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Structural Monitoring and Maintenance
  Volume 9, Number 4, December 2022 , pages 337-357

Prediction of ultimate shear strength and failure modes of R/C ledge beams using machine learning framework
Ahmed M. Yousef, Karim Abd El-Hady and Mohamed E. El-Madawy

    The objective of this study is to present a data-driven machine learning (ML) framework for predicting ultimate shear strength and failure modes of reinforced concrete ledge beams. Experimental tests were collected on these beams with different loading, geometric and material properties. The database was analyzed using different ML algorithms including decision trees, discriminant analysis, support vector machine, logistic regression, nearest neighbors, naïve bayes, ensemble and artificial neural networks to identify the governing and critical parameters of reinforced concrete ledge beams. The results showed that ML framework can effectively identify the failure mode of these beams either web shear failure, flexural failure or ledge failure. ML framework can also derive equations for predicting the ultimate shear strength for each failure mode. A comparison of the ultimate shear strength of ledge failure was conducted between the experimental results and the results from the proposed equations and the design equations used by international codes. These comparisons indicated that the proposed ML equations predict the ultimate shear strength of reinforced concrete ledge beams better than the design equations of AASHTO LRFD-2020 or PCI-2020.
Key Words
    algorithms; failure modes; ledge beams; machine learning framework; reinforced concrete; ultimate shear strength
Ahmed M. Yousef and Mohamed E. El-Madawy: Department of Structural Engineering, Faculty of Engineering, Mansoura University,
El-Mansoura, 35516, Egypt
Karim Abd El-Hady: Department of Civil Engineering, Faculty of Engineering, Damietta University, New Damietta, 34517, Egypt

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