Satisfactory layout planner
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Generally, these methods include machine learning (ML) and optimization techniques, neural networks (NN) and evolutionary algorithms. In the context of this article, with SOFP we refer to the combination of methods that designers use to automatically generate spatial building layouts. A direct application of this method is represented by self-organizing floor plans (SOFP). Once the design logic has been elaborated, this approach requires the use (or the development) of an algorithm, the implementation of which will allow the final design to be computed. Unlike traditional architects, computational designers need to start their work by modeling the design problem (what is required by the project brief), including all relevant information (from building regulations to dimensional guidelines) and elaborating a logic that allows all parts of the design to be hierarchically related and processed. During this nonlinear process, the designer evaluates each development of the drawings against the prescriptive data (building regulations, health and safety, minimum distances, etc.) and subjective aspects (taste, experience from previous projects, and experimental concerns).Īlthough automation in architecture and design is an integral part of the history of design (think of Sigfried Giedion’s comprehensive accounts of how “Mechanization takes Command”), traditional approaches where projects start with sketches and templates and are developed through continuous refinements, are today challenged by a more linear and data-driven workflow where designers use algorithms to produce their project (cf. Through several procedural stages, this sketch is then developed into spatial arrangements, floor plans, and technical drawings (Plowright, 2014). Usually, designers start the spatial organization of space (a room, a building, an entire city) by encapsulating the main spatial qualities in an initial sketch. More generally, people refer to floor plans today as blueprints, spatial layouts, internal spatial arrangements, and so on. In the context of design, floor plan is an architectural term that indicates the bidimensional spatial arrangement used by designers to determine internal building layouts. Of course, the question is more elaborate that it seems and it is, probably, even less exciting. This article discusses how the space in which we live can be designed by algorithms instead of humans, with designers working out their projects driven by computer logic instead of Euclidean geometry.
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Keywords: self-organizing floor plans, computational design, architecture, machine learning, generative adversarial networks, artificial neural networks The final section of the article provides some general comments considering pitfalls and possible future developments, as well as speculating on the future of this trend. The central part discusses some of the most common techniques with concrete examples, including Neuro Evolution of Augmenting Topologies (NEAT) and Generative Adversarial Networks (GAN).
SATISFACTORY LAYOUT PLANNER SOFTWARE
The first part of this work contextualizes self-organizing floor plans in architecture and computational design, highlighting their importance and potential for designers as well as software developers. This is a relatively new tendency in computational design that reflects a growing interest in advanced generative and optimization models by architects and building engineers. This article introduces and comments on some of the techniques currently used by designers to generate automatic building floor plans and spatial configurations in general, with emphasis on machine learning and neural networks models.