In this research, we present an efficient implementation of machine learning (ML) models that forecast the mechanical properties of basalt fiber-reinforced high-performance concrete (BFHPC). The objective of the present study was to predict compressive, flexural, and tensile strengths of BFHPC through ML techniques and propose some correlations between these properties. Moreover, the modulus of elasticity (ME) values and compressive stress–strain curves were simulated using ML techniques. In this regard, three predictive algorithms, including linear regression (LR), support vector regression (SVR), and polynomial regression (PR), were considered. LR, SVR, and PR were utilized to forecast the compressive, flexural, and tensile strengths of BFHPC, and the PR technique was employed to simulate the compressive stress–strain curves. The performance of the models was also determined by the coefficient of determination (R²), mean absolute errors (MAE), and root mean square errors (RMSE). According to the obtained values of R², MAE, and RMSE, the performance of PR was better than other types of algorithms in estimating the compressive, tensile, and flexural strengths. For example, R² values were 0.99, 0.94, and 0.98 in predicting the compressive, flexural, and tensile strengths using PR, respectively. This shows the higher accuracy and reliability of the PR technique compared with other predictive algorithms. Finally, we concluded that ML techniques can be appropriately applied to assess the mechanical characteristics of BFHPC.

Coarse-grained soils are known for their high shear strength and are widely used in geotechnical projects. Shear strength of granular soils can be examined by friction angle which is obtained from direct shear or triaxial test. According to the proposed standards, there are limitations on sample size depending on the diameter of the largest grain. . Furthermore, there is a tendency to use small direct shear instead of large-scale direct shear apparatus, due to some difficulties and lack of large-scale apparatus in most of the laboratories. In this study, a new method is presented for modifying particle size distribution of a well-graded gravel for testing in small direct shear samples. The results showed that the proposed modification reduces the maximum shear strength, peak dilation, and friction angles of the samples. Hence, based on this method, one can determine the friction angle of a coarse-grained soil using small direct shear apparatus.