article · International Journal of Concrete Structures and Materials
Standard design models for reinforced concrete T-beams generally ignore the shear resistance provided by the beam flanges, resulting in designs that are overly conservative and expensive. To address this, five machine learning techniques were trained and tested using 360 experimental datasets to predict shear capacity while accounting for flange contributions. The models evaluated were Decision Tree, Random Forest, Gradient Boosting Regression Tree, Light Gradient Boosting Machine, and Extreme Gradient Boosting. The Extreme Gradient Boosting model delivered the highest accuracy, achieving an R-squared value of 99.10 percent and outperforming several standard design codes and empirical formulas. Analysis of feature importance showed that the shear span-to-depth ratio had the greatest impact on shear capacity, followed by the properties of the shear reinforcement, flange thickness, and flange width. A user interface platform was also created to simplify the use of this predictive model.
Current structural engineering codes often overlook the shear strength added by T-beam flanges, leading to excessive material use and unnecessary construction costs. By accurately capturing how these beam shapes behave under load, structural engineers can produce more efficient, cost-effective, and safe building designs without relying on overly conservative approximations.
This work is at an applied and tested stage, having been validated against experimental datasets and benchmarked against international design codes. The developed user-friendly interface platform directly targets civil and structural engineers, consulting firms, and design software developers seeking more cost-effective beam designs. Wider commercial adoption would require formal integration into commercial structural analysis software and alignment with regional building regulations.
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Abstract The contribution of shear resisted by flanges of T-beams is usually ignored in the shear design models even though it was proven by many experimental studies that the shear strength of T-beams is higher than that of equivalent rectangular cross-sections. Ignoring such a contribution result in a very conservative and uneconomical design. Therefore, the aim of this research is to investigate the capability of machine learning (ML) techniques to predict the shear capacity of reinforced concrete T-beams (RCTBs) by incorporating the contribution of the flange. Five machine learning (ML) techniques, which are the Decision Tree (DT), Random Forest (RF), Gradient Boosting Regression Tree (GBRT), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), are trained and tested using 360 sets of data collected from experimental studies. Among the various machine learning models evaluated, the XGBoost model demonstrated exceptional reliability and precision, achieving an R-squared value of 99.10%. The SHapley Additive exPlanations (SHAP) approach is utilized to identify the most influential input features affecting the predicted shear capacity of RCTBs. The SHAP results indicate that the shear span-to-depth ratio (a/d) has the most significant effect on the shear capacity of RCTBs, followed by the ratio of shear reinforcement multiplied by the yield strength of shear reinforcement ( $${\rho }_{{\text{v}}}{f}_{{\text{yv}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>ρ</mml:mi> <mml:mtext>v</mml:mtext> </mml:msub> <mml:msub> <mml:mi>f</mml:mi> <mml:mtext>yv</mml:mtext> </mml:msub> </mml:mrow> </mml:math> ), flange thickness ( $${h}_{{\text{f}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>h</mml:mi> <mml:mtext>f</mml:mtext> </mml:msub> </mml:math> ), and flange width ( $${b}_{{\text{f}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>b</mml:mi> <mml:mtext>f</mml:mtext> </mml:msub> </mml:math> ). The accuracy of the XGBoost model in predicting the shear capacity of RCTBs is compared with established codes of practice (ACI 318-19, BS 8110-1:1997, EN 1992-1-2, CSA23.3-04) and existing formulas from researchers. This comparison reinforces the superior reliability and accuracy of the machine learning approach compared to traditional methods. Furthermore, a user-friendly interface platform is developed, effectively simplifying the implementation of the proposed machine-learning model. The reliability analysis is performed to determine the value of the resistance reduction factor (ϕ) that will achieve a target reliability index ( $${\beta }_{T}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>β</mml:mi> <mml:mi>T</mml:mi> </mml:msub> </mml:math> = 3.5).
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DOI: 10.1186/s40069-024-00690-z
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