###### Abstract
[4], [PERSON] [5], [PERSON] [29], [PERSON] [30], and [PERSON] and [PERSON] [31] carried out laboratory experiments on different rockfill materials and concluded that the behavior of stress-strain is nonlinear, inelastic and based on the level of stress. They also noted that with an increase in maximum particle size for riverbed rockfill material, the angle of internal friction increases, and a reverse pattern for quarried rockfill material is observed. [PERSON] et al. [32] proposed a rational approach for assessing rockfill shear strength on the basis of size effects; [PERSON] and [PERSON] [9] developed power law to relate the shear strength parameter to some index properties of riverbed RFM. Describing the mechanical behavior of rockfill materials and challenges in large-scale strength tests have incited several approaches in modeling the respective behavior of such soils. In this context, the artificial neural network (ANN) approach used by [PERSON] [33] needs fewer rockfill parameters and was found to be more efficient in predicting RFM shear strength. [PERSON] et al. [34] have recently used cubist and random forest regression algorithms and have found that both can deliver better predictive RFM shear strength results than ANN and conventional regression models. This field, however, continues to be further explored. Considering that large-scale strength tests to characterize the shear strength are challenging, ML algorithms based on support vector machine (SVM), random forest (RF), AdaBoost, and k-nearest neighbor (KNN) models are proposed. Furthermore, the ML algorithms SVM, RF, AdaBoost, and KNN have demonstrated excellent prediction efficiency in a variety of fields [35; 36; 37; 38; 39] because of their generalization capability. The application in civil engineering field more significantly in prediction of RFM shear strength is limited based on literature surveys. The main intention of the present study is to explore the capability of using SVM, RF, AdaBoost, and KNN algorithms to establish a more precise and parsimonious behavioral model for predicting the RFM shear strength. A critical review of existing literature suggests that despite the successful implementation of these techniques in various domains, their implementation in the prediction of RFM shear strength is scarcely explored. One of the primary significances of this study is that the data division in the training and testing data sets has been made with due regard to statistical aspects such as maximum, minimum, mean, and standard deviation. The splitting of the data sets is made to determine the predictive capability and generalization performance of established models and later helpsto better evaluate them. Additionally, sensitivity analysis is carried out to find the main parameter influencing RFM shear strength. Concisely, the present study investigated and expanded the scope of machine learning algorithms for the development of the RFM shear strength model, which will provide theoretical support for researchers to establish a basis in selecting optimal machine learning algorithms in improving the predictive performance of RFM shear strength. The rest of this article is structured as follows: The next section introduces the description of the used database and preliminaries of the algorithms used in the proposed approach and discusses the model evaluation metrics. Development of SVM, RF, AdaBoost, and KNN models are described in Section 3. Section 4 is dedicated to the performances and comparison of proposed models. Finally, Section 5 draws conclusions and outlines promising directions for future work.