Strut-and-tie model for predicting shear strength of squat shear walls under earthquake loads. (1st April 2022)

• Record Type: Journal Article
• Authors: Chetchotisak, Panatchai; Chomchaipol, Weerapong; Teerawong, Jaruek; Shaingchin, Somboon
• Is Part Of: Engineering structures. Volume 256(2022)
• Publication Date: 2022-04-01
• Subjects: Structural wall -- Seismic loading -- Reinforced concrete -- Shear strength -- Prediction; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.114042
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A strut-and-tie model for shear strength of squat shear walls was developed. The model was calibrated using 614 test results and validated based on ten-fold cross validation. Better accuracy and reliability were achieved compared to existing approaches. Abstract: In this study, a strut-and-tie model (STM) was established to predict the peak shear strength of squat shear walls with and without boundary elements. The model was based on two independent shear-transferring mechanisms: diagonal strut and truss mechanisms. The first mechanism was derived using the Kupfer failure criterion for concrete. This derivation comprises the interactive correlation between the shear strengths of the diagonal concrete strut and steel tension tie and expresses the shear strength model of squat walls without web reinforcement. The truss mechanism provides shear strength from the orthogonal web reinforcement. Through nonlinear optimization, three empirical coefficients describing the complex behavior of squat walls and the contribution of the web reinforcement, were determined using a data set of 614 test results. The ten-fold cross validation approach was applied to assess the predictive performance and generality of the proposed model. The accuracy of the proposed model was compared with those of ACI 318–19 and state-of-the-art models, and the comparison demonstrates that the proposed STM performs better than existing models.