Modified interactive strut-and-tie modeling of reinforced concrete deep beams and corbels. (November 2022)

• Record Type: Journal Article
• Authors: Chetchotisak, Panatchai; Teerawong, Jaruek; Yindeesuk, Sukit
• Is Part Of: Structures. Volume 45(2022)
• Page Start: 284
• Page End: 298
• Publication Date: 2022-11
• Subjects: Shear strength -- Strut-and-tie -- Reinforced concrete -- Short beam -- Bracket -- Prediction; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2022.08.116
• Languages: English
• ISSNs: 2352-0124

Abstract

Highlights: A modified interactive strut-and-tie model for deep beams and corbels was developed based on the new proposed failure envelope of concrete. The model was calibrated against datasets of 682 deep beams and 338 corbels and validated based on tenfold cross-validation. The model provides more precise and consistent prediction than existing models. The model reasonably describes size effect in shear, deep beams subjected to unsymmetrical loads and corbels supported by wide columns. Abstract: In this study, a strut-and-tie model (STM) for predicting the shear strength of reinforced concrete deep beams and corbels was proposed. The model was derived on the basis of two independent force-transferring mechanisms, namely, diagonal strut and truss mechanisms. The first mechanism was modified from the so-called interactive strut-and-tie model (ISTM). The novelty comprises the derivation of a new proposed failure criterion of concrete used to develop the strength contributed by the strut mechanism. The truss mechanism represents the strength contribution from the orthogonal web reinforcement. The combination of the strength contributions from the two mechanisms results in the predicted shear strength. The model was calibrated on a large database of 682 deep beams and 338 corbels and verified using tenfold cross-validation. The shear strength predicted by the proposed STM was benchmarked against those computed using the original ISTMs and other state-of-the-art models. The comparison indicates that the proposed STM affords a more accurate prediction than the benchmark approaches.