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A novel machine learning-based approach to determine the reduction factor for punching shear strength capacity of voided concrete slabs
Punching shear failure constitutes a critical structural concern in conventional concrete slabs, prompting the development of various equations to determine their maximum shear strength. Voided concrete slabs have gained prominence for their ecological advantages and sustainability. However, the calculation of punching shear strength for voided slabs lacks a standardized approach due to limited experimental data. In this study, an innovative application of machine learning is proposed to address this challenge. Initially, a predictive model for punching shear strength in conventional slabs is established using the Random Forest Regressor (RFR). This model treats voided slabs as solid ones to generate a reduction factor, representing the reduction of punching shear strength in voided concrete slabs compared to the corresponding conventional ones, for estimating the punching shear strength of voided slabs. The coefficient is multiplied by the shear strength of the slabs based on the distance from the exterior side of the column to the first existing hole in the slab and the effective depth of the slab (d). The efficacy of the approach is showcased using the Random Forest Regressor model, finely tuned through a Grid Search technique, with performance evaluated using the R-squared coefficient and Root Mean Squared Error metrics. If the aforementioned distance is less than 0.3d, a factor of 0.55 should be applied. For distances equal to or greater than 0.3d but less than d, a factor of 0.61 should be used. Lastly, for voided concrete slabs where the distance between the first void and the column face is equal to or greater than d, a factor of 0.8 should be used. Additionally, an alternative approach utilizing a direct Random Forest model with fivefold cross-validation has been developed to enhance the robustness of the predictions. The results indicate that this model can effectively estimate the punching shear strength of voided concrete slabs, contributing to the advancement of structural engineering practices.
A novel machine learning-based approach to determine the reduction factor for punching shear strength capacity of voided concrete slabs
Punching shear failure constitutes a critical structural concern in conventional concrete slabs, prompting the development of various equations to determine their maximum shear strength. Voided concrete slabs have gained prominence for their ecological advantages and sustainability. However, the calculation of punching shear strength for voided slabs lacks a standardized approach due to limited experimental data. In this study, an innovative application of machine learning is proposed to address this challenge. Initially, a predictive model for punching shear strength in conventional slabs is established using the Random Forest Regressor (RFR). This model treats voided slabs as solid ones to generate a reduction factor, representing the reduction of punching shear strength in voided concrete slabs compared to the corresponding conventional ones, for estimating the punching shear strength of voided slabs. The coefficient is multiplied by the shear strength of the slabs based on the distance from the exterior side of the column to the first existing hole in the slab and the effective depth of the slab (d). The efficacy of the approach is showcased using the Random Forest Regressor model, finely tuned through a Grid Search technique, with performance evaluated using the R-squared coefficient and Root Mean Squared Error metrics. If the aforementioned distance is less than 0.3d, a factor of 0.55 should be applied. For distances equal to or greater than 0.3d but less than d, a factor of 0.61 should be used. Lastly, for voided concrete slabs where the distance between the first void and the column face is equal to or greater than d, a factor of 0.8 should be used. Additionally, an alternative approach utilizing a direct Random Forest model with fivefold cross-validation has been developed to enhance the robustness of the predictions. The results indicate that this model can effectively estimate the punching shear strength of voided concrete slabs, contributing to the advancement of structural engineering practices.
A novel machine learning-based approach to determine the reduction factor for punching shear strength capacity of voided concrete slabs
Discov Civ Eng
Mahmoudian, Alireza (author) / Mahmoudi, Mussa (author) / Yekrangnia, Mohammad (author) / Tajik, Nima (author) / Mohammadzadeh Taleshi, Mostafa (author)
2025-02-27
Article (Journal)
Electronic Resource
English
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