Volume 16, Issue 1 (1-2026)
IJOCE 2026, 16(1): 107-129 |
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, Paknahad M, Kaveh A. A COMPREHENSIVE REVIEW OF DATA-DRIVEN AND METAHEURISTIC OPTIMIZATION IN CONCRETE MIXTURE AND STRUCTURAL DESIGN. IJOCE 2026; 16 (1) :107-129
URL: http://ijoce.iust.ac.ir/article-1-667-en.html
URL: http://ijoce.iust.ac.ir/article-1-667-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-16, Iran
2- School of Civil Engineering, Iran University of Science and Technology, Tehran-16, Iran
Abstract: (92 Views)
Concrete mixture design optimization has evolved from traditional trial-and-error approaches to sophisticated computational methods. This paper presents a comprehensive review of optimization techniques applied to concrete mixture proportioning, covering statistical methods (Response Surface Methodology, Taguchi method), particle packing models, machine learning algorithms (Artificial Neural Networks, Random Forest, XGBoost, Support Vector Regression), and metaheuristic optimization techniques (Particle Swarm Optimization, Genetic Algorithms, EVPS, SA-EVPS). The review synthesizes findings from over 180 published studies, with detailed analysis of recent advances in artificial intelligence applications for multi-objective optimization of mechanical properties, cost, workability, durability, environmental sustainability, and structural performance. Key findings indicate that ensemble machine learning methods achieve superior prediction accuracy (R² > 0.95) for compressive strength, while metaheuristic algorithms effectively handle multi-objective trade-offs generating Pareto frontiers. The review also identifies critical research gaps including the need for standardized datasets, interpretable AI models, integration of life cycle assessment, and field validation of optimization results. Recent developments in self-adaptive algorithms (SA-EVPS) demonstrate improved convergence and solution quality for both material and structural optimization problems.
Keywords: Concrete mixture optimization, Machine learning, Metaheuristic algorithms, Sustainable concrete, Artificial intelligence
Type of Study: Research |
Subject:
Optimal design
Received: 2025/12/27 | Accepted: 2026/02/21 | Published: 2026/02/26
Received: 2025/12/27 | Accepted: 2026/02/21 | Published: 2026/02/26
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
