A Transparent ANN–Excel Model for Shear Strength of Steel Fiber-Reinforced Concrete Beams: Parametric Analysis, and Design Implications

Abstract

In this paper, an ANN-based model is suggested for the prediction of shear strength of steel fiber-reinforced concrete (SFRC) beams without transverse reinforcements, and the model is developed directly using Excel software to ensure computational transparency and statistical interpretability, while remaining an empirical data-driven tool. In this study, a comprehensive database including 923 experimental data is collected, prepared and utilized to develop the neural network. Following the completion of the learning process, the ultimate weight and bias for the neural network are obtained. These parameters are employed in the Excel worksheet. With this process, the learned model is converted into a standalone and operational design tool. Sensitivity analysis, which has been performed through a step-by-step and perturbation method, revealed that the beam width (b) is the most influential parameter in the determination of shear strength (33.4%), followed by concrete compressive strength (fc, 17.1%) and effective depth (d, 15.3%). Then, a parametric study is conducted to study the effect of the shear span-to-depth ratio (a/d), longitudinal reinforcement ratio (ρ) and fiber aspect ratio (Lf/df) on the shear behavior of the members.