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Structural Engineering and Mechanics Volume 83, Number 5, September10 2022 , pages 671-680 DOI: https://doi.org/10.12989/sem.2022.83.5.671 |
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Cost-based optimization of shear capacity in fiber reinforced concrete beams using machine learning |
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Nadia Nassif, Zaid A. Al-Sadoon, Khaled Hamad and Salah Altoubat
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| Abstract | ||
| The shear capacity of beams is an essential parameter in designing beams carrying shear loads. Precise estimation of the ultimate shear capacity typically requires comprehensive calculation methods. For steel fiber reinforced concrete (SFRC) beams, traditional design methods may not accurately predict the interaction between different parameters affecting ultimate shear capacity. In this study, artificial neural network (ANN) modeling was utilized to predict the ultimate shear capacity of SFRC beams using ten input parameters. The results demonstrated that the ANN with 30 neurons had the best performance based on the values of root mean square error (RMSE) and coefficient of determination (R2) compared to other ANN models with different neurons. Analysis of the ANN model has shown that the clear shear span to depth ratio significantly affects the predicted ultimate shear capacity, followed by the reinforcement steel tensile strength and steel fiber tensile strength. Moreover, a Genetic Algorithm (GA) was used to optimize the ANN model's input parameters, resulting in the least cost for the SFRC beams. Results have shown that SFRC beams' cost increased with the clear span to depth ratio. Increasing the clear span to depth ratio has increased the depth, height, steel, and fiber ratio needed to support the SFRC beams against shear failures. This study approach is considered among the earliest in the field of SFRC. | ||
| Key Words | ||
| evolutionary algorithm; fiber-reinforced; genetic algorithm (GA); neural network; optimizations; reinforced concrete (RC) structure | ||
| Address | ||
| Nadia Nassif: Department of Civil and Environmental Engineering, College of Engineering, University of Sharjah, P.O. Box 27272 Sharjah, Sharjah, UAE; Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, UAE Zaid A. Al-Sadoon, Khaled Hamad, Salah Altoubat: Department of Civil and Environmental Engineering, College of Engineering, University of Sharjah, P.O. Box 27272 Sharjah, Sharjah, UAE | ||