## 5 Conclusions  
This study employed and examined the SVM, RF, AdaBoost, and KNN algorithms in the RFM shear strength prediction problem. To construct and validate a new model on the basis of the aforementioned algorithms, a comprehensive database containing 165 RFM case studies was collected from the available literature. Thirteen different predictive variables for rockfill characterization were selected as the input variables: \(D_{10}\) (mm), \(D_{30}\) (mm), \(D_{60}\) (mm), \(D_{90}\) (mm), \(C_{c}\), \(C_{u}\), \(GM\), \(FM\), \(R\), \(UCS_{min}\) (MPa), \(\gamma\) (kN/m3), \(UCS_{max}\) (MPa), and \(\sigma_{n}\) (MPa). The predictive performance of the proposed models is verified and compared. The conclusions can be outlined as follows:  
1. In this study, the SVM model (R\({}^{2}\) = 0.9656, NSE = 0.9654, RMSE = 0.0153, and RSR = 0.1861) successfully achieved a high level of modeling prediction efficiency to RF (R\({}^{2}\) = 0.9181, NSE = 0.9164, RMSE = 0.0797, and RSR = 0.2891), AdaBoost (R\({}^{2}\) = 0.8951, NSE = 0.8835, RMSE = 0.0941, and RSR = 0.3414), and KNN (R\({}^{2}\) = 0.6304, NSE = 0.6076, RMSE = 0.1727, and RSR = 0.6264) in the test data set. As the same methodology (having the same training and test data sets) for structuring all models is taken into consideration, the SVM model resulted the best and highest performance in this aspect. This implies that this algorithm is robust in comparison with others in RFM shear strength prediction. 2. The performance (in terms of R\({}^{2}\)) of the test data set for the SVM, RF, and AdaBoost algorithms studied falls in the range of 0.9656-0.8951 across the three models with 13 input valuables. Results conclude that it is rational and feasible to estimate the shear strength of RFM from the gradation, particle size, dry unit weight (\(\gamma\)), material hardness, FM, and normal stress (\(\sigma_{n}\)). 3. Sensitivity analysis results revealed that normal stress (\(\sigma_{n}\)) was the key parameter affecting the shear strength of RFM. The findings show that the SVM model is a useful and accurate artificial intelligence technique for predicting RFM shear strength and can be used in various fields. Further, the generalization of the proposed approach for achieving improved performance results, more experimental data should be collected in future research. Finally, RFM shear strength prediction using advanced machine learning algorithms (i.e., deep learning) is left as a future research topic. Figure 6: Sensitivity analysis results. **Author Contributions:** Conceptualization, M.A. ([PERSON]); P.K. and P.O. ; methodology, M.A. ([PERSON]), P.K. ; software, M.A. ([PERSON]) and F.A. ; validation, P.O., F.A., M.A. ([PERSON]), M.A. ([PERSON]) and F.A. ; formal analysis, M.A. ([PERSON]); investigation, M.A. ([PERSON]), F.A., P.K., M.J.I. and S.S.; resources, P.K. and P.O. ; data curation, M.A. ([PERSON]), M.A. ([PERSON]) and M.J.I. ; writing--original draft preparation, M.A. ([PERSON]); writing--review and editing, M.A. ([PERSON]), B.J.K., F.A. and S.S.; supervision, P.K. and P.O. ; project administration, P.K. ; funding acquisition, P.K. and P.O. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data used to support the findings of this study are included within the article. **Conflicts of Interest:** The authors declare no conflict of interest.