Ensemble Learning Models for Prediction of Punching Shear Strength in RC Slab-Column Connections

Omid Habibi; Tarik Youssef; Hamed Naseri; Khalid Ibrahim

doi:10.28991/CEJ-SP2024-010-01

Authors

Omid Habibi
omid.habibi@concordia.ca
Department of Building, Civil and Environmental Engineering, Concordia University, Montreal H3G 2W1,, Canada https://orcid.org/0000-0002-5088-2609
Tarik Youssef Faculty of Engineering, L'Université Française d'í‰gypte, Al-Shorouk City 11837,, Egypt
Hamed Naseri Department of Civil, Geological, and Mining Engineering, Polytechnique Montreal, Montreal H3T 1J4,, Canada
Khalid Ibrahim Department of Structural Engineering, Faculty of Engineering, Ain Shams University, Cairo 11535,, Egypt

Vol. 10 (2024): Special Issue "Sustainable Infrastructure and Structural Engineering: Innovations in Construction and Design"

Special Issue "Sustainable Infrastructure and Structural Engineering: Innovations in Construction and Design"

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In reinforced concrete (RC) structures, accurate prediction of the punching shear strength (PSS) of slab-column connections is imperative for ensuring safety. The existing equations in the literature show variability in defining parameters influencing PSS. They neglect potential variable interactions and rely on a limited dataset. This study aims to develop an accurate and reliable model to predict the PSS of slab-column connections. An extensive dataset, including 616 experimental results, was collected from earlier studies. Six robust ensemble machine learning techniques”random forest, gradient boosting, extreme gradient boosting, adaptive boosting, gradient boosting with categorical feature support, and light gradient boosting machines”are employed to predict the PSS. The findings indicate that gradient boosting stands out as the most accurate method compared to other prediction models and existing equations in the literature, achieving a coefficient of determination of 0.986. Moreover, this study utilizes techniques to explain machine learning predictions. A feature importance analysis is conducted, wherein it is observed that the reinforcement ratio and compressive strength of concrete demonstrate the highest influence on the PSS output. SHapley Additive exPlanation is conducted to represent the influence of variables on PSS. A graphical user interface for PSS prediction was developed for users' convenience.

Doi: 10.28991/CEJ-SP2024-010-01

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Habibi, O., Youssef, T., Naseri, H., & Ibrahim, K. (2024). Ensemble Learning Models for Prediction of Punching Shear Strength in RC Slab-Column Connections. Civil Engineering Journal, 10, 1–20. https://doi.org/10.28991/CEJ-SP2024-010-01

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Ensemble Learning Models for Prediction of Punching Shear Strength in RC Slab-Column Connections

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