Techno Press
You logged in as Techno Press

Computers and Concrete
  Volume 28, Number 6, December 2021 , pages 533-547
DOI: https://doi.org/10.12989/cac.2021.28.6.533
 


Prediction of the shear capacity of reinforced concrete slender beams without stirrups by applying artificial intelligence algorithms in a big database of beams generated by 3D nonlinear finite element analysis
George Markou and Nikolaos P. Bakas

 
Abstract
    Calculating the shear capacity of slender reinforced concrete beams without shear reinforcement was the subject of numerous studies, where the eternal problem of developing a single relationship that will be able to predict the expected shear capacity is still present. Using experimental results to extrapolate formulae was so far the main approach for solving this problem, whereas in the last two decades different research studies attempted to use artificial intelligence algorithms and available data sets of experimentally tested beams to develop new models that would demonstrate improved prediction capabilities. Given the limited number of available experimental databases, these studies were numerically restrained, unable to holistically address this problem. In this manuscript, a new approach is proposed where a numerically generated database is used to train machine-learning algorithms and develop an improved model for predicting the shear capacity of slender concrete beams reinforced only with longitudinal rebars. Finally, the proposed predictive model was validated through the use of an available ACI database that was developed by using experimental results on physical reinforced concrete beam specimens without shear and compressive reinforcement. For the first time, a numerically generated database was used to train a model for computing the shear capacity of slender concrete beams without stirrups and was found to have improved predictive abilities compared to the corresponding ACI equations. According to the analysis performed in this research work, it is deemed necessary to further enrich the current numerically generated database with additional data to further improve the dataset used for training and extrapolation. Finally, future research work foresees the study of beams with stirrups and deep beams for the development of improved predictive models.
 
Key Words
    artificial intelligence algorithms; beams without stirrups; design formulae; machine learning; reinforced concrete; shear strength prediction
 
Address
George Markou: Structures Division, Civil Engineering Department, University of Pretoria, Hatfield Campus, 0028 Pretoria, South Africa
Nikolaos P. Bakas: Research and Development Department, RDC Informatics, 2 Irous St, 10442, Athens, Attica, Greece
 

Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2023 Techno Press
P.O. Box 33, Yuseong, Daejeon 305-600 Korea, Tel: +82-42-828-7996, Fax : +82-42-828-7997, Email: info@techno-press.com