Employment of Regression-Based Decision Tools to Predict the Shear Capacity of Reinforced Concrete Beams Without Web Reinforcement
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Abstract
Shear failure in the reinforced concrete (RC) beams with no web reinforcement is a structural problem due to its sudden nature and absence of precursors. The purpose of this research is to predict and assess the shear capacity (Vc) of the beams by applying several statistical regression models. Different combinations of a full linear regression model, a simplified linear model, and stepwise multivariate regression (SMR) models were formulated, tested, and compared. For training and validation, a dataset containing 398 RC beams with different geometries, material properties, and loading configurations was obtained. Other key factors included the beam width, effective depth, reinforcement area, concrete compressive strength, and the shear span-to-depth ratio. The model was developed in Python and SPSS, and the outcomes were evaluated based on R², RMSE, MAE, classification accuracy, and residual analysis. The results indicated that the full linear regression model retained the best predictive performance as indicated by an R² score of 0.9733. However, the fifth-order SMR model scored the lowest RMSE of 17.8 MPa. Furthermore, the simplified linear model greatly underestimated the strength and performed poorly in its predictive functionality. The study emphasizes that stepwise regression model building improves the accuracy of the model while maintaining clear practical relevance. This research enables engineers to make decisions based on reliable data.
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References
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