###### Abstract  
Supervised machine learning and its algorithms are a developing trend in the prediction of rockfill material (RFM) mechanical properties. This study investigates supervised learning algorithms--support vector machine (SVM), random forest (RF), AdaBoost, and k-nearest neighbor (KNN) for the prediction of the RFM shear strength. A total of 165 RFM case studies with 13 key material properties for rockfill characterization have been applied to construct and validate the models. The performance of the SVM, RF, AdaBoost, and KNN models are assessed using statistical parameters, including the coefficient of determination (R\({}^{2}\)), Nash-Sutcliffe efficiency (NSE) coefficient, root mean square error (RMSE), and ratio of the RMSE to the standard deviation of measured data (RSR). The applications for the abovementioned models for predicting the shear strength of RFM are compared and discussed. The analysis of the R\({}^{2}\) together with NSE, RMSE, and RSR for the RFM shear strength data set demonstrates that the SVM achieved a better prediction performance with (R\({}^{2}\) = 0.9655, NSE = 0.9639, RMSE = 0.1135, and RSR = 0.1899) succeeded by the RF model with (R\({}^{2}\) = 0.9545, NSE = 0.9542, RMSE = 0.1279, and RSR = 0.2140), the AdaBoost model with (R\({}^{2}\) = 0.9390, NSE = 0.9388, RMSE = 0.1478, and RSR = 0.2474), and the KNN with (R\({}^{2}\) = 0.6233, NSE = 0.6180, RMSE = 0.3693, and RSR = 0.6181). Furthermore, the sensitivity analysis result shows that normal stress was the key parameter affecting the shear strength of RFM. AdBoost; support vector machine; k-nearest neighbor; random forest; rockfill materials; shear strength +
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like a Mohr/Coulomb material, albeit without cohesion and with relatively high internal friction angles. Crushed rockfill, loosely layered, can behave like coarse sand. The shear strength of both types of RFM is affected by many factors such as mineral composition, surface structure, particle size, shape, relative density, individual particle strength, etc. [3; 4; 5]. Because of the variable jointing, angularity/roundness, and rock particle size distribution, the RFM can be considered the most complex material [6]. In order to know the mechanical properties of RFMs, extensive field and laboratory research is essential for understanding RFM behavior and determining shear strength parameters in order to design safe and cost-effective structures. In situ direct shear system was used to monitor the shear strength of RFM, as well as the variation in the shear strength of rockfill along with the fill lift [7]. [PERSON] [8] carried out some large-scale shear resistance experiments to simulate the material's original grain size distribution and the expected load level. RFM with a large particle size (maximum particle size of 1200 mm) is incompatible in laboratory testing [9]. Owing to restricting the effects of large particle sizes on test apparatus, such behavior makes it much more difficult to design representative/realistic large-scale strength tests. Furthermore, determining the shear strength of RFM directly is considered a costly and difficult process. Large-scale shear tests are often time-consuming and complex, and estimating the nonlinear shear strength function without using an analytical method is difficult. As a result, several researchers have attempted to determine the mechanical properties of RFM using indirect methods based on machine learning (ML) techniques. In recent years, several researchers used ML algorithms and achieved efficient successes in different civil engineering and other sectors such as environmental [10], geotechnical [11; 12; 13; 14; 15; 16; 17; 18], and other fields of science [19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. Numerous researchers have documented the behavior of the RFM. [PERSON] [3], [PERSON] et al.