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Voxelization resolution ranges from 5-90 cm enabling an almost accurate representation of the curved geometry. Figure shows voxelization resolution 30/30/30 and 15/15/15 cm.
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Prototypes for automated spatial layout in architecture focus on approaches, which define occupiable space as an orthogonal 2D-grid and use algorithms to allocate each rectangle of the grid to a particular function. However, these approaches are limiting the design to orthog- onal spatial layouts. Based on SAT solving techniques, the prototype pres...
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Citations
... 2. The constraints, conditions, and in particular user's requirements for a project are often ill-defined. 3. The "ideal" solution is the sense of optimization has not been found. ...
... However, in fact it applies to the vast majority of the existing building stock. An approach for layout problems not limited to orthogonal rooms has been investigated in [3,8]. • Main rooms, that it rooms which require daylight constitute chains. ...
... The best FLs are close to the coordinate system's origin. The coordinates of the best three FLs are: (3,1,15), (2,2,16), and (2,3,15). The individual values of the parameters, that is: i. the size of corridors, ii. ...
This chapter describes the method for creating optimal architectural functional layouts. The methodology is based on coarse grid and three general steps: i. generation of layouts satisfying requirements given by the designer, ii. selection of the “proper” layouts, and iii. ranking of the “proper” layouts according to multiple objectives. Presented methodology can be used in architectural practice, urban or graphic design, and wherever the allocation of interrelated shapes is to be optimized. For clarity, simplified examples of a single-story two-apartment residential building are shown. Despite this simplicity, presented layouts resemble realistic functional solutions. One example of a practical-size floor-plan of three apartments of total twenty rooms is generated. The material is organized as follows: the concept of space discretization with coarse grid is introduced; the backtrack (depth-first) search algorithm is implemented for the generation of a number “potentially good” layouts. A machine learning method (feed-forward artificial neural network) is implemented for the classification of “proper” and “improper” layouts based on the “corridor criterion”. Simple examples of dynamic multi-criterial ranking of “proper” layouts are demonstrated.
... Fleming et al. (1992) developed 2D-layout design systems of constraint satisfaction techniques that used an orthogonal 2D grid and used algorith-mic optimization. Bier et al. (2008) created a prototype called FunctionLayouter (FL) to automatically generate 2D layouts of functional objects in 3D-space. Even though algorithmic optimization and robotic architecture is not the focus of this chapter, reviewing these areas links this case study to other relevant research areas. ...
Much like creative knowledge work environments, studio-based design education environments are changing rapidly to include: multidisciplinary teams, information technology, geographically distributed teams, and flexible workspaces. Factors such as, architectural space design, furniture choices, technical infrastructure features, acoustics, socio-cultural norms, and privacy and visibility of wall-sized displays support or hinder workers in creative environments. In this chapter, I describe a case study of a graduate design studio at the School of Design at Carnegie Mellon University. The studio has four connected spaces: individual workspaces, collaborative spaces, a kitchen and social café area, and a distance-learning classroom. In earlier work, researchers evaluated student satisfaction through fieldwork, pre-post occupancy surveys, and interviews. In this chapter, I analyze a design studio environment through time-lapse photography, Space Syntax analysis, and semi-structured interviews. This research identifies locations where people and teams work and the factors that support collaboration, such as space configuration, wall-sized display affordances, furniture configurations, and support infrastructures. Teams worked more often in locations that were less visible from other locations, provided greater laptop screen and display privacy, had whiteboards, and electrical outlets. Students did individual work throughout the studio-suite regardless of the function assigned to the spaces.
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A design support system with a new strategy for finding the optimal functional configurations of rooms for architectural layouts is presented. A set of configurations satisfying given constraints is generated and ranked according to multiple objectives. The method can be applied to problems in architectural practice, urban or graphic design—wherever allocation of related geometrical elements of known shape is optimized. Although the methodology is shown using simplified examples—a single story residential building with two apartments each having two rooms—the results resemble realistic functional layouts. One example of a practical size problem of a layout of three apartments with a total of 20 rooms is demonstrated, where the generated solution can be used as a base for a realistic architectural blueprint. The discretization of design space is discussed, followed by application of a backtrack search algorithm used for generating a set of potentially ‘good’ room configurations. Next the solutions are classified by a machine learning method (FFN) as ‘proper’ or ‘improper’ according to the internal communication criteria. Examples of interactive ranking of the ‘proper’ configurations according to multiple criteria and choosing ‘the best’ ones are presented. The proposed framework is general and universal—the criteria, parameters and weights can be individually defined by a user and the search algorithm can be adjusted to a specific problem.
Architectural programming is the research and decision-making process that identifies the scope of work to be designed. Programming is difficult because it involves identifying, collecting, analyzing and updating information from different sources such as engineers, clients, users, consultants, and others. In this paper I propose a computational model for programming and describe its implementation, a tool called PENA that allows a programming expert to represent different processes and people involved in a project using intelligent agents. By delegating responsibility to agents, a programming expert can better organize and manage project data as well as find creative solutions to conflicting issues through agent negotiation. As a proof-of-concept, I show how an agent, called the Arch-Learner, manages adjacencies of rooms in a simple program for a house by clustering them into public and private rooms. I conclude with a discussion of future work and development of PENA.
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