Abstract
Steel beams' shear strength is one of the most important factors that influence how quickly webs buckle. Despite extensive studies having been performed over the previous three decades, the existing procedures did not achieve the necessary reliability to predict the ultimate shear resistance of plate girders. New techniques called Learner Techniques have started to be used over the last few years; these techniques were applied to calculate the steel beam shear strength. In this study, a Regression Learner Techniques model was built using data from 100 test results from previously published research. Based on the geometric and material properties of the web and flanges available in the published tests, a model was built using Artificial Neural Networks. Based on sensitivity analysis, a Cascade Forward Backpropagation Neural Networks (CFBNN) approach was utilized to anticipate the shear strength of steel beams. The proposed models outperformed current hybrid artificial intelligence models developed using the same collected datasets and demonstrated to accurately predict the ultimate shear strength. The performance of the models was evaluated using a range of statistical assessment methods, which led to a valuable conclusion. The CFBNN model achieved the highest root mean square (R-2 = 0.95). The results corresponding to each test were verified by specimen shear strength values calculated by a theoretical approach. The resultant maximum shear force obtained by the proposed modified equation was compared with the experimental results and the shear force was estimated using two different approaches proposed by the European code. Finally, two approaches were used to verify the proposed model. The first approach was the data reported from an experimental shear test program conducted by the authors, and the second was the results of the shear values acquired experimentally by other researchers. Based on the test results of the previous studies and the current work, the suggested model gives an adequate degree of accuracy for estimating the shear strength of steel beams.