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Drawing in design
Published in Bryan Lawson, The Design Student’s Journey, 2018
Diagrams include charts and graphs and other representations of selective data or information. Unlike most other types of drawings here, they do not represent an attempt to portray a possible solution in any physical sense. More often than not, designers use them to represent some aspects of the problem in an attempt to understand it better. In recent times a good deal of attention has been given to the way careful and imaginative graphical design can portray data more meaningfully. Among the most common design diagrams are the ‘bubble diagrams’ used by architects to represent desired flow and adjacencies around a building. One of the very first computer-aided architectural design computer programs was created to make these representations from a matrix of numbers and then to go on and attempt some form of simple layout. It was first demonstrated on a hospital unit as in our illustration here (Figure 4.5). Such a use of computers has unsurprisingly never taken off. The process of making the diagram by hand means you process the information in your brain and this gives a much deeper understanding that can be vital in the design process.
Metadesigning Customizable Houses
Published in Branko Kolarevic, José Pinto Duarte, Mass Customization and Design Democratization, 2018
Constructing a topology of a house isn’t as complicated as it sounds. Early work in graph theory that was carried out in the 1970s by Lionel March, Philip Steadman, and others, captured in William Mitchell’s seminal book, Computer Aided Architectural Design,13 contains the seeds of an appropriate definition of the house’s topology. In The Logic of Architecture, William Mitchell offered examples of shape grammars as a way of defining and structuring the topology of houses.14 In well-defined design worlds,15 shape grammars could indeed provide an appropriate basis for the topological definition of the designs.16
Background to BIM
Published in Jonathan Ingram, Understanding BIM, 2020
The University of Michigan Architectural and Planning Research Laboratory investigated Computer Aided Architectural Design (CAAD) from the early 70s and developed the Computer Aided Engineering and Architectural Design System (CAEADS). This was led by Harold Borkin and supported by the Army Corp of Engineers Construction Research Laboratory and was used to design military facilities. It is an integrated set of automated tools to support data development in conducting studies in the early concept design process. CAEADS programs interface with other standalone programs, such as energy analysis, structural analysis and drafting systems.18
Overview of 3D construction printing and future perspectives: a review of technology, companies and research progression
Published in Architectural Science Review, 2022
Stelladriana Volpe, Valentino Sangiorgio, Francesco Fiorito, Humberto Varum
Scopus has a large coverage of scientific production and a fast-indexing process (Zhao et al. 2019). It is the largest abstract and citation database of peer-reviewed research literature in the fields of science, technology, medicine, social sciences, arts and humanities. It covers over 20,000 peer-reviewed journals (Fahimnia, Sarkis, and Davarzani 2015). Dimension is an inclusive abstract and indexing database that allows users to explore connections between a wide range of research data. By December 2019, Dimensions included more than 106 million publications (“Dimensions” 2022). CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design (“CumInCAD” 2022). CumInCAD constitutes a significant source for research related to computer-aided architectural design, architectural computing, computational design and design technology. For this reason, it was chosen as a source of data pertaining specifically to architecture.
Architecture meets computation: an overview of the evolution of computational design approaches in architecture
Published in Architectural Science Review, 2020
During the 80 s, the number of international conferences greatly increased: in 1981, Mitchell, Eastman, and Yessios founded the north-American Association for Computer-Aided Design in Architecture (ACADIA) conference to discuss the role of computation in Architecture, while encouraging innovation in the architectural design practice (Celani and Veloso 2015). In 1983, the conference Education and Research in Computer Aided Architectural Design in Europe (eCAADe) was first held, introducing education as a new research focus. Established in 1985, the CAADFutures conference embraced all continents aiming at fomenting CAD advancements envisioning the quality of the built environment; the conference Artificial Intelligence in Design (renamed as Design Computing and Cognition in 2004) focused on using Artificial Intelligence techniques in design; and the bi-annual International IBPSA Building Simulation conference aimed at improving the design, construction, operation, and maintenance of both new and existing buildings. In 1989, the International Conference on Computational and Cognitive Models of Creative Design explored the advancement of designers’ understanding of computational and cognitive models of creative design.
Generating Synthetic Space Allocation Probability Layouts Based on Trained Conditional-GANs
Published in Applied Artificial Intelligence, 2019
Morteza Rahbar, Mohammadjavad Mahdavinejad, Mohammadreza Bemanian, Amir Hossein Davaie Markazi, Ludger Hovestadt
Space allocation is a well-known algorithmic problem in computer-aided architectural design. The target of space allocation problem (SAP) is to define an algorithm that could propose a layout (space arrangement) based on topological and geometrical constraints. The topological and geometrical constraints are influenced by different objective and subjective agents. Objective agents are factors that could be defined as numerical objective functions such as architectural program, the energy efficiency of the project, municipality regulations, design standards, client preferences, etc. In opposite, the subjective agents are factors that deal with the mentality of the designer. These factors are more based on the designer’s experience than numerical rules. For instance, the aesthetic aspects of the design or some user’s environmental behaviors are among subjective agents that influence the topological and geometrical constraints.