Volume 13, Issue 3 (7-2023)                   2023, 13(3): 327-338 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Hosseini P, Kaveh A, Naghian A. THE USE OF ARTIFICIAL NEURAL NETWORKS AND METAHEURISTIC ALGORITHMS TO OPTIMIZE THE COMPRESSIVE STRENGTH OF CONCRETE. International Journal of Optimization in Civil Engineering 2023; 13 (3) :327-338
URL: http://ijoce.iust.ac.ir/article-1-558-en.html
Abstract:   (4393 Views)
Cement, water, fine aggregates, and coarse aggregates are combined to produce concrete, which is the most common substance after water and has a distinctly compressive strength, the most important quality indicator. Hardened concrete's compressive strength is one of its most important properties. The compressive strength of concrete allows us to determine a wide range of concrete properties based on this characteristic, including tensile strength, shear strength, specific weight, durability, erosion resistance, sulfate resistance, and others. Increasing concrete's compressive strength solely by modifying aggregate characteristics and without affecting water and cement content is a challenge in the direction of concrete production. Artificial neural networks (ANNs) can be used to reduce laboratory work and predict concrete's compressive strength. Metaheuristic algorithms can be applied to ANN in an efficient and targeted manner, since they are intelligent systems capable of solving a wide range of problems. This study proposes new samples using the Taguchi method and tests them in the laboratory. Following the training of an ANN with the obtained results, the highest compressive strength is calculated using the EVPS and SA-EVPS algorithms.
Full-Text [PDF 396 kb]   (2275 Downloads)    
Type of Study: Research | Subject: Optimal design
Received: 2023/04/18 | Accepted: 2023/07/19 | Published: 2023/07/19

Add your comments about this article : Your username or Email:

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Iran University of Science & Technology

Designed & Developed by : Yektaweb