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Dorosti, Iman, Jahani, Ehsan. (1405). Prediction of the Compressive Strength of Concrete Circular Columns Confined with FRP Using Neural Networks. پژوهشنامه مدیریت اجرایی, 2(3), 16-32. doi: 10.22080/ceas.2026.30756.1061
Iman Dorosti; Ehsan Jahani. "Prediction of the Compressive Strength of Concrete Circular Columns Confined with FRP Using Neural Networks". پژوهشنامه مدیریت اجرایی, 2, 3, 1405, 16-32. doi: 10.22080/ceas.2026.30756.1061
Dorosti, Iman, Jahani, Ehsan. (1405). 'Prediction of the Compressive Strength of Concrete Circular Columns Confined with FRP Using Neural Networks', پژوهشنامه مدیریت اجرایی, 2(3), pp. 16-32. doi: 10.22080/ceas.2026.30756.1061
Dorosti, Iman, Jahani, Ehsan. Prediction of the Compressive Strength of Concrete Circular Columns Confined with FRP Using Neural Networks. پژوهشنامه مدیریت اجرایی, 1405; 2(3): 16-32. doi: 10.22080/ceas.2026.30756.1061

Prediction of the Compressive Strength of Concrete Circular Columns Confined with FRP Using Neural Networks

Civil Engineering and Applied Solutions
دوره 2، شماره 3، مهر 2026، صفحه 16-32    اصل مقاله (1.62 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22080/ceas.2026.30756.1061
نویسندگان
Iman Dorosti؛ Ehsan Jahani*
Department of Civil Engineering, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran
تاریخ دریافت: 20 آذر 1404،  تاریخ بازنگری: 21 دی 1404،  تاریخ پذیرش: 11 بهمن 1404 
چکیده
This study aims to predict the compressive strength of circular concrete columns confined with Fiber Reinforced Polymer (FRP), particularly for normal and high-strength concrete under axial loading. Existing predictive models have limitations, such as restricted applicability to specific ranges of concrete strength and the inability to account for FRP variations. To address these challenges, this research employs Neural Networks (NN) to enhance prediction accuracy and efficiency. A dataset of 574 data points was compiled from prior studies, encompassing various FRP types and concrete strengths. The NN models were trained using Levenberg-Marquardt (LM) and Bayesian Regularization (BR) methods, with different configurations tested to optimize performance. K-fold cross-validation was performed to ensure robustness. The models were validated and compared with existing approaches using  and MSE as performance metrics. Results showed that the NN models achieved up to an 11.67% improvement in  and an 84.24% reduction in MSE, significantly outperforming traditional methods. This study highlights the potential of NN-based approaches to provide reliable and accurate predictions for FRP-confined concrete columns. These findings offer valuable insights for engineers and designers, paving the way for safer and more efficient structural design practices in the construction industry.
کلیدواژه‌ها
Fiber-reinforced polymer (FRP)؛ Neural network؛ Confinement؛ High strength؛ Concrete columns
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