Title: New Equations to Estimate Reinforced Concrete Wall Shear Strength Derived from Machine Learning and Statistical Methods

Author(s): Matias Rojas-Leon, John W. Wallace, Saman A. Abdullah, and Kristijan Kolozvari

Publication: Structural Journal

Volume: 121

Issue: 1

Appears on pages(s): 89-104

Keywords: code equation; machine learning (ML); shear strength; shear wall; statistics; structural wall

DOI: 10.14359/51739187

Date: 1/1/2024

Abstract:
Wall shear-strength equations reported in the literature and used in building codes are assessed using a comprehensive database of reinforced concrete wall tests reported to have failed in shear. Based on this assessment, it is concluded that mean values varied significantly, and coefficients of variation were relatively large (>0.28) and exceeded the target error for a code-oriented equation defined in a companion paper (Rojas-León et al. 2024). Therefore, a methodology employing statistical and machine-learning approaches was used to develop a new equation with a format similar to that currently used in ACI 318-19. The proposed equation applies to walls with rectangular, barbell, and flanged cross sections and includes additional parameters not considered in ACI 318-19, such as axial stress and quantity of boundary longitudinal reinforcement. Parameter limits—for example, on wall shear and axial stress—and an assessment of the relative contributions to shear strength are also addressed.