Volume 15, Issue 3 (8-2025)
IJOCE 2025, 15(3): 317-334 |
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:
Talatahari S, Nouhi B. GENERATIVE ARTIFICIAL INTELLIGENCE IN STRUCTURAL OPTIMIZATION: OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS. IJOCE 2025; 15 (3) :317-334
URL: http://ijoce.iust.ac.ir/article-1-641-en.html
URL: http://ijoce.iust.ac.ir/article-1-641-en.html
1- Data Science Institute, Faculty of Engineering & Information Technology, University of Technology Sydney, 2007 Ultimo, Australia & School of Computing, Macquarie University, Sydney, Australia; Department of Computer Science, Khazar University, Baku, Azerbaijan
2- Data Science Institute, Faculty of Engineering & Information Technology, University of Technology Sydney, 2007 Ultimo, Australia
2- Data Science Institute, Faculty of Engineering & Information Technology, University of Technology Sydney, 2007 Ultimo, Australia
Abstract: (890 Views)
The emergence of Generative Artificial Intelligence (GenAI) presents new possibilities for transforming structural optimization processes in civil and structural engineering. Unlike traditional AI models focused on prediction or classification, GenAI models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, and Large Language Models (LLMs), enable the generation of novel structural designs by learning complex patterns within design-performance data. This paper provides a comprehensive review of how GenAI can support tasks such as design generation, inverse design, data augmentation for surrogate modeling, and multi-objective trade-off exploration. It also examines key challenges, including constraint integration, model interpretability, and data scarcity. By evaluating recent applications and proposing hybrid frameworks that blend generative modeling with domain knowledge and optimization strategies, this study outlines a research roadmap for the responsible and effective use of GenAI in structural optimization. The findings emphasize the need for interdisciplinary collaboration to translate GenAI’s creative potential into physically valid, structurally sound, and engineering-relevant solutions.
Keywords: Generative Artificial Intelligence, Structural Optimization, Surrogate Modeling, Variational Autoencoder, Generative Adversarial Network, Diffusion Models, Large Language Models
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
