In recent years, several soft computing models have been proposed to estimate the elastic modulus of magmatic rocks. However, there are lacks in models that consider the different weathering degrees in determining the elastic modulus of rocks. In the literature, mechanical properties are widely used as inputs in predictive models for weathered rocks; however, there are only a few models that use index properties representing the effect of weathering on magmatic rocks. In this study, support vector regression (SVR) Gaussian process regression (GPR), and artificial neural network (ANN) models were developed to predict the elastic modulus of magmatic rocks with different degrees of weathering. The inputs selected by the best subset regression approach were porosity, P-wave velocity, and slake durability index. Key performance indicators (KPIs) were computed to validate the accuracy of the developed models. In addition to KPIs, Taylor diagrams and regression error characteristic (REC) curves were used to assess the performance of the developed prediction models. In this study, considering the difficulties of expressing the error using only RMSE and MAE, a new performance index (PI), PI_MAE, was proposed using normalized MAE instead of normalized RMSE. It was also indicated that PI_RMSE and PI_MAE should be used together in performance analysis. When considering the Taylor diagram, PI_RMSE, and PI_MAE, the GPR models performed best, and the SVR model performed the worst in both the training and test periods. Similarly, according to the REC curve in both periods, the performance of the SVR was the worst, while the performance of the ANN model was the best. The PI_RMSE and PI_MAE values of the GPR model for the test data were 1.3779 and 1.4142, respectively, and they were 1.2567 and 1.4139, respectively, for the ANN model. According to the computed response surfaces, an increase in the P-wave velocity, and a decrease in the porosity increased the elastic modulus. However, changes in slake durability index only had a minor effect on the elastic modulus.

近年来，已经提出了几种软计算模型来估计岩浆岩的弹性模量。然而，在确定岩石弹性模量时，缺乏考虑不同风化程度的模型。在文献中，力学特性被广泛用作风化岩预测模型的输入；然而，只有少数模型使用指数属性来表示风化对岩浆岩的影响。本研究开发了支持向量回归 (SVR) 高斯过程回归 (GPR) 和人工神经网络 (ANN) 模型来预测不同风化程度的岩浆岩的弹性模量。最佳子集回归方法选择的输入是孔隙度、P 波速度和海浪耐久性指数。计算关键绩效指标 (KPI) 以验证开发模型的准确性。除了 KPI 之外，泰勒图和回归误差特征 (REC) 曲线也用于评估开发的预测模型的性能。在本研究中，考虑到仅使用 RMSE 和 MAE 来表示误差的困难，新的性能指标（PI），PI_MAE，提出使用归一化 MAE 代替归一化 RMSE。还指出PI _RMSE和PI _MAE应该在性能分析中一起使用。在考虑泰勒图、PI _RMSE和 PI _{MAE 时}，GPR 模型在训练和测试期间表现最好，而 SVR 模型表现最差。同样，根据两个时期的REC曲线，SVR的性能最差，而ANN模型的性能最好。PI _RMSE和 PI _MAEGPR 模型的测试数据值分别为 1.3779 和 1.4142，ANN 模型的值分别为 1.2567 和 1.4139。根据计算出的响应面，纵波速度的增加和孔隙度的减少增加了弹性模量。然而，熟化耐久性指数的变化对弹性模量的影响很小。