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Three-dimensional printing technology (3D printing) is becoming a more and more popular technology for aerospace, biology, medicine, mechanics, civil and other engineering fields. In civil engineering, researchers and engineers attempt to print construction materials such as concrete using 3D-printing technology. This study aims to investigate the reinforcement of concrete beams with 3D printing. To achieve this, fused deposition modeling (FDM) technology as a printing method and polylactic acid (PLA) as a material were selected. Two types of geometries were chosen to find the optimal mechanical behavior of concrete: high-performance concrete (HPC) reinforced with four types of trusses (Pratt, Howe, Warren, and Warren with vertical) and ultra-high-performance concrete (UHPC) reinforced with a hyperboloid shell structure. The compressive and tensile strengths of reinforced UHPC were examined by a three-point bending test, and reinforced HPC was examined by a four-point bending test. The results of the experiments show that hyperboloid shell structures can absorb energy, although the strength of reinforced UHPC is reduced. For example, there was a decrease of over 20% in the compressive strength and 41% in the flexural strength, but the ductility was raised. Adding the hyperboloid shell structure improved the deformability of the UHPC. When Warren and Howe trusses were added to the HPC as reinforcements, the flexural strength improved by over 26% and 4.3%, respectively. The overall results of this study show that the concrete reinforced with 3D-printed trusses was better than that with a hyperboloid shell structure.

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 (R 2 ), mean absolute errors (MAE), and root mean square errors (RMSE). According to the obtained values of R 2 , MAE, and RMSE, the performance of PR was better than other types of algorithms in estimating the compressive, tensile, and flexural strengths. For example, R 2 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.

In this research, the authors have developed an algorithm for predicting the compressive strength and compressive stress-strain curve of Basalt Fiber High-Performance Concrete (BFHPC), which is enhanced by a classical programming algorithm and Logistic Map. For this purpose, different percentages of basalt fiber from 0.6 to 1.8 are mixed with High-Performance Concrete with high-volume contact of cement, fine and coarse aggregate. Compressive strengths and compressive stress-strain curves are applied after 7-, 14-, and 28-day curing periods. To find the compressive strength and predict the compressive stress-strain curve, the Logistic Map algorithm was prepared through classical programming. The results of this study prove that the logistic map is able to predict the compressive strength and compressive stress-strain of BFHPC with high accuracy. In addition, various types of methods, such as Coefficient of Determination (R2), are applied to ensure the accuracy of the algorithm. For this purpose, the value of R2 was equal to 0.96, which showed that the algorithm is reliable for predicting compressive strength. Finally, it was concluded that The Logistic Map algorithm developed through classical programming could be used as an easy and reliable method to predict the compressive strength and compressive stress-strain of BFHPC.