Physics-informed, Generative Adversarial Design of Funicular Shells

TL;DR

This paper introduces a physics-informed GAN framework to design structurally efficient, realistic funicular shell geometries suitable for 3D printed concrete structures, addressing a longstanding challenge in structural design.

Contribution

It presents a novel generative adversarial approach guided by physical constraints to create optimal funicular shell structures, extending GAN applications to structural engineering.

Findings

Model generates realistic, structurally efficient shell geometries.

Framework produces previously unseen, smooth funicular shells.

Results demonstrate stability and physical optimality of generated structures.

Abstract

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.