CEApr 17

Physics-informed, Generative Adversarial Design of Funicular Shells

arXiv:2604.1662110.1h-index: 14
Predicted impact top 76% in CE · last 90 daysOriginality Incremental advance
AI Analysis

For architects and engineers designing 3D-printed concrete shells, this method provides a way to generate structurally efficient, compression-only forms without reinforcement.

This work proposes a physics-informed GAN framework to generate funicular shell structures that are structurally efficient and in pure compression, addressing the long-standing challenge of generalizing the funicular polygon to 3D. The model produces previously unseen, physically optimal shell geometries with high membrane factor distributions.

Shell structures are pivotal in the fields of architecture and engineering, due to their aesthetic appeal and structural efficiency. Recently, 3D concrete printing has reignited the interest in these structures. But, as printed concrete cannot be reinforced with steel, structures built in this way must be designed to withstand primarily pure compression: they must be funicular shells. Nevertheless, a fundamental challenge remains unsolved since Robert Hooke's discovered the catenary arch in 1675: it is not known whether the concept of a funicular polygon can be generalised to three-dimensional structures. Generative Adversarial Networks (GANs), have shown remarkable success in generating realistic data samples matching the distribution of the training data and have been shown to produce highly convincing synthetic images. This work proposes a physics-informed generative adversarial framework for the design of funicular shell structures. The approach employs a modified Deep Convolutional Generative Adversarial architecture physically guided by an auxiliary discriminator to generate realistic and structurally efficient shell geometries. Specifically, the model is constrained by the membrane factor to penalize geometries dominated by bending. An additional discriminator is also employed allowing the model to deal with more complex structures. Results show that the developed model is stable and capable of generating physically optimal, previously unseen, funicular shells with smooth forms and high membrane factor distributions.

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