然岩石样本的9000多个剖面特征进行计算后，为每个表面引入包含表面剖面代表性特征的代表向量，并反算节理粗糙度系数（JRC）对岩石样品进行 84 次直剪试验。然后，对代表向量进行主成分分析和线性判别分析，以便为分类准备输入。训练多层感知器（MLP）和径向基函数（RBF）人工神经网络和支持向量机（SVM）来对岩石表面进行分类。另一方面，从代表性表面轮廓的图像数据中自动提取特征的卷积神经网络（CNN）是岩石表面分类的另一种方法。结果的比较表明 RBF 是一种稳健且可靠的分类器，在所研究的方法中产生最小的分类误差。此外，将机器学习方法的结果与传统方程的结果进行比较，证明 SVM、MLP 和 RBF 具有优越的性能。