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Abstract：
The strength of rock under uniaxial compression, commonly known as Uniaxial Compressive Strength (UCS), plays a crucial role in various geomechanical applications such as designing foundations, mining projects, slopes in rocks, tunnel construction, and rock characterization. However, sampling and preparation can become challenging in some rocks, making it difficult to determine the UCS of the rocks directly. Therefore, indirect approaches are widely used for estimating UCS. This study presents two Machine Learning Models, Simple Linear Regression and Step-wise Regression, implemented in Python to calculate the UCS of Charnockite rocks. The models consider Ultrasonic Pulse Velocity (UPV), Schmidt Hammer Rebound Number (N), Brazilian Tensile Strength (BTS), and Point Load Index (PLI) as factors for forecasting the UCS of Charnockite samples. Three regression metrics, including Coefficient of Regression (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), were used to evaluate and compare the performance of the models. The results indicate a high predictive capability of both models. Notably, the Step-wise model achieved a testing R2 of 0.99 and a training R2 of 0.988 for predicting Charnockite strength, making it the most accurate model. The analysis of the influential factors indicates that UPV plays a significant role in predicting the UCS of Charnockite.
摘要：
单轴压缩下岩石的强度，通常称为单轴抗压强度（UCS），在各种地质力学应用中起着至关重要的作用，如设计基础，采矿项目,岩石上的斜坡，隧道施工,和岩石表征。然而,在一些岩石中，取样和准备可能变得具有挑战性，这使得很难直接确定岩石的UCS。因此,间接方法被广泛用于估计UCS。本研究提出了两种机器学习模型，简单线性回归和逐步回归，在Python中实现，以计算Charnockite岩石的UCS。该模型考虑超声波脉冲速度（UPV），施密特锤子反弹数（N），巴西抗拉强度(BTS),和点负荷指数(PLI)作为预测Charnockite样本UCS的因素。三个回归指标，包括回归系数(R2)，均方根误差(RMSE),和平均绝对误差(MAE)，用于评估和比较模型的性能。结果表明，这两个模型都有很高的预测能力。值得注意的是,逐步模型实现了0.99的测试R2和0.988的训练R2，用于预测Charnockite强度，使其成为最精确的模型。对影响因素的分析表明，UPV在预测Charnoccite的UCS中起着重要作用。
