Predictive modeling of wide-shallow RC beams shear strength considering stirrups effect using (FEM-ML) approach
Ahmed A. Soliman, Dina M. Mansour, Ayman H. Khalil, Ahmed Ebid
TL;DR
This study combines finite element analysis and machine learning to predict the shear strength of wide-shallow reinforced concrete beams, showing high accuracy and the importance of concrete strength and beam geometry.
Contribution
A novel (FEM-ML) approach is introduced to predict shear strength of wide-shallow RC beams with high accuracy using machine learning models.
Findings
The FEM model showed a maximum 8% and 12% difference in loads and deflections compared to experimental results.
Machine learning models achieved over 95% correlation, with ANN reaching 99% accuracy.
Concrete strength and beam aspect ratio were identified as significant factors influencing shear strength.
Abstract
This paper presents an analysis and prediction of the shear strength of wide-shallow reinforced concrete beams, utilizing Finite Element Analysis (FEA) and machine learning techniques. The methodology involves validating a detailed Finite Element Model (FEM) against experimental results, conducting a parametric study, and developing three Machine Learning prediction equations. The FEM captures concrete and steel behaviors, including cracking and crushing for concrete and linear isotropic properties for steel reinforcement. Loading and boundary conditions are defined for accuracy and validated against 13 experimental specimens, exhibiting a maximum 8% and 12% difference in loads and deflections, respectively. A parametric study generates a dataset of 77 wide beam configurations, exploring variations in beam widths, concrete strengths, compression rebars, and shear reinforcement. This…
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Taxonomy
TopicsStructural Behavior of Reinforced Concrete · Innovative concrete reinforcement materials · Concrete Properties and Behavior