Michael J. Ostwald

Purpose: This study investigates how computationally measured formal properties of building façades relate to human verbal perceptions of façade character. It focuses on two key formal dimensions, complexity and order, and examines whether these measurable visual properties correspond to the language observers use when describing façade drawings. Design/methodology/approach: The study combined a dataset of 108 façade line drawings with verbal responses collected from 51 participants. Visual complexity was measured using fractal dimension, while formal order was measured using a computational framework based on bilateral symmetry and two-axis rhythm regularity. Participants' spoken descriptions were transcribed and processed to derive two image-level language variables: “complexity-language score” and “order-language score”. Descriptive statistics, Spearman correlation, ordinary least squares regression, and a 2 × 2 complexity-order typology were used to examine the relationships between computational measures and verbalised perceptions. Findings: The results show that façade drawings vary meaningfully in both computational complexity and formal order. Fractal dimension was positively associated with complexity-related language, while the order index was positively associated with order-related language. Regression models further showed that complexity predicted complexity-language, whereas order predicted order-language. The typology analysis suggested that order-related language was strongest for façades combining relatively low complexity with high order. Originality/value: The study contributes a reproducible computational framework for analysing façade character from line drawings and linking formal image properties to open verbal descriptions. It also introduces a novel human-centred method for operationalising verbalised perception and connecting computational form analysis with architectural perception research.

Architectural designers, historians, and theorists have, until relatively recently, approached the topic of spatial experience using two connected qualitative strategies. The first generalises the ...

Aesthetic judgement in architecture—a capacity to differentiate building façades and forms—is crucial to many built environment policies, processes and determinations. Until recently, this cognitive skill has been the sole domain of humans, whether laypeople or experts, who make intuitive or informed assessments of the visual properties of buildings. However, increasingly, Artificial Intelligence (AI) is being used for this purpose, as it appears to be able to replicate simple human aesthetic reasoning tasks, such as determining how similar or different two façades are. There has been a parallel rise in AI-enabled approaches optimised in response to various architectural theories. Several recent examples use Christopher Alexander’s famous theory of ‘living structure’ and its 15 properties to drive AI-enabled façade assessments. Despite the analytical and regulatory potential of these approaches, neither has been extensively tested against human data to assess its validity. In response, this research reports the results of the first study to compare: (i) baseline AI assessments of façade similarity ( n = 204 façades), (ii) AI assessment using Alexander’s 15 properties for living structure of the same 204 façades, and (iii) human similarity assessments of these façades ( n = 147 participants). To address the knowledge gaps, two pairs of hypotheses are framed for testing using statistical analysis. The research finds that AI can broadly approximate human perceptions of similarity, but they are inconsistent and require nuanced interpretation. Nevertheless, the research demonstrates sufficient utility to suggest that AI could be refined to better match human perceptions, thereby providing a valuable method for professional design and planning assessment.

This qualitative research investigates the adoption of Circular Economy (CE) principles in contemporary Australian architectural practice, referred to as circularity, to address climate change, resource scarcity, and increasing demands for built-environment resilience. The Australian government’s 2035 national circularity target, which aims to double 2024 levels, will have profound implications for architectural practice. This research examines the current and future ability of practices to adopt circularity. It addresses two specific knowledge gaps: (i) how circularity is currently being adopted by architectural practices in Australia, and (ii) what factors restrict or undermine this adoption. To address these gaps, the research draws on insights developed from focus groups and interviews (n = 33 participants) with professional Australian architectural service providers and closely related design and engineering practitioners. Qualitative data collection captured empirical evidence on the barriers, challenges, and opportunities for circularity, followed by NVivo-based Reflexive Thematic Analysis (RTA) that iteratively and inductively identified emerging themes. The findings indicate that architects’ and associated practitioners’ adoption of circularity in Australia is evident but constrained by short-term project horizons, fragmented responsibilities, limited procurement infrastructure, and uncertainty about material supply and skilled labour. The paper concludes that, despite some conceptual ambiguity and structural limitations in current practice models, adoption remains fragmented and selective and offers actions for architects and other stakeholders to address logistical infrastructure, regulatory frameworks, legal contracts, and barriers stemming from a short-term economic value mindset.

Artificial Intelligence (AI) is reshaping building design education and practice, yet its role remains contested. This study examines how AI is being applied across design stages and how its integration can be understood as a holistic and dynamic phenomenon. Drawing on 30 semi-structured interviews with professionals, academics, and students in Australia, a mixed-method analysis was conducted. Frequency analysis reveals that AI is most often used in early phases of design, including ideation, concept development, and visualisation, with more limited uptake in communication, documentation, and regulatory tasks. Hybrid content–thematic analysis highlights how participants conceptualise AI in multiple ways: as a material that expands representational resources, as a creativity support tool that accelerates iteration and enhances productivity, and as a collaborator that reconfigures authorship, decision-making, and team dynamics. These roles are not fixed but shift across contexts, underscoring the hybrid and dynamic character of human–AI design processes. The findings further demonstrate how AI reshapes cognitive processes of reflection and problem-solving, mediates social dynamics of collaboration and culture, and interacts with technical systems of task allocation and integration. Two emergent themes, (i) active learning and (ii) sustainable and ethical practice, point to AI’s potential for transforming pedagogy and contributing to global challenges. By extending sociotechnical perspectives into a Cognitive–Social–Technical (C–S–T) framework, the study advances theoretical, pedagogical, and empirical insights into AI’s evolving role as a collaborator in architectural design education and practice.