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This paper describes a novel approach to structural optimisation based on learning design strategies rather than searching for optimal solutions. In the proposed approach, an AI agent is trained through Reinforcement Learning (RL) to explore a 3D modelling environment and iteratively morph a flat NURBS surface into a doubly-curved shell structure. At each iteration of the 3D modelling process, the agent computes the maximum structural displacement through FEM analysis: it learns to select modelling actions through this feedback and progressively improves the performance of the input surface. Unlike current applications of RL in structural design, where the AI agent generates design options by recombining a predefined set of design variables, our approach aims to create structural forms through the interaction of a designer and an AI agent within a 3D modelling environment. An application illustrates that our agent can interpret a preliminary structural form defined by a designer and iteratively transform such a form to improve its structural performance. The trained agent can hence transform the geometry and improve the structural performance of any open surface that features a square footprint and is defined through a sequence of modelling commands. Preliminary results suggest that this AI agent can be used for the development of more interactive tools for structural design and optimisation.

The design of music venues, such as concert halls and open-air concert stages, requires an integrated approach in which the acoustic response of the space being created is evaluated at every stage of the process to inform its formal development and associated performance. Various software exists to assess acoustic performance: Odeon is considered by many as the industry standard standalone software for detailed and accurate acoustic analyses, whereas Pachyderm is a plugin for Grasshopper (Rhinoceros 3D), which provides architects and engineers with more rapid feedback on preliminary design ideas because of its integration with a parametric modelling environment. Although an experienced acoustic designer could rapidly learn to use any of these programs, their usefulness during conceptual design or teaching activities is limited by the computational power and time required to get acoustic performance results. In these instances, testing time is more valuable than accuracy. This paper introduces Aeolus, an acoustic modelling plugin for Grasshopper. Unlike other similar Grasshopper plugins, Aeolus allows users to control the simulation accuracy and can therefore be used to rapidly test the performance of design ideas. Aeolus can also easily be interfaced with various Grasshopper optimisation plugins. Aeolus v0.1 was publicly released on 7 March 2023, and will be the focus of a Masterclass offered at the IASS Symposium 2023 in Melbourne, Australia.

Space frame structures are increasingly adopted in contemporary free-form architectural designs due to their elegant appearance and excellent structural performance. However, a space frame structure in a doubly-curved form typically comprises nodes of different shapes. This often requires extensive node customization, hence incurring high manufacturing costs. In this study, we propose a new clustering–optimization framework to reduce the number of different nodes in space frame structures. In clustering, nodes are divided into different groups, with similar shapes grouped together, using an enhanced k-means clustering technique. In optimization, nodes within the same group are transformed towards congruence while closely approximating the target surface. Together, by interleaving clustering and optimization, our method can minimize the node shape variety under a user-defined error threshold. The effectiveness of the method is validated through a variety of numerical examples. The potential practical application of our method is demonstrated by re-designing a complex, free-form architectural project.