Modelling of Shear Strength for Reinforced Concrete Beams Provided with Side-Face Reinforcement in Dependence of Crack Inclination Angle

Abstract

Shear behavior of reinforced concrete beams (RCbeams) is proved to be influenced by different parameters such as web reinforcement, beam size, shear span-to-depth ratio, concrete strength, and longitudinal reinforcement. In addition to these parameters, researches acknowledge the significant contribution of side-face reinforcement (SFR) in shear strength of RC-beams. This paper aims at proposing a new model for predicting shear strength of RC-beams that accounts for the contribution of SFR in shear strength along with the other above-mentioned parameters. An explicit formula is derived based on a mechanical conceptual model that considers the variation of the inclination angle of diagonal shear cracking. The derived formula is verified on the basis of numerical analysis results in addition to the available results from relevant experimental researches in literature. Reliability of the proposed formula is investigated compared to design provisions in different codes. Results demonstrates that the proposed formula is more capable of predicting shear strength of RC-beams provided with SFR rather than shear design codes. Consistency of the proposed formula in predicting shear strength implies that the mechanical concept, on which the proposed formula is derived, is in consistent with the actual mechanical behavior.

Predicting of Punching Shear Capacity of Corroded Reinforced Concrete Slab-column Joints Using Artificial Intelligence Techniques

“…In accordance with the modified compression field theory (MCFT), Wu et al [4] established a prediction model, and numerous experimental data were used to validate its performance in making predictions. Based on the regression linear analysis results of the experimental data, a prediction model was created by Chetchotisak et al [13] But, the above mechanical or experimental models have a problem with the accurate prediction [15]- [17]. This study creates a machine learning (ML) analysis based on the Monte Carlo sampling technique (MCS) for reliability analysis to fulfill the needs of real projects.…”