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.