ANN-based Shear Capacity of Steel Fiber-Reinforced Concrete Beams
Without Stirrups
Abstract
Comparing experimental results on the shear capacity of steel
fiber-reinforced concrete (SFRC) beams without mild steel stirrups, to
the ones predicted by current design equations and other available
formulations, still shows significant differences. In this paper we
propose the use of artificial intelligence to estimate the shear
capacity of these members. A database of 430 test results reported in
the literature is used to develop an artificial neural network-based
formula that predicts the shear capacity of SFRC beams without shear
reinforcement. The proposed model yields maximum and mean relative
errors of 0.0% for the 430 data points, which represents a better
prediction (mean Vtest / VANN =
1.00 with a coefficient of variation of 1× 10-15) than
the existing expressions, where the best model yields a mean value of
Vtest / Vpred = 1.01 and a
coefficient of variation of 27%.