In this study, uniaxial compressive strength (UCS), unit weight (UW), Brazilian tensile strength (BTS), Schmidt hardness (SHH), Shore hardness (SSH), point load index (Is₅₀) and P-wave velocity (V_p) properties were determined. To predict the UCS, simple regression (SRA), multiple regression (MRA), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) have been utilized. The obtained UCS values were compared with the actual UCS values with the help of various graphs. Datasets were modeled using different methods and compared with each other. In the study where the performance indice PI_at was used to determine the best performing method, MRA method is the most successful method with a small difference. It is concluded that the mean PI_at equal to 2.46 for testing dataset suggests the superiority of the MRA, while these values are 2.44, 2.33, and 2.22 for GEP, ANFIS, and ANN techniques, respectively. The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others. According to the performance index assessment, the weakest model among the nine model is P7, while the most successful models are P2, P9, and P8, respectively.

在这项研究中，单轴抗压强度（UCS），单位重量（UW），巴西拉伸强度（BTS），施密特硬度（SHH），肖氏硬度（SSH），点载荷指数（Is ₅₀）和纵波速度（V _p）的性质被确定。为了预测UCS，已使用了简单回​​归（SRA），多元回归（MRA），人工神经网络（ANN），自适应神经模糊推理系统（ANFIS）和遗传表达编程（GEP）。借助各种图形将获得的UCS值与实际UCS值进行比较。使用不同的方法对数据集进行建模，并相互比较。在研究中，绩效会影响PI_在用来确定最佳性能的方法，MRA方法是最成功的方法，差异很小。结论：平均PI_在等于2.46用于测试数据集暗示MRA的优越性，而这些值分别为2.44，2.33，和2.22 GEP，ANFIS和ANN技术。结果表明，与其他方法相比，MRA可用于预测具有较高承载能力的岩石的UCS。根据性能指标评估，九个模型中最弱的模型是P7，而最成功的模型分别是P2，P9和P8。