Machine Learning-Based Model for Predicting the Shear Strength of Slender Reinforced Concrete Beams without Stirrups

Odey Alshboul; Ghassan Almasabha; Ali Shehadeh; Rabia Emhamed Al Mamlook; Ali Saeed Almuflih; Naif Almakayeel

doi:10.3390/buildings12081166

Buildings (Aug 2022)

Machine Learning-Based Model for Predicting the Shear Strength of Slender Reinforced Concrete Beams without Stirrups

Odey Alshboul,
Ghassan Almasabha,
Ali Shehadeh,
Rabia Emhamed Al Mamlook,
Ali Saeed Almuflih,
Naif Almakayeel

Affiliations

Odey Alshboul: Department of Civil Engineering, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan
Ghassan Almasabha: Department of Civil Engineering, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan
Ali Shehadeh: Department of Civil Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Shafiq Irshidatst, Irbid 21163, Jordan
Rabia Emhamed Al Mamlook: Department of Industrial Engineering and Engineering Management, Western Michigan University, Kalamazoo, MI 49008, USA
Ali Saeed Almuflih: Department of Industrial Engineering, King Khalid University, King Fahad St, Guraiger, Abha 62529, Saudi Arabia
Naif Almakayeel: Department of Industrial Engineering, King Khalid University, King Fahad St, Guraiger, Abha 62529, Saudi Arabia

DOI: https://doi.org/10.3390/buildings12081166
Journal volume & issue: Vol. 12, no. 8
p. 1166

Abstract

Read online

The influence of concrete mix properties on the shear strength of slender structured concrete beams without stirrups (SRCB-WS) is a widespread point of contention. Over the past six decades, the shear strength of SRCB-WS has been studied extensively in both experimental and theoretical contexts. The most recent version of the ACI 318-19 building code requirements updated the shear strength equation for SRCB-WS by factoring in the macroeconomic factors and the contribution of the longitudinal steel structural ratio. However, the updated equation still does not consider the effect of the shear span ratio (a/d) and the yield stress of longitudinal steel rebars (Fy). Therefore, this study investigates the importance of the most significant potential variables on the shear strength of SRCB-WS to help develop a gene expression-based model to estimate the shear strength of SRCB-WS. A database of 784 specimens was used from the literature for training and testing the proposed gene expression algorithm for forecasting the shear strength of SRCB-WS. The collected datasets are comprehensive, wherein all considered concrete properties were considered over the previous 68 years. The performance of the suggested algorithm versus the ACI 318-19 equation was statistically evaluated using various measures, such as root mean square error, mean absolute error, mean absolute percentage error, and the coefficient of determination. The evaluation results revealed the superior performance of the proposed model over the current ACI 318-19 equation. In addition, the proposed model is more comprehensive and considers additional variables, including the effect of the shear span ratio and the yield stress of longitudinal steel rebars. The developed model reflects the power of employing gene expression algorithms to design reinforced concrete elements with high accuracy.

Published in Buildings

ISSN: 2075-5309 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Technology: Building construction
Website: https://www.mdpi.com/journal/buildings

About the journal

Abstract

Keywords