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Mahmoudabadi, Masoud, Hasani, Seyed Mohammad Reza. (1404). Prediction of Shear Capacity of RC Deep Beams Via a Soft Computing Method. پژوهشنامه مدیریت اجرایی, (), -. doi: 10.22080/ceas.2025.29893.1037
Masoud Mahmoudabadi; Seyed Mohammad Reza Hasani. "Prediction of Shear Capacity of RC Deep Beams Via a Soft Computing Method". پژوهشنامه مدیریت اجرایی, , , 1404, -. doi: 10.22080/ceas.2025.29893.1037
Mahmoudabadi, Masoud, Hasani, Seyed Mohammad Reza. (1404). 'Prediction of Shear Capacity of RC Deep Beams Via a Soft Computing Method', پژوهشنامه مدیریت اجرایی, (), pp. -. doi: 10.22080/ceas.2025.29893.1037
Mahmoudabadi, Masoud, Hasani, Seyed Mohammad Reza. Prediction of Shear Capacity of RC Deep Beams Via a Soft Computing Method. پژوهشنامه مدیریت اجرایی, 1404; (): -. doi: 10.22080/ceas.2025.29893.1037

Prediction of Shear Capacity of RC Deep Beams Via a Soft Computing Method

Civil Engineering and Applied Solutions
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 28 شهریور 1404
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22080/ceas.2025.29893.1037
نویسندگان
Masoud Mahmoudabadi* 1؛ Seyed Mohammad Reza Hasani2
1Department of Civil Engineering, Faculty of Engineering, University of Qom, Qom, Iran
2Department of Civil Engineering, Qom Branch, Islamic Azad University, Qom, Iran
تاریخ دریافت: 01 شهریور 1404،  تاریخ بازنگری: 21 شهریور 1404،  تاریخ پذیرش: 27 شهریور 1404 
چکیده
It is well known that the shear capacity of RC deep beams is affected by many mechanical and geometric parameters. The accurate prediction of the shear capacity still stands out as one of the major stumbling blocks in structural engineering practice. Traditional prediction methods have often proven less than precise. On the other hand, artificial intelligence-based methods, particularly those represented by SVMs, have presented themselves as a promising alternative. This research employed an enhanced machine learning technique, known as WLS-SVM, to estimate the shear capacity of reinforced concrete deep beams. In assembling a comprehensive dataset, 214 experimental results were obtained from literature. From selected inputs and outputs, under the supervision of a teaching-learning type approach, a predictive model was derived via WLS-SVM. This model was compared with other AI-based methods and codified design procedures. It presented the best accuracy, with major statistical indicators, including an R² of 0.9804, showing the superiority of the WLS-SVM approach when compared to other methods. Therefore, the study's results reveal WLS-SVM as a very accurate and viable option for the structural calculation and design of reinforced concrete deep beams.
کلیدواژه‌ها
Shear capacity؛ Artificial intelligence؛ Support vector machine؛ Reinforced concrete deep beam
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