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• W4220795864 abstract "Intelligent structural design using generative adversarial networks (GANs) is a revolutionary design approach for building structures. Despite its far-reaching capability, the data quantity and quality may have limited the performance of such a data-driven network. This study proposes to enhance the objectiveness of training processes by innovatively introducing a surrogate model, Physics Estimator, that informs the generator by appraising the physical behavior of the generated design. Dual loss functions evaluated by a traditional data-driven discriminator and the Physics Estimator collaboratively foster the physics-enhanced GAN architecture. We further develop a structural mechanics model to train and optimize the inherent accuracy of the Physics Estimator. The comparative study suggests that the proposed physics-enhanced GAN can generate structural designs from architectural drawings and specified design conditions 44% better than a data-driven design method and 90 times faster than a competent engineer." @default.
• W4220795864 created "2022-04-03" @default.
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• W4220795864 date "2022-03-07" @default.
• W4220795864 modified "2023-10-17" @default.
• W4220795864 title "Intelligent structural design of shear wall residence using physics‐enhanced generative adversarial networks" @default.
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• W4220795864 doi "https://doi.org/10.1002/eqe.3632" @default.
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