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. The paper proposes the use of Artificial Intelligence (AI) 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 (ANN)-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 x 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%.
Highlights
Since concrete is strong in compression but weak in tension, adding steel fibers to the material can be a solution to the limited strength in tension – they keep crack widths small (Amin et al 2016)
The differences for the model proposed in this work, derived from an Artificial Intelligence (AI) technique, are the following: (i) the dataset used comprising 430 lab tests, is significantly larger than the ones used in the previous studies; (ii) a larger number of artificial neural networks (ANN) were simulated for this work; and (iii) the model proposed in this paper yields smaller mean and maximum errors for the aforementioned 430 experimental instances
This measure aims to make the performance ranking of all combos within each ‘small’ analysis more ‘reliable’, since results used for comparison are based on target and output datasets as used in ANN training and yielded by the designed network, respectively
Summary
Since concrete is strong in compression but weak in tension, adding steel fibers to the material can be a solution to the limited strength in tension – they keep crack widths small (Amin et al 2016). When steel fibers are added to a concrete mix, all shear-carrying mechanisms are affected (Lantsoght 2019a) Those mechanics are still not fully understood, which makes it quite meaningful to research the behavior of steel fiber-reinforced concrete (SFRC) with longitudinal reinforcement and no stirrups. Such an approach can study the contribution of steel fibers to the shear capacity of structural concrete (e.g., Torres and Lantsoght 2019), and optimal combinations of steel fiber reinforcement and regular stirrups can be searched. The differences for the model proposed in this work, derived from an AI technique (called artificial neural network), are the following: (i) the dataset used comprising 430 lab tests, is significantly larger than the ones used in the previous studies; (ii) a larger number of ANNs were simulated for this work; and (iii) the model proposed in this paper yields smaller mean and maximum errors for the aforementioned 430 experimental instances
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