Estimation of ultimate torque capacity of the SFRC beams using ANN

In this study, in order to propose an efficient model to predict the torque capacity of steel fiber reinforced concrete (SFRC) beams, the existing experimental data related to torsional response of beams is reviewed. It is observed that existing data neglects the effects of some parameters on the variation of torque capacity. Thus, an experimental research was also conducted to obtain the effects of neglected parameters. In the experimental study, a total of seventeen SFRC beams are tested against torsion. The parameters considered in the experiments are concrete compressive strength, steel fiber aspect ratio, volumetric ratio of steel fibers and longitudinal reinforcement ratio. The effect of each parameter is discussed in terms of torque versus unit angle of twist graphs. The data obtained from this experimental research is also combined with the data got from previous studies and employed in artificial neural network (ANN) analysis to estimate the ultimate torque capacity of SFRC beams. In addition to parameters considered in the experiments, aspect ratio of beam cross-section, yield strengths of both transverse and longitudinal reinforcements, and transverse reinforcement ratio are also defined as parameters in ANN analysis due to their significant effects observed in previous studies. Assessment of the accuracy of ANN analysis in estimating the ultimate torque capacity of SFRC beams is performed by comparing the analytical and experimental results. Comparisons are conducted in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of efficiency ($E_f$). The results of this study revealed that addition of steel fibers increases the ultimate torque capacity of reinforced concrete beams. It is also found that ANN is a powerful method and a feasible tool to estimate ultimate torque capacity of both normal and high strength concrete beams within the range of input parameters considered.