Predicting the effect of stirrups on shear strength of reinforced normal-strength concrete (NSC) and high-strength concrete (HSC) slender beams using artificial intelligence

Abstract

The exact effect that each of the basic shear design parameters exerts on the shear capacity of reinforced concrete (RC) beams without shear reinforcement (V c ) is still unclear. Previous research on this subject often yielded contradictory results, especially for reinforced high-strength concrete (HSC) beams. Furthermore, by simply adding V c and the contribution of stirrups V s to calculate the ultimate shear capacity V u , current shear design practice assumes that the addition of stirrups does not alter the effect of shear design parameters on V c . This paper investigates the validity of such a practice. Data on 656 reinforced concrete beams were used to train an artificial neural network model to predict the shear capacity of reinforced concrete beams and evaluate the performance of several existing shear strength calculation procedures. A parametric study revealed that the effect of shear reinforcement on the shear strength of RC beams decreases at a higher reinforcement ratio. It was also observed that the concrete contribution to shear resistance, V c , in RC beams with shear reinforcement is noticeably larger than that in beams without shear reinforcement, and therefore most current shear design procedures provide conservative predictions for the shear strength of RC beams with shear reinforcement.Résumé : L'effet précis que chaque paramètre de conception de base du cisaillement exerce sur la capacité de résis-tance au cisaillement de poutres en béton armé sans renforcement en cisaillement (V c ) n'est pas encore bien connu. Les recherches antérieures sur ce sujet présentaient souvent des résultats de rendement contradictoires, particulièrement pour les poutres en béton à haute résistance. De plus, en ajoutant simplement V c et V s pour calculer V u , la pratique courante en conception contre le cisaillement présume que l'addition d'étriers ne modifie pas l'effet des paramètres de conception contre le cisaillement sur V c . Cet article examine la validité d'une telle pratique. Les données de 656 poutres en béton armé ont été utilisées pour former un modèle de réseau neuronal artificiel à prédire la capacité de résistance au cisaillement de poutres en béton armé et à évaluer le rendement de plusieurs procédures existantes de calcul de la ré-sistance au cisaillement. Une étude paramétrique a indiqué que l'effet du renforcement en cisaillement sur la résistance au cisaillement des poutres en béton armé diminue à un plus haut rapport de renforcement. Il a également été remarqué que la contribution du béton à la résistance au cisaillement, V c , dans les poutres en béton armé avec renforcement en cisaillement est plus importante que dans les poutres sans renforcement en cisaillement et, qu'ainsi, les plus récentes procédures de conception contre le cisaillement fournissent des prévisions conservatrices de résistance au cisaillement des poutres en béton armé avec renforcement en cisaillement.

Shear Strength Prediction of Reinforced Concrete Shallow Beams Without Shear Reinforcements

“…Scholars have implemented the artificial neural networks (ANNs) as an alternative to conventional analytical techniques which are frequently constrained by firm assumptions of normality, linearity, homogeneity, etc. Several neural networks models were found in the literature built to investigate the shear strength of reinforced concrete beams shallow beams without shear reinforcements [7][8][9][10][11][12][13][14][15][16][17]. Elsanadedy et al [7], Oreta [8], Cladera and Mari [9], El-Chabib et al [10], Seleemah [11], Jung and Kim [12], and Keskin [17] applied the artificial neural networks (ANNs) to predict the shear behavior of reinforced concrete shallow beams without shear reinforcements.…”

“…Elsanadedy et al [7], Oreta [8], Cladera and Mari [9], El-Chabib et al [10], Seleemah [11], Jung and Kim [12], and Keskin [17] applied the artificial neural networks (ANNs) to predict the shear behavior of reinforced concrete shallow beams without shear reinforcements. While, Cladera and Mari [13], El-Chabib et al [14], Abdalla et al [15], and Mansour et al [16] applied the artificial neural networks (ANNs) to predict the shear behavior of reinforced concrete shallow beams with shear reinforcements.…”

“…All variables influencing the shear strength are considered [7][8][9][10][11][12][13][14][15][16][17]. The test variables include the concrete compressive strength, the flexural reinforcement ratio, the effective depth of a beam, and the shear span-depth ratio.…”

Artificial Neural Network Model for Low Strength RC Beam Shear Capacity

“…The major challenge in this research area is that of an analytical direction which constitutes a basic approach to understanding shear behavior with respect to material properties and structural analysis (Shah and Ahmad, 2007, Regan, 1993, Oreta, 2004, Jung and Kim, 2008.Analytical models such as co mpressio n field mo dels (Zsutty, dicted the ultimate shear strength based on 111 experimental data processed by ANN. El-Chabib et al (2006) also developed ANN models using 398 experimental data to study the effect of stirrups on shear.…”

Knowledge-based prediction of shear strength of concrete beams without shear reinforcement

“…Seleemah [9] and Kim et al [10], in their independent research, used a new shear database that has 398 experimental data (Reineck et al [1], Kim [2]). El-Chabib et al [11,12] used another large database to develop ANN models, and to study the effect of stirrups on shear. Nehdi et al [13] applied ANN approach in estimating the shear capacity of FRP reinforced concrete beams.…”