Title: Empirical Equations for Shear Strength of Conventional Reinforced Concrete Shear Walls

Author(s): Zeynep Tuna Deger and Cagri Basdogan

Publication: Structural Journal

Volume: 118

Issue: 2

Appears on pages(s): 61-71

Keywords: empirical equations; machine learning; reinforced concrete shear wall; shear strength; wall test database

DOI: 10.14359/51728177

Date: 3/1/2021

Abstract:
The majority of shear wall buildings constructed prior to the introduction of modern seismic codes includes inadequate reinforcement and detailing. To achieve an effective evaluation of such buildings, wall behavior should be well-understood and analytical models should be able to capture expected responses as accurately as possible. This study assesses a prominent shear wall characteristic, shear strength, for different dominant wall behaviors. Major seismic code equations and existing empirical expressions are assessed and alternative relations are proposed for conventional reinforced concrete shear walls. Wall shear strength equations provided by ACI 318 and JSC-2001 reveal the closest-to-accurate results for most wall types, although some discrepancies are observed for nonrectangular shear-controlled walls. The proposed empirical equations are derived using regression analyses with a machine learning approach and are valuable in that they are easy to use, interpretable, compatible with physical behavior, and are able to estimate shear strength reasonably close to accurate.