GENERATIVE ARTIFICIAL INTELLIGENCE MODELS APPLIED TO STEEL STRUCTURES: A SYSTEMATIC LITERATURE REVIEW
Abstract
The growing interest in automating structural design has driven the application of generative artificial intelligence models to tasks such as layout generation and automated design documentation. However, despite recent scientific advances, steel structures remain an underexplored field, with no consolidated systematic review mapping the architectures employed, the tasks addressed, and the persistent gaps in this specific domain. This work aims to conduct a systematic literature review on the application of generative AI models to steel structure design, carried out according to the protocol of Kitchenham (2004) and Kitchenham and Charters (2007). The databases consulted were IEEE Xplore, ScienceDirect, Scopus, SpringerLink, and the CAPES Journal Portal, covering publications from 2019 to April 30, 2026. A total of 895 articles were initially identified, resulting in 12 studies included after application of eligibility and quality criteria. Results reveal a still incipient scientific production concentrated in steel structural layout generation, with a marked scarcity of studies addressing the broader steel structure domain. It is concluded that the field requires deeper exploration of structural typologies, steel connection morphologies, and the incorporation of design specifications into synthetic outputs.
Author Biographies
Bachelor's degree in Civil Engineering from the Federal University of Paraná (UFPR). Master's student in the Post-Graduate Program in Civil Engineering at UFPR.
Ph.D. in Numerical Methods in Engineering from the Federal University of Paraná (2011). Associate Professor in the Department of Civil Construction at UFPR. Permanent faculty member of the Post-Graduate Program in Civil Engineering at UFPR and member of CESEC/UFPR.
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