Hybrid support vector regression approaches for modeling punching shear strength of reinforced concrete flat plates

Abstract

Predicting the punching shear strength (PSS) of flat slabs is crucial for ensuring the safety and efficiency of reinforced concrete structures. This study presents novel hybrid approaches combining support vector regression (SVR) with advanced optimization algorithms to enhance the accuracy of PSS predictions. Four optimization algorithms, krill herd algorithm, biogeography-based optimization, equilibrium optimizer, and genetic algorithm (GA), were employed to optimize SVR parameters for improved PSS estimation. A data set of 264 samples with seven design parameters was used as input to model PSS. Sensitivity analysis and comparison to standard equations were conducted to evaluate the significance of input variables and the reliability of proposed models in predicting PSS. The results demonstrated that integrating optimization algorithms significantly improved the predictive performance of SVR models. Among the proposed approaches, the SVR-GA model achieved the highest accuracy, with a correlation coefficient of 0.95 and a mean absolute error of 132.28 kN in the testing phase. Sensitivity analysis revealed that slab thickness and depth, followed by concrete strength, were the most influential parameters for predicting PSS. The proposed SVR-GA model was found more accurate than American, European, and Canadian concrete code standards in modeling PSS. These findings underscore the effectiveness of hybrid SVR models in accurately modeling PSS and highlight the importance of optimizing input features to ensure robust predictions.

Graphical abstract

Keywords

flat slab / PSS / SVR / hybrid / GA

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