دوره 14، شماره 3 - ( 4-1403 )
جلد 14 شماره 3 صفحات 460-445 |
برگشت به فهرست نسخه ها
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Hosseini P, Kaveh A, Naghian A, Abedi A. OPTIMIZATION OF ARTIFICIAL STONE MIX DESIGN USING MICROSILICA AND ARTIFICIAL NEURAL NETWORKS. IJOCE 2024; 14 (3) :445-460
URL: http://ijoce.iust.ac.ir/article-1-602-fa.html
URL: http://ijoce.iust.ac.ir/article-1-602-fa.html
OPTIMIZATION OF ARTIFICIAL STONE MIX DESIGN USING MICROSILICA AND ARTIFICIAL NEURAL NETWORKS. عنوان نشریه. 1403; 14 (3) :445-460
چکیده: (4425 مشاهده)
This study aimed to develop and optimize artificial stone mix designs incorporating microsilica using artificial neural networks (ANNs) and metaheuristic optimization algorithms. Initially, 10 base mix designs were prepared and tested based on previous experience and literature. The test results were used to train an ANN model. The trained ANN was then optimized using SA-EVPS and EVPS algorithms to maximize 28-day compressive strength, with aggregate gradation as the optimization variable. The optimized mixes were produced and tested experimentally, revealing some discrepancies with the ANN predictions. The ANN was retrained using the original and new experimental data, and the optimization process was repeated iteratively until an acceptable agreement was achieved between predicted and measured strengths. This approach demonstrates the potential of combining ANNs and metaheuristic algorithms to efficiently optimize artificial stone mix designs, reducing the need for extensive physical testing.
نوع مطالعه: پژوهشي |
موضوع مقاله:
Applications
دریافت: 1403/5/20 | پذیرش: 1403/6/21 | انتشار: 1403/4/3
دریافت: 1403/5/20 | پذیرش: 1403/6/21 | انتشار: 1403/4/3
| بازنشر اطلاعات | |
 | این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است. |
