MECHANICAL PROPERTIES OF TERNARY BLENDED CONCRETE MADE BY MK-FA-GGBS AND HYBRID FIBERS-EXPERIMENTAL & SIMULATION APPROACH

Because of the excellent strength of concrete reinforced with fibers pulled in the consideration of researchers throughout the most recent decades. The proposed technique manages the experimental investigation to determine the properties of Ternary Blended Fiber Reinforced Concrete (TBFRC) with the assistance of soft computing methodology performed in MATLAB software. In the present experimental examination a mix design of M50 is tried at utilizing ternary blend of Ground Granulated Blast Furnace Slag (GGBS), Fly Ash (FA) and Metakaolin (MK) as partial replacement by weight of concrete at different mixing rates running between 0% – 30% with extra steel and polypropylene fibers. Here, the mechanical properties, for example, compressive strength, split tensile strength, and flexural strength, are anticipated by utilizing Deep Learning Neural Network (DNN) strategy with various fiber rate. The input factors for the neural network depict the materials and different mix extents of concrete. In this network structure, the weights are enhanced by utilizing Adaptive Crow Search Algorithm (ACSA). Additionally by utilizing this system of ternary blended fiber reinforced concrete is delivered at a sensible cost than that of traditional concrete. In addition, the Optimal DNN predicted the mechanical properties optimally for all curing days (28, 56, and 90 days) compared with experimental and existing strategies (ANN).