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Hemmatian, Abolfazl, Jalali, Meysam, Naderpour, Hosein. (1404). Algorithms in Machine Learning for Predicting the Pull-Out Energy of Twin-Twisted Fibers within Cementitious Composites. , (), -. doi: 10.22059/ceij.2025.390540.2253
Abolfazl Hemmatian; Meysam Jalali; Hosein Naderpour. "Algorithms in Machine Learning for Predicting the Pull-Out Energy of Twin-Twisted Fibers within Cementitious Composites". , , , 1404, -. doi: 10.22059/ceij.2025.390540.2253
Hemmatian, Abolfazl, Jalali, Meysam, Naderpour, Hosein. (1404). 'Algorithms in Machine Learning for Predicting the Pull-Out Energy of Twin-Twisted Fibers within Cementitious Composites', , (), pp. -. doi: 10.22059/ceij.2025.390540.2253
Hemmatian, Abolfazl, Jalali, Meysam, Naderpour, Hosein. Algorithms in Machine Learning for Predicting the Pull-Out Energy of Twin-Twisted Fibers within Cementitious Composites. , 1404; (): -. doi: 10.22059/ceij.2025.390540.2253

Algorithms in Machine Learning for Predicting the Pull-Out Energy of Twin-Twisted Fibers within Cementitious Composites

Civil Engineering Infrastructures Journal
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 21 مرداد 1404    اصل مقاله (2.41 M)
شناسه دیجیتال (DOI): 10.22059/ceij.2025.390540.2253
نویسندگان
Abolfazl Hemmatian1؛ Meysam Jalali* 2؛ Hosein Naderpour3
1Faculty of Civil Engineering, Shahrood University of Technology
2Faculty of Civil Engineering , Shahrood University of Technology.
3Department of Civil Engineering, Toronto Metropolitan University
چکیده
This research examines the pull-out characteristics of twisted twin fibers within concrete employing advanced soft computing methods. The study highlights the necessity for precise predictive models in fiber-reinforced concrete scenarios, considering the intricate interactions between fibers and their surrounding matrix. Artificial Neural Networks (ANN) and Gene Expression Programming (GEP), were created to forecast the pull-out energy needed for fiber extraction. A detailed dataset comprising 228 experimental samples was used, and various models were trained, including 51 ANN designs and 10 GEP configurations. For the first time, a mathematical formula was established using GEP to estimate pull-out energy, showcasing high accuracy with minimal error margins. The ANN model, especially the one utilizing a log-sigmoid activation function, achieved the highest correlation coefficient (0.995), surpassing the GEP model, which also demonstrated a robust correlation (0.98). Sensitivity analysis indicated that compressive strength had the most substantial effect on pull-out energy, accounting for 18.5% of the observed variance. The results offer a new and precise method for predicting fiber pull-out energy, improving the comprehension of fiber-matrix interactions in cement-based materials. Future investigations should aim to broaden the dataset and examine additional fiber shapes to enhance predictive accuracy.
کلیدواژه‌ها
Fiber reinforce concrete؛ Pull-out energy؛ Twin-twisted fibers؛ Artificial neural network؛ Gene expression programming
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تعداد مشاهده مقاله: 373
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