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.
Hemmatian, A. , Jalali, M. and Naderpour, H. (2025). Algorithms in Machine Learning for Predicting the Pull-Out Energy of Twin-Twisted Fibers within Cementitious Composites. Civil Engineering Infrastructures Journal, (), -. doi: 10.22059/ceij.2025.390540.2253
MLA
Hemmatian, A. , , Jalali, M. , and Naderpour, H. . "Algorithms in Machine Learning for Predicting the Pull-Out Energy of Twin-Twisted Fibers within Cementitious Composites", Civil Engineering Infrastructures Journal, , , 2025, -. doi: 10.22059/ceij.2025.390540.2253
HARVARD
Hemmatian, A., Jalali, M., Naderpour, H. (2025). 'Algorithms in Machine Learning for Predicting the Pull-Out Energy of Twin-Twisted Fibers within Cementitious Composites', Civil Engineering Infrastructures Journal, (), pp. -. doi: 10.22059/ceij.2025.390540.2253
CHICAGO
A. Hemmatian , M. Jalali and H. Naderpour, "Algorithms in Machine Learning for Predicting the Pull-Out Energy of Twin-Twisted Fibers within Cementitious Composites," Civil Engineering Infrastructures Journal, (2025): -, doi: 10.22059/ceij.2025.390540.2253
VANCOUVER
Hemmatian, A., Jalali, M., Naderpour, H. Algorithms in Machine Learning for Predicting the Pull-Out Energy of Twin-Twisted Fibers within Cementitious Composites. Civil Engineering Infrastructures Journal, 2025; (): -. doi: 10.22059/ceij.2025.390540.2253
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