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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.

This paper presents a comparison between human-defined and AI-generated design spaces through simple optimisation applications.

A design space is a formal expression of a design idea. It is constructed by selecting a set of variables, which limit the search for suitable solutions to a design problem within a specific range of options. Most computational approaches to structural design are based on parametric modelling, which require the definition of a design space, and therefore an analytical formulation of a design idea. In structural optimisation, such approaches tend to limit the search for optimal solutions to a subset of the entire space of design possibilities, and do not necessarily prompt the designer’s creativity.

Recent AI models, such as Variational Autoencoders (VAEs) (Kingma and Welling, 2014), have the potential to overcome some of the limitations described above. VAEs can construct design spaces by extracting implicit design variables from a dataset of design solutions. Such variables result from a learning process and are conditioned exclusively by the characteristics of the dataset, rather than by a human-formalisation of design thoughts.

A VAE has been trained on an artificial dataset of shell structures to construct a design space, which has then been compared with a design space constructed through the explicit definition of design variables. The comparison has been performed by analysing the diversity of the solutions retrieved from both design spaces in two optimisation applications.

The comparison demonstrates that optimisation based on AI-generated design spaces results in a greater diversity of design outputs than the predictable solutions provided by optimisation based on human-defined design spaces. Furthermore, such design outputs respond better to the selected performance criteria.

‘Sketches of Thought’ is a human-machine collaborative design system based on Artificial Intelligence (AI). The first aim of ‘Sketches of Thought’ is illustrating an approach to AI-integration within the designer’s creative workflow. The system translates a hand-drawn architecture sketch into a photorealistic image that suggests a possible evolution of the design idea. The dialogue with the system happens through a visual interface whereby the designer communicates by sketching directly on a drawing monitor, while the system responds by showing the results of the image translation process on a second monitor. Interaction with the system does not end after a first iteration. Instead, the designer is encouraged to adjust the initial sketch – or even make new sketches – for several times to explore, with the aid of the machine’s feedback, different elaborations of an idea. This system does not require particular drawing skills, and therefore anyone can experience a proficient ‘exchange of ideas’ with the AI model. The second aim of ‘Sketches of Thought’ is helping the designer familiarise with AI technology. This is achieved by unveiling the black box of the AI model functioning, that is, through a representation of its internal processes. Moreover, as the AI model simulates some aspects of human cognition, a look inside the black box of AI also means visualising a simplified version of the human mental processes. Therefore, learning about AI is an opportunity for humans to learn more about themselves. The relevance of this virtual prototype is twofold: first, it promotes the view of AI as a means to augment rather than replace the human cognitive capabilities. Second, it challenges the current beliefs and prejudices on AI-technology by making the AI internal processes explicit through a visual representation.

In several of Frei Otto’s early membrane structures, acoustics was a rather relevant design aspect that could not be controlled through classical form-finding approaches based on physical models. Moreover, it is possible to add that, geometrically speaking, Frei Otto’s membranes could naturally perform acoustically, because of their anticlastic shape and the ability of the convex areas to scatter sound. In this paper, two of Frei Otto’s key projects are revisited and redesigned parametrically using mathematical optimisation: first, the Dance Pavilion that was conceived for the Federal Garden Exhibition in Cologne (1957); and, second, the large umbrellas that were built at the same venue in 1971. His architectural form of “necessity” represents the structural optimum, that is, a buildable technology, which can now be informed by other performance criteria, such as acoustics. The work described in this paper could show a possible way of expanding Frei Otto’s two-step design method, which was based on the sequential use of a form-finding model first – to literally “find” structural form – and then a technological model – to study the tectonic aspects of the structure and endow it with materiality. This paper was inspired by the rock set designs of Mark Fisher and – in particular – by the retractable umbrellas that Frei Otto designed, in 1977, for the second leg of Pink Floyd’s “In the Flesh Tour”, which was also known as the “Animals Tour”.