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In architectural design optimization, fitness landscapes are used to visualize design space parameters in relation to one or more objective functions for which they are being optimized. In our design study with domain experts, we developed a visual analytics framework for exploring and analyzing fitness landscapes spanning data, projection, and visualization layers. Within the data layer, we employ two surrogate models and three sampling strategies to efficiently generate a wide array of landscapes. On the projection layer, we use star coordinates and UMAP as two alternative methods for obtaining a 2D embedding of the design space. Our interactive user interface can visualize fitness landscapes as a continuous density map or a discrete glyph-based map. We investigate the influence of surrogate models and sampling strategies on the resulting fitness landscapes in a parameter study. Additionally, we present findings from a user study (N = 12), revealing how experts' preferences regarding projection methods and visual representations may be influenced by their level of expertise, characteristics of the techniques, and the specific task at hand. Furthermore, we demonstrate the usability and usefulness of our framework by a case study from the architecture domain, involving one domain expert. [ABSTRACT FROM AUTHOR]

The principal curvature (PC) of a freeform surface, as an important indicator of its fundamental features, is frequently used to guide their rationalization in the field of architectural geometry. The division of a surface using its PC lines into principal strips (PSs) is an innovative way to break down a freeform surface for construction. However, the application of PC networks in architectural design is hindered by the difficulty to generate them and flexibly control their density. This paper introduces a method for PS-based reconstruction of freeform surfaces with different umbilical conditions in the early stages of design. An agent-based modeling approach is developed to find the umbilics and increase the degree of control over the spacing of PC lines. This research can effectively expand the application range of PS-based surface reconstruction methods for freeform architectures. [ABSTRACT FROM AUTHOR]

Fibrous architecture constitutes an alternative approach to conventional building systems and established construction methods. It shows the potential to converge architectural concerns such as spatial expression and structural elegance, with urgently required resource effectiveness and material efficiency, in a genuinely computational approach. Fundamental characteristics of fibre composite are shared with fibre structures in the natural world, enabling the transfer of design principles and providing a vast repertoire of inspiration. Robotic fabrication based on coreless filament winding, a technique to deposit resin impregnated fibre filaments with only minimal formwork, as well as integrative computational design methods are imperative to the development of complex fibrous building systems. Two projects, the BUGA Fibre Pavilion as an example for long-span structures, and Maison Fibre as an example of multi-storey architecture, showcase the application of those techniques in an architectural context and highlight areas of further research opportunities. The highly interrelated aesthetic, structural and fabrication characteristics of fibre nets are difficult to understand and go beyond a designer's comprehension and intuition. An AI powered, self-learning agent system aims to extend and thoroughly explore the design space of fibre structures to unlock the full design potential coreless filament winding offers. In order to ensure feedback between all relevant design and performance criteria and enable interdisciplinary convergence, these novel design methods are embedded in a larger co-design framework. It formalizes the interaction of involved interdisciplinary domains and allows for interactive collaboration based on a central data model, serving as a base for design optimisation and exploration. To further advance research on fibre composites in architecture, bio-based materials are considered, continuing the journey of discovery of fibrous architecture to fundamentally rethinking design and construction towards a novel, computational material culture in architecture. [ABSTRACT FROM AUTHOR]

Concepts of swarm intelligence are becoming increasingly relevant in the field of architectural design. An example is the use of agent-based modeling and simulation methods, which can help manage the complexity of building designs that feature many similar, but geometrically unique elements. Apart from leading to effective solutions and expanding the architectural design space, agent-based design methods can also be employed in integrated planning processes, in which the contributions of various disciplines take place in an integrated loop instead of being executed consecutively. We propose a computational framework for architectural design, in which agents represent building elements and/or joints between building elements. Behavior parameters, behavior weighting, and the environment can be modified in real-time while the agent system is running. Additionally, the designer can interact with individual agents directly, while slowing down or pausing agent movement if so desired. In the resulting design approach, the designer can globally adjust behavior parameters, while retaining local control over details where needed. To facilitate an integrative design process, domain-specific data and the results of external analysis can be included, either directly as input for agent behaviors, or by modifying the environment. We illustrate the potential of this computational framework using the example of the design of plate structures and show how this method can lead to quantifiable results while also attaining aesthetic goals. Furthermore, we provide an outlook toward possible further extensions of agent-based design methods in architecture. [ABSTRACT FROM AUTHOR]