Volume 16, Issue 1 (1-2026)
IJOCE 2026, 16(1): 49-62 |
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Taghizadeh A R, Gholizadeh S. SEISMIC LIFE-CYCLE COST OPTIMIZATION OF REINFORCED CONCRETE FRAMES USING METAHEURISTIC ALGORITHMS AND NEURAL NETWORKS. IJOCE 2026; 16 (1) :49-62
URL: http://ijoce.iust.ac.ir/article-1-662-en.html
URL: http://ijoce.iust.ac.ir/article-1-662-en.html
1- Department of Civil Engineering, Urmia University, Urmia, Iran
Abstract: (906 Views)
This paper employs a hybrid approach that integrates a metaheuristic algorithm with a properly trained neural network (NN) to perform seismic life‑cycle cost optimization of reinforced concrete (RC) frames within the framework of performance‑based design. In the proposed hybrid methodology, the center of mass optimization (CMO) metaheuristic algorithm is used to explore the design space. Additionally, a properly trained NN model is employed to estimate the nonlinear seismic response of the RC frames in order to evaluate the design constraints and compute the life‑cycle cost during the optimization process within a reasonable computational time. The efficiency of the proposed hybrid methodology is assessed through two performance‑based design optimization case studies involving 5‑ and 10‑story RC frames. The numerical results demonstrate that the proposed approach is an effective tool for optimizing the life‑cycle cost of RC frames by substantially reducing the computational burden of the optimization process.
Keywords: seismic life cycle cost, performance-based design, nonlinear pushover analysis, neural network, reinforced concrete frame
Type of Study: Research |
Subject:
Optimal design
Received: 2025/12/2 | Accepted: 2026/01/21 | Published: 2026/01/26
Received: 2025/12/2 | Accepted: 2026/01/21 | Published: 2026/01/26
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