Accurate estimation of shear strength of deep reinforced concrete beams using meta-heuristic methods

Shear force is one of the most important influencing forces of structural beams and especially unreinforced deep concrete beams that cause sudden failure and sudden collapse of the structure and for this reason, it is of interest to designers and implementers of structures. The study of the conducted research shows that the proposed methods including the use of artificial neural network cannot predict Provide an accurate description of the behavior of deep reinforced concrete beams against shear force. This research, while specifying the cause of the error of the artificial neural network in estimating the shear strength of deep reinforced concrete beams, presents a solution to achieve accurate results by optimizing the artificial neural network by the meta-heuristic algorithm of particle swarm. For this purpose, 309 laboratory samples of deep concrete beams were collected from the research literature and the particle swarm algorithm was used to optimize the weights in the artificial neural network using MATLAB software. The comparison of the results of this method with the results of these letters and other existing methods that were examined in this research showed that the use of the combination of particle swarm algorithm and optimized artificial neural network to estimate the shear strength of unreinforced deep reinforced concrete beams provides more accurate answers.