## 4 Results and Discussion
The SVM achieved a better prediction performance with (R\({}^{2}\) = 0.9655, RMSE = 0.0513 and mean absolute error (MAE) = 0.0184) in comparison to the cubist method (R\({}^{2}\) = 0.9645, RMSE = 0.0975, and MAE = 0.0644) and ANN method (R\({}^{2}\) = 0.9386, RMSE = 0.1320 and MAE = 0.0841) reported by [PERSON] et al. [34] and [PERSON] [33], respectively, for the test data. Additionally, the accuracy of modeling determined by the linear regression method reported by [PERSON] et al. [58]  
Figure 3: Scatter plots of actual vs. predicted RFM shear strength in training stage: (**a**) SVM, (**b**) RF, (**c**) AdaBoost, and (**d**) KNN. between measured and calculated values of shear strength (R\({}^{2}\) = 0.836) was slightly lower than the proposed SVM model. In general, the generalization and reliability of the SVM algorithm perform well, and larger data sets can yield better prediction results. In the present research, a sensitivity analysis was also conducted using [PERSON]'s [59] method to evaluate the influence of input parameters on RFM shear strength. This approach has been used in several studies [60; 61; 62; 63] and is formulated as:  
\[r_{ij}=\frac{\sum_{n=1}^{n}(y_{im}\times y_{om})}{\sqrt{\sum_{m=1}^{n}y_{im}{}^ {2}{\sum_{m=1}^{n}y_{om}}^{2}}} \tag{7}\]  
where \(n\) is the number of data values (this study used 132 data values) and \(y_{im}\) and \(y_{om}\) are the input and output parameters. The \(r_{ij}\) value ranged from zero to one for each input parameter, and the highest \(r_{ij}\) values suggested the most efficient output parameter (which was RFM shear strength in this study). The \(r_{ij}\) values for all input parameters are presented in Figure 6. It can be seen from Figure 6 that the \(\sigma_{n}\) with \(r_{ij}\) is 0.990. Similar research of sensitivity analyses on RFM shear strength was also implemented by [PERSON] [33] and [PERSON] et al. [34]. The findings demonstrated that normal stress is the most sensitive factor, which shows agreement with the present mentioned results. Figure 4: Scatter plots of actual vs. predicted RFM shear strength in testing stage: (a) SVM, (b) RF, (c) AdaBoost, and (d) KNN. Despite the fact that the proposed model produces desirable prediction results, certain limitations should be addressed in the future. 1. Similar to other machine learning methods, the major disadvantages of SVM, RF, AdaBoost, and KNN models are sensitive to the fitness of the data set. Generally, if the data set is small, the generalization and reliability of the model would be influenced. However, the SVM, RF, and AdaBoost algorithms work with a limited data set, i.e., 165 cases, except for KNN. The prediction performances could be better on a larger data set. Furthermore, the developed models can always be updated to yield better results as new data becomes available. 2. Other qualitative indicators such as the Los Angeles abrasion value and lithology may also have influences on the prediction results of the shear strength of RFM. Accordingly, it is significant to analyze the influence of these indicators on the prediction results for improving performance. Figure 5: Comparison of R\({}^{2}\), NSE, RMSE, and RSR values from the SVM, RF, AdaBoost, and KNN models in (**a**) training phase and (**b**) testing phase.