Open AccessShear capacity of reinforced concrete deep beams using genetic algorithm
Author(s) -
Tadesse G. Wakjira,
Mohamed Ibrahim,
Bilal Sajjad,
Usama Ebead
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/910/1/012002
Subject(s) - shear (geology) , reinforced concrete , structural engineering , reinforcement , materials science , genetic algorithm , computer science , engineering , composite material , machine learning
It is vital to understand the shear behaviour of reinforced concrete (RC) beams in order to avoid a catastrophic shear failure and design for ductile failure. However, due to the complexity in the shear failure mechanism and various parameters influencing the shear behaviour of RC beams, the accuracy in the determination of the shear capacity remains a challenge. In this paper, machine learning and genetic algorithm are utilized to develop an improved shear design equation for RC deep beams without stirrups. The proposed model considers the parameters influencing the shear capacity of beams including concrete compressive strength, cross-sectional dimension of the beams, aspect ratio, and internal reinforcement ratio. The prediction capability of the proposed model has been compared with that of ACI 318 and resulted in a better prediction in terms of safety, accuracy, and economic aspects.