The article "Transformer model for sensitivity analysis of steel and steel-fiber reinforced concrete beams", written by Stefanie Schoen, Steffen Freitag, Vladislav Gudzulic, and Günther Meschke, has been published in "Advances in Engineering Software" by Elsevier.
Abstract:
Due to inherent uncertainties, it is essential to quantify both aleatory and epistemic uncertainties when assessing the structural behavior and reliability of reinforced concrete (RC) and steel-fiber reinforced concrete (SFRC) structures, as these uncertainties can significantly impact load-bearing capacity and crack development. To enable fast predictions during the design process, circumventing time consuming finite element simulations, and considering implicitly material and structural uncertainties, a novel Transformer-based surrogate model is proposed in this paper. The surrogate model efficiently predicts the history-dependent response of RC and hybrid RC-SFRC beams, specifically, load–displacement and maximum crack width-displacement curves. Unlike conventional feedforward neural networks, the Transformers captures long-range dependencies across the entire loading process in parallel, making it well-suited for path-dependent structural behavior. To assess the influence of key uncertainties, the surrogate model is applied within a systematic sensitivity analysis. Results show that the concrete cover dominates the influence on the load–displacement behavior in RC beams, while the fiber properties govern the response in hybrid RC-SFRC beams. The findings demonstrate the potential of Transformer models as a computationally efficient tool for reliability assessment in structural engineering.
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https://authors.elsevier.com/c/1msaE3Rf7bHSTK.
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https://doi.org/10.1016/j.advengsoft.2026.104166
Chen Xu presented his doctoral theses with the title "Physics Meets Data: Machine Learning Framework
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