Volume 14, Issue 3 (6-2024)
IJOCE 2024, 14(3): 445-460 |
Back to browse issues page
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-en.html
URL: http://ijoce.iust.ac.ir/article-1-602-en.html
1- Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran
2- School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
2- School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
Abstract: (2064 Views)
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.
Keywords: Artificial stone Microsilica, Mix design optimization, Artificial neural networks, Metaheuristic algorithms, Enhanced Vibrating Particles System (EVPS), Self-Adaptive Enhanced Vibrating Particles System (SA-EVPS)
Type of Study: Research |
Subject:
Applications
Received: 2024/08/10 | Accepted: 2024/09/11 | Published: 2024/06/23
Received: 2024/08/10 | Accepted: 2024/09/11 | Published: 2024/06/23
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
