Comparative analysis of machine learning techniques for predicting flexural behavior in RC beams

Yonas Alemu; Naveen Bhari Onkareswara; Habtamu Alemayehu

doi:10.1007/s42107-025-01507-4

August 14, 2025Asian Journal of Civil Engineering

Comparative analysis of machine learning techniques for predicting flexural behavior in RC beams

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Key Points

Flexural behavior predictions outperform traditional methods, significantly enhancing structural analysis accuracy.
Machine learning techniques, notably random forest and neural networks, showed the best predictive performance in testing.
Analysis utilizes historical data from reinforced concrete beam tests and various machine learning algorithms for comparison.
These findings indicate the potential for machine learning to transform engineering practices in structural assessments.

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Implication

Observational analysis compares machine learning techniques predicting flexural behavior in reinforced concrete beams, suggesting improved modeling accuracy.

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Alemu et al. (2025) studied this question.

synapsesocial.com/papers/69a75abac6e9836116a20ed0 https://doi.org/10.1007/s42107-025-01507-4

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Comparative analysis of machine learning techniques for predicting flexural behavior in RC beams

Key Points

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Discussion

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Comparative analysis of machine learning techniques for predicting flexural behavior in RC beams

Key Points

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Implication

Discussion

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