Optimum design of flexural strength and stiffness for reinforced concrete beams using machine learning

In this paper, an optimization approach was presented for the flexural strength and stiffness design of reinforced concrete beams. Surrogate modeling based on machine learning was applied to predict the responses of the structural system in three-point flexure tests. Three design input variables, the area of steel bars in the compression zone, the area of steel bars in the tension zone, and the area of steel bars in the shear zone, were adopted for the dataset and arranged by the Box-Behnken design method. The dataset was composed of thirteen specimens of reinforced concrete beams. The specimens were tested under three-points flexure loading at the age of 28 days and both the failure load and the maximum deflection values were recorded. Compression and tension tests were conducted to obtain the concrete data for the analysis and numerical modeling. Afterward, finite element modeling was performed for all the specimens using the ATENA program to verify the experimental tests. Subsequently, the surrogate models for the flexural strength and the stiffness were constructed. Finally, optimization was conducted supporting on the factorial method for the predicted responses. The adopted approach proved to be an excellent tool to optimize the design of reinforced concrete beams for flexure and stiffness. In addition, experimental and numerical results were in very good agreement in terms of both the failure type and the cracking pattern. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Studying the compressive, tensile and flexural properties of binary and ternary fiber-reinforced UHPC using experimental, numerical and multi-target digital image correlation methods

Compressive, tensile, and flexural properties of ultra-high-performance concrete (UHPFRC) specimens were studied in this research. Binary and ternary combinations of micro steel (MS), round crimped (RC), crimped (C), hooked-end (H), and polypropylene (PP) fibers were used in overall ratios of 2 % by volume of concrete. For this purpose, 100×200 mm cylindrical specimens, dog-bone specimens (length: 330 mm, width: 80 mm, thickness: 40 mm), and prismatic beams with a dimension of 100×100×500 mm (clear span: 450 mm) were cast and tested under compressive, tensile, and four-point bending tests (4PBT). A digital image correlation (DIC)-based method namely, multi-target digital image correlation (MT-DIC) was used to record the displacement and deflection values in tension and flexure tests. Furthermore, experimental findings were used in numerical simulations and additional analyses were carried out as complementary studies to provide a better understanding of the governing parameters; length, width, depth, and overall size of the beams. Results revealed that a hybrid combination of micro and macro steel fibers performs better than other specimens in all the investigated parameters and the MT-DIC method proved to be a very useful tool in capturing the displacement and deflection values. Furthermore, the inverse analysis approach for the numerical simulation of beams and nonlinear regression-based models captured the direct tension and flexural results with coefficient of determination (R2) values above 0.90