Novel Empirical Expression to Predict Shear Strength of Reinforced Concrete Walls Based on Particle Swarm Optimization

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Title: Novel Empirical Expression to Predict Shear Strength of Reinforced Concrete Walls Based on Particle Swarm Optimization

Author(s): Hadi Baghi, Hani Baghi, and Sasan Siavashi

Publication: Structural Journal

Volume: 116

Issue: 5

Appears on pages(s): 247-260

Keywords: diagonal crack; empirical equation; particle swarm optimization; reinforced concrete walls; shear strength; tensile stress factor

Date: 9/1/2019

Abstract:
Several different approaches exist to calculate the shear strength of reinforced concrete (RC) walls. However, due to the complex behavior of RC walls, it is challenging to establish a reasonably accurate and straightforward method. This paper presents a simple empirical equation to predict, with an accuracy higher than those of other available methods, the shear capacity of RC walls. Particle swarm optimization (PSO) was employed to derive an empirical equation to predict the shear strength of an RC wall. The proposed approach takes into consideration the variation in the tensile stress factor (β) and change in the inclination of the critical diagonal crack (θ). The proposed method explicitly considers the influence of the amount and strength of vertical and horizontal reinforcements as well as the longitudinal reinforcement of boundary columns (flanges) on the shear strength of the RC walls. Due to the considerable influence of the dowel action in walls with flanges on the shear strength of RC walls, the dowel action was incorporated in the proposed model. A comprehensive database composed of more than 200 RC walls was obtained from the literature to establish the proposed model, and it was subsequently applied for verification purposes. Using the collected database, the predictive capability of the proposed approach was compared with those of the method defined in ACI 318-14 and that proposed by Chandra et al. by using common statistical indicators and the modified demerit points classification criteria.