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Ghasemi, Mohammad, Moodi, Yaser, Mousavi, Seyed Rohollah. (1404). Improving Prediction of Compressive Strength of Rectangular/Square FRP-Confined Concrete Using Machine Learning. نشریه هنرهای کاربردی, (), -. doi: 10.22075/macs.2025.35977.1764
Mohammad Ghasemi; Yaser Moodi; Seyed Rohollah Mousavi. "Improving Prediction of Compressive Strength of Rectangular/Square FRP-Confined Concrete Using Machine Learning". نشریه هنرهای کاربردی, , , 1404, -. doi: 10.22075/macs.2025.35977.1764
Ghasemi, Mohammad, Moodi, Yaser, Mousavi, Seyed Rohollah. (1404). 'Improving Prediction of Compressive Strength of Rectangular/Square FRP-Confined Concrete Using Machine Learning', نشریه هنرهای کاربردی, (), pp. -. doi: 10.22075/macs.2025.35977.1764
Ghasemi, Mohammad, Moodi, Yaser, Mousavi, Seyed Rohollah. Improving Prediction of Compressive Strength of Rectangular/Square FRP-Confined Concrete Using Machine Learning. نشریه هنرهای کاربردی, 1404; (): -. doi: 10.22075/macs.2025.35977.1764

Improving Prediction of Compressive Strength of Rectangular/Square FRP-Confined Concrete Using Machine Learning

Mechanics of Advanced Composite Structures
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 26 تیر 1404    اصل مقاله (1.02 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22075/macs.2025.35977.1764
نویسندگان
Mohammad Ghasemi* 1؛ Yaser Moodi2؛ Seyed Rohollah Mousavi3
1Assistant Professor, Department of Civil Engineering, University of Velayat, Iranshahr, Iran.
2Assistant Professor, Civil Engineering Department, Sirjan University of Technology, Sirjan, Iran.
3Associate Professor, Civil Engineering Department, University of Sistan and Baluchestan, Zahedan, Iran
تاریخ دریافت: 09 آذر 1403،  تاریخ بازنگری: 07 تیر 1404،  تاریخ پذیرش: 26 تیر 1404 
چکیده
Several experimental studies have been conducted on concrete confined with FRP sheets, and various models have been proposed in previous research to determine its compressive strength. However, studies have shown that Machine Learning (ML)-based methods offer higher accuracy than these models. In this study, the effectiveness of different machine learning methods is investigated for predicting the ultimate compressive strength of Rectangular/Square (R/S) FRP-confined concrete columns. These methods include ELM, GMDH, ANFIS, and the Kriging interpolation method. Also, this study proposes utilizing optimization science as a solution to enhance the performance of ANFIS method. As an innovation in this study, Marine Predators Algorithm (MPA) nature-inspired metaheuristic has been used to optimize the parameters of ANFIS method. To show the ability of ML methods to estimate compressive strength, statistical indices were calculated and ML methods is compared; the correlation coefficient (R2) for ELM, GMDH, ANFIS, ANFIS-MPA, and Kriging interpolation methods was equal to 0.89, 0.92, 0.92, 0.93 and 0.98, respectively. Also, these results show that the proposed methods have better performance than the best models in previous studies, with an average error reduction of 62%.
کلیدواژه‌ها
Extreme Learning Machine؛ Adaptive Neural Fuzzy Inference System؛ Marine Predators Algorithm؛ Kriging؛ Compressive strength
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