This paper illustrates the design and fabrication processes of the Hypar Up pavilion, which served as a proof-of-concept to demonstrate the viability of a design-to-fabrication workflow for complex yet modular architectural geometries that utilise small and planar timber offcuts geometries discretised as Planar Quadrilateral (PQ) meshes. By integrating computational design and optimisation with efficient manufacturing processes, this research highlights the technical challenges of repurposing materials with unknown characteristics, notably detailing solutions, and evaluates the efficiency of design-to-manufacturing workflows with surplus timber products, using a quantitative cost analysis of the fabrication and assembly phases. While exploring the potential of repurposing scrap wood into hypar-shaped modular construction components, this work expands on existing research on segmented shells and investigates methods and means to move beyond the use of shell structures as monolithic and static artefacts. The pavilion is intended as a 1:1 modular prototype that can be resized to accommodate different dimensions of the timber panel offcuts and potential applications to be tested in future applications, such as load-bearing walls and facade retrofitting.

This paper presents the different steps of the journey that led to the development and implementation of a design ePortfolio as part of the “Architecture Major” of the “Bachelor of Design” (BDes) at The University of Melbourne, Australia. This design ePortfolio was developed over two years through interviews, focus groups with students, and the development of exercises and assignment guidelines. The implementation took place in different phases, starting with the introduction of four thematic exercises in the Capstone Architecture Design Studio of the BDes, and then, more broadly, at the curriculum level, with similar activities that prepared the students to already start developing a reflective journal from the first year of their studies.
The objective of this research has been to explore the potential of integrating a design ePortfolio with traditional design portfolios on the curricular activities of an undergraduate architecture degree to prioritise the students’ reflections on their personal qualities, growth, career trajectory and goals, as well as on the relationship between curricular and extracurricular activities rather than those of a conventional design portfolio, which tend to be focused more on showcasing skills and competencies. The results of this two-year project illustrate the potential of such a teaching/learning tool to link the personal and professional interests and achievements of students holistically, therefore highlighting the relationship between curricular and extracurricular activities, as well as indirect connections between design studios and seminar-based subjects, that is, history and architectural technology subjects.
So far, our design ePortfolio has been successfully used to encourage architecture students to reframe problems through a different lens and, as a key to creativity, to distinguish themselves from the homogeneity of students the university produces through its degree structure and modes of operation. The significance of this teaching and learning project lies in the identification of patterns in the curricular and extracurricular experiences of students that helps them define their identity, goals, and purpose. For this reason, this research has the potential of being linked to the University’s “Student Life” project, which is related to academic advice, personal growth, mentorship and the well-being of students.

Practitioners across domains—from architectural design (Zari, 2010; Ha and Lu, 2020), medical interventions (Chen et al., 2021), and robotics (Coyle et al., 2018; Ahmed et al., 2022) to materials science (Wegst et al., 2015)—are increasingly drawing inspiration from biology to address a wide range of challenges. This is perhaps unsurprising because life on earth represents over 3.8 billion years of evolution, whereby natural selection ruthlessly purges design failures. Despite the growth and promise of biomimetic and bioinspired approaches, the analogy to biological form or function is often superficial or largely figurative. This Research Topic was born from the topic editors' shared belief that to capitalize on insights from biology, we need to move beyond figurative referencing toward functional analogy informed by accurate biological knowledge. For this reason, we advocate a biologically-informed (or bio-informed) approach that captures key properties of living organisms and systems, such as sustainability, multifunctionality and self-assembly.

The articles collected in this interdisciplinary Research Topic illustrate the strengths of a bio-informed approach to design processes and applications. Ultimately, the goal is to demonstrate how simple concepts can be abstracted from highly complex and inter-connected biological materials, structures and processes to be applied or manufactured at scale.

A bio-informed approach requires deep collaboration between biologists and practitioners in other fields. To gauge the current extent of engagement with biologists in biomimetic and bioinspired research,Ng et al. surveyed the literature over 30 years (1990–2020) to reveal that only 41% of research papers published in the field included an author affiliated to a biology-related department, and most of them focus on a limited range of popular model species. The authors show the value of capitalizing on biological diversity and understanding the ecological and evolutionary context of the biological models. They also highlight that interdisciplinary engagement is a two-way street—for example, application of engineering approaches can improve biological understanding just as biology can offer new engineering solutions.

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 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.