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Nazari, Ali, Lale Arefi, Shahin. (1404). Data-Driven Modeling of Concrete Fracture Energy Using Linear Genetic Programming. پژوهشنامه مدیریت اجرایی, 1(1), 89-98. doi: 10.22080/ceas.2025.29159.1008
Ali Nazari; Shahin Lale Arefi. "Data-Driven Modeling of Concrete Fracture Energy Using Linear Genetic Programming". پژوهشنامه مدیریت اجرایی, 1, 1, 1404, 89-98. doi: 10.22080/ceas.2025.29159.1008
Nazari, Ali, Lale Arefi, Shahin. (1404). 'Data-Driven Modeling of Concrete Fracture Energy Using Linear Genetic Programming', پژوهشنامه مدیریت اجرایی, 1(1), pp. 89-98. doi: 10.22080/ceas.2025.29159.1008
Nazari, Ali, Lale Arefi, Shahin. Data-Driven Modeling of Concrete Fracture Energy Using Linear Genetic Programming. پژوهشنامه مدیریت اجرایی, 1404; 1(1): 89-98. doi: 10.22080/ceas.2025.29159.1008

Data-Driven Modeling of Concrete Fracture Energy Using Linear Genetic Programming

Civil Engineering and Applied Solutions
دوره 1، شماره 1، شهریور 2025، صفحه 89-98    اصل مقاله (1.06 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22080/ceas.2025.29159.1008
نویسندگان
Ali Nazari1؛ Shahin Lale Arefi* 2
1Department of Civil Engineering, Sharif University of Technology, Tehran, Iran
2Department of Civil Engineering, Esfarayen University of Technology, Esfarayen, Iran
تاریخ دریافت: 15 اردیبهشت 1404،  تاریخ بازنگری: 26 اردیبهشت 1404،  تاریخ پذیرش: 26 اردیبهشت 1404 
چکیده
The fracture energy of the concrete is an important parameter which can be used to identify the fracture process of concrete members, especially when subjected to tension and flexural loading. Practices to measure this property in experiments can be expensive and time-consuming. In this study, a statistical model using Linear Genetic Programming is introduced to predict concretes' fracture energy with three readily measured input parameters, namely, compressive strength, maximum aggregate size, and water to cemented ratio. The model was developed and trained based on a dataset of 64 measured experimental values taken from published research. The performance of the model was evaluated using statistical indices such as the coefficient of determination, root mean squared error, and mean absolute error, and compared with previously proposed empirical models. The experimental results show that the proposed LGP- based model is superior to old regression-based equations in accuracy and generalization. This model can be a useful methodology for engineers in design and analysis, minimizing the need for large amount of laboratory testing.
کلیدواژه‌ها
Concrete fracture energy؛ Linear genetic programming؛ Prediction model؛ Soft computing؛ Compressive strength؛ Artificial intelligence
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