Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning Models

The shear behavior of beams cast with steel fiber reinforced concrete and provided with stirrups is a complex phenomenon that depends on various factors. In the present research effort, a hybrid support vector regression model combined with a particle swarm optimization algorithm is provided, to explore the relationship between the material and dimensional characteristics of a concrete beam and its shear strength. A database with diverse material properties associated with the shear strength of a steel fiber reinforced concrete beam was established from numerous reliable published research articles and was utilized for the development and evaluation of the model. The obtained results from the hybrid support vector regression model were then validated through the results of the artificial neural network and convolutional neural network models combined with the particle swarm optimization algorithm. In conclusion, the adopted hybrid support vector regression approach was proven to be a successful engineering technique that can be used in structural and construction engineering problems.