International Journal of Architectural Computing

Can computers make our designs more intelligent and better informed? This is the implication of the theme of the special issue. Architectural design is often thought of as the design of the object, and design models of architecture seek to explicate this process. As an architect, however, I cannot subscribe to that view. In this particular article, I will explore how computational approaches have illuminated and expanded my work to enable the interaction of these themes across scores of projects. Underpinning the projects are foundational concepts: design is real, complex, inclusive, emergent and evil. Design is grounded in reality and facts, that we can derive design outcomes from a deep and unblemished understanding of the world around us. It is not a stylistic escape. Reality is complex. Architectural design has sought to simplify. This was inescapable when projects are so large yet need to be communicated succinctly. ‘Less is more’ justified this approach. In town planning, this is evident in the tool of zoning. Parse the problem and then address each piece. What we do is part of a larger effort. The field of architecture seeks distinction. Design theories want to distinguish and elevate architecture. But if design is complex and it is real, then it is tied to messy realism. Designing has to become accessible to other realms of knowledge. Designing is the seeking of opportunity. For many, design is simply finding the answer – think of Herbert Simon’s statement that design is problem solving. Design reveals opportunities, and these emergent conditions are to be grasped. As designers, our decisions have implications. We know now that what we build has future implications in ways that are profound. When we define design as problem solving, we ignore the truth that design is problem making.

This article presents the results of the Urban Strategy Playground research group. Over the last 5 years, the focus of an interdisciplinary team of researchers was the conception, implementation and evaluation of a decision-support system for inner-city urban and architectural planning. The overall aim of past and ongoing research is to enable planners to validate and compare possible planning measures based on objective criteria. The Urban Strategy Playground software framework is an expandable toolbox that supports planners in developing strategies, evaluating them and visually preparing them for political decision-making processes and public participation. Examples of implemented tools are the simulation and monitoring of building codes, analysis of key density indicators and green space provision, simulation of shading, building energy and noise dispersion. For visualising the planning results, the framework provides interfaces for rapid prototyping of haptic models, as well as web viewers and a connection to Augmented Reality applications. Core aspects of the system were evaluated through case studies in cooperation with urban planning offices, housing companies and municipalities, proving feasibility, high acceptance of the decision-support software, and need for more tailored tools.

This article describes a novel algorithm for built environment 2.5D digital model shadow generation that allows identities of shadowing sources to be efficiently precalculated. For any point on the ground, all sources of shadowing can be identified and are classified as actual or experiential obstructions to sunlight. The article justifies a 2.5D raster approach in the context of modelling of architectural and urban environments that has in recent times shifted from 2D to 3D, and describes in detail the algorithm which builds on precedents for 2.5D raster calculation of shadows. The algorithm is efficient and is applicable at even precinct scale in low-end computing environments. The simplicity of this new technique, and its independence of GPU coding, facilitates its easy use in research, prototyping and civic engagement contexts. Two research software applications are presented with technical details to demonstrate the algorithm’s use for participatory built environment simulation and generative modelling applications. The algorithm and its shadow origin tagging can be applied to many digital workflows in architectural and urban design, including those using big data, artificial intelligence or community participative processes.

Spatial and visual connectivity are important metrics when developing workplace layouts. Calculating those metrics in real time can be difficult, depending on the size of the floor plan being analysed and the resolution of the analyses. This article investigates the possibility of considerably speeding up the outcomes of such computationally intensive simulations by using machine learning to create models capable of identifying the spatial and visual connectivity potential of a space. To that end, we present the entire process of investigating different machine learning models and a pipeline for training them on such task, from the incorporation of a bespoke spatial and visual connectivity analysis engine through a distributed computation pipeline, to the process of synthesizing training data and evaluating the performance of different neural networks.

Shape-changing materials have become increasingly popular among architects in designing responsive systems. One of the greatest challenges of designing with these materials is their dynamic nature, which requires architects to design with the fourth dimension, time. This article presents a study that formalizes the shape-changing behavior of three-dimensional printed wood-based composite materials and the rules that serve to compute their shape-change in response to variations in relative humidity. In this research, we first developed custom three-dimensional printing protocols and analyzed the effects of three-dimensional printing parameters on shape-change. We thereafter three-dimensional printed kirigami geometries to amplify hygroscopic material transformation of wood-based composites.

In recent years, the application of space-frame structures on large-scale freeform designs has significantly increased due to their lightweight configuration and the freedom of design they offer. However, this has introduced a level of complexity into their construction, as doubly curved designs require non-uniform configurations. This article proposes a novel computational workflow that reduces the construction complexity of freeform space-frame structures, by minimizing variability in their joints. Space-frame joints are evaluated according to their geometry and clustered for production in compliance with the tolerance requirements of the selected fabrication process. This provides a direct insight into the level of customization required and the associated construction complexity. A subsequent geometry optimization of the space-frame’s depth minimizes the number of different joint groups required. The variables of the optimization are defined in relation to the structure’s curvature, providing a direct link between the structure’s geometry and the optimization process. Through the application of a control surface, the dimensionality of the design space is drastically reduced, rendering this method applicable to large-scale projects. A case study of an existing structure of complex geometry is presented, and this method achieves a significant reduction in the construction complexity in a robust and computationally efficient way.