This paper presents the development and application of a computational design tool that can be used to explore an AI-generated design space for the conceptual design of shell and tensile structures. An AI model was trained to extract geometric features from a dataset of 40 well-known design precedents of shell and tensile structures and to construct a design space. The trained model was then endowed with an interface to allow the designer to explore the design space within CAD software. Unlike the majority of current approaches to parametric design and optimisation, the exploration of the design space-and therefore the interaction between the designer and the computational model-does not take place via design variables, but through visual input. The potential of this tool to support the conceptual design of shell and tensile structures is examined through an application involving iconic design precedents. The application shows that, unlike form-finding and optimisation, this tool generates design suggestions that are not performance-driven, and do not require the statement of the boundary conditions, which would predetermine the results. Despite this, such design suggestions can be considered plausible because they embed specific design knowledge resulting from a re-elaborating process of the main geometric features of the precedents used to train the AI model. These features include, for example, the shape of the openings, the number and location of the support points or the inversion of curvature, where present. The application results question the role of computational tools in conceptual design and illustrate an alternative strategy to explore the design space.

]This paper presents the development and application of a computational workflow for the design and fabrication of acoustic concrete shells. The workflow allows controlling the shape of small, curved panels that can be assembled into continuous surfaces. The panels are designed to comply with the robotic fabrication requirements of a novel Parametric Adjustable Mould (PAM) technology, and assembled to create shells that satisfy a set of acoustic requirements. The technology is used to prototype custom curved concrete panels using a single mould frame that reduces waste in concrete formwork.

This paper presents a theoreticalframework for the implementation of Artificial Intelligence (AI) in architecturaland structural design processes, and it is complemented by some practicalapplications. The aim is to demonstrate that AI can be used to simulate certainaspects of human cognition and can therefore be integrated into CAD software tosupport conceptual design and idea generation in a number of different ways.The aim of this study is also to investigate to what extent AI models caninteract with a designer to explore future forms of human–machine interaction,including autonomous and participative design. This study identifies andapplies AI models to simulate three distinct learning mechanisms: designexpertise, playfulness and analogical reasoning. Each strategy has been appliedto train different AI models, including generative models and reinforcementlearning agents. In the first application, the AI model extracts visualfeatures from a dataset of shell and spatial structures, and then recombinessuch features to generate new design propositions. In the second application,an AI agent learns a design strategy to solve a toy-design problem with noprior knowledge of precedents. The third application illustrates that AI can betrained to discover meaningful features from biological forms and generatesimple design objects through the visual abstraction of such forms. Theapplications demonstrate the ability of AI to synthesise design options andinteract with a designer through visual data formats, such as 2D images and 3Dmodels. This work does not focus on assessing the usefulness of AI models in areal-world design scenario, or on comparing AI with current computationaldesign tools and approaches. It instead investigates different forms of designexploration for computational design purposes, thus paving the way for thedevelopment of future autonomous and participative design systems.

Biodiversity is in a state of global collapse. Among the main drivers of this crisis is habitat degradation that destroys living spaces for animals, birds, and other species. Design and provision of human-made replacements for natural habitat structures can alleviate this situation. Can emerging knowledge in ecology, design, and artificial intelligence (AI) help? Current strategies to resolve this issue include designing objects that reproduce known features of natural forms. For instance, conservation practitioners seek to mimic the function of rapidly disappearing large old trees by augmenting utility poles with perch structures. Other approaches to restoring degraded ecosystems employ computational tools to capture information about natural forms and use such data to monitor remediation activities. At present, human-made replacements of habitat structures cannot reproduce significant features of complex natural forms while supporting efficient construction at large scales. We propose an AI agent that can synthesise simplified but ecologically meaningful representations of 3D forms that we define as visual abstractions. Previous research used AI to synthesise visual abstractions of 2D images. However, current applications of such techniques neither extend to 3D data nor engage with biological conservation or ecocentric design. This article investigates the potential of AI to support the design of artificial habitat structures and expand the scope of computation in this domain from data analysis to design synthesis. Our case study considers possible replacements of natural trees. The application implements a novel AI agent that designs by placing three-dimensional cubes – or voxels – in the digital space. The AI agent autonomously assesses the quality of the resulting visual abstractions by comparing them with three-dimensional representations of natural trees. We evaluate the forms produced by the AI agent by measuring relative complexity and features that are meaningful for arboreal wildlife. In conclusion, our study demonstrates that AI can generate design suggestions that are aligned with the preferences of arboreal wildlife and can support the development of artificial habitat structures. The bio-informed approach presented in this article can be useful in many situations where incomplete knowledge about complex natural forms can constrain the design and performance of human-made artefacts.