Inverse structural design with generative and probabilistic autoencoders and diffusion models
Traditional structural design is a forward trial-and-error process. Designers need to iterate through different design solutions and conduct structural analysis until the design meets the codes and standards. This study proposes and investigates a generative machine learning (ML) framework for inver...
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| Vydáno v: | Engineering applications of artificial intelligence Ročník 161; s. 112143 |
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| Jazyk: | angličtina |
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Elsevier Ltd
01.12.2025
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| ISSN: | 0952-1976 |
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| Abstract | Traditional structural design is a forward trial-and-error process. Designers need to iterate through different design solutions and conduct structural analysis until the design meets the codes and standards. This study proposes and investigates a generative machine learning (ML) framework for inverse design of continuous beam systems. Three generative ML models, including conditional variational autoencoder (CVAE), conditional autoencoder with maximum likelihood estimation (CAE-MLE), and denoising diffusion models (DDMs) are trained and fine-tuned on the CBeamXP (Continuous Beam Cross-section Predictors) dataset with 1,000,000 beam sections to generate the cross sectional properties. Research results show that CAE-MLE achieves the highest generation accuracy and robustness, while CVAE offers more variability through latent space sampling. DDMs provide controllable generation variability via a stochasticity parameter in the inverse diffusion process. The proposed framework enables efficient generation of multiple design solutions and can potentially accelerate the conceptual design workflows in structural engineering. This work also demonstrated the feasibility toward artificial intelligence (AI)-assisted structural design using generative approaches and tabular datasets.
•A generative design framework is developed for continuous beam systems.•Three generative ML models, CVAE, CAE-MLE, and DDMs, are trained and fine-tuned.•The generative design performance of different models is compared.•The design variability is investigated under specific design conditions. |
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| AbstractList | Traditional structural design is a forward trial-and-error process. Designers need to iterate through different design solutions and conduct structural analysis until the design meets the codes and standards. This study proposes and investigates a generative machine learning (ML) framework for inverse design of continuous beam systems. Three generative ML models, including conditional variational autoencoder (CVAE), conditional autoencoder with maximum likelihood estimation (CAE-MLE), and denoising diffusion models (DDMs) are trained and fine-tuned on the CBeamXP (Continuous Beam Cross-section Predictors) dataset with 1,000,000 beam sections to generate the cross sectional properties. Research results show that CAE-MLE achieves the highest generation accuracy and robustness, while CVAE offers more variability through latent space sampling. DDMs provide controllable generation variability via a stochasticity parameter in the inverse diffusion process. The proposed framework enables efficient generation of multiple design solutions and can potentially accelerate the conceptual design workflows in structural engineering. This work also demonstrated the feasibility toward artificial intelligence (AI)-assisted structural design using generative approaches and tabular datasets.
•A generative design framework is developed for continuous beam systems.•Three generative ML models, CVAE, CAE-MLE, and DDMs, are trained and fine-tuned.•The generative design performance of different models is compared.•The design variability is investigated under specific design conditions. |
| ArticleNumber | 112143 |
| Author | Smyl, Danny Gallet, Adrien Zhuang, Bozhou |
| Author_xml | – sequence: 1 givenname: Bozhou orcidid: 0000-0003-2123-8862 surname: Zhuang fullname: Zhuang, Bozhou email: bzhuang31@gatech.edu organization: School of Civil and Environmental Engineering, Georgia Institute of Technology, 756 W Peachtree St NW, Atlanta, 30308, GA, USA – sequence: 2 givenname: Adrien orcidid: 0000-0003-4939-9916 surname: Gallet fullname: Gallet, Adrien email: adrien.gallet@unipart.com organization: Unipart Construction Technologies, Advanced Manufacturing Park, Brunel Way, Sheffield, S60 5WG, UK – sequence: 3 givenname: Danny orcidid: 0000-0002-6730-5277 surname: Smyl fullname: Smyl, Danny email: danny.smyl@ce.gatech.edu organization: School of Civil and Environmental Engineering, Georgia Institute of Technology, 756 W Peachtree St NW, Atlanta, 30308, GA, USA |
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| Cites_doi | 10.1109/CVPR46437.2021.01342 10.1111/mice.13236 10.1016/j.engstruct.2025.120662 10.1016/j.autcon.2025.106129 10.1609/aaai.v38i15.29647 10.1177/14759217241294042 10.1016/j.engappai.2024.109461 10.1061/JCCEE5.CPENG-6076 10.1016/j.engstruct.2022.115170 10.1098/rspa.2021.0526 10.1016/j.aei.2023.102190 10.1002/eqe.3862 10.1016/j.engstruct.2025.119652 10.1016/j.autcon.2022.104470 10.1016/j.autcon.2023.105187 10.1016/j.autcon.2025.106024 10.1088/1361-6420/ad3334 10.1016/j.autcon.2023.105243 10.1016/j.engstruct.2024.118068 10.1016/j.autcon.2023.105019 10.1016/j.autcon.2023.105223 10.1155/2013/271031 |
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| Keywords | Structural design Generative artificial intelligence Tabular data Continuous beams Conditional autoencoders Denoising diffusion models Inverse problem |
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| SubjectTerms | Conditional autoencoders Continuous beams Denoising diffusion models Generative artificial intelligence Inverse problem Structural design Tabular data |
| Title | Inverse structural design with generative and probabilistic autoencoders and diffusion models |
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