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CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id	cf2015_484
id	cf2015_484
authors	Liao, Kai; Vries, Bauke de; Kong, Jun and Zhang, Kang
year	2015
title	Pattern, cognition and spatial information processing: Representations of the spatial layout of architectural design with spatial-semantic analytics
source	The next city - New technologies and the future of the built environment [16th International Conference CAAD Futures 2015. Sao Paulo, July 8-10, 2015. Electronic Proceedings/ ISBN 978-85-85783-53-2] Sao Paulo, Brazil, July 8-10, 2015, pp. 484.
summary	In this paper, we review and extend the idea of Alexander’s “pattern language”, especially from the viewpoints of complexity theories, information systems, and human-computer interaction, to explore spatial cognition-based design representations for “intelligent and adaptive/interactive environment” in architecture and urban planning. We propose a theoretic framework of design patterns “with spatial information processing”, and attempt to incorporate state-of-the-art computational methods of information visualization/visual analytics into the conventional CAAD approaches. Focused on the spatial-semantic analytics, together with abstract syntactic pattern representation, by using “spatial-semantic aware” graph grammar formalization, i.e., Spatial Graph Grammars (SGG), the relevant models, algorithms and tool are proposed. We testify our theoretic framework and computational tool VEGGIE (a Visual Environment of Graph Grammar Induction Engineering) by using actual architectural design works (spatial layout exemplars of a small office building and the three house projects by Frank Lloyd Wright) as study cases, so as to demonstrate our proposed approach for practical applications. The results are discussed and further research is suggested.
keywords	Pattern language, complex adaptive systems, spatial cognition, design representations, spatial information processing, Artificial Intelligence, visual language, Spatial Graph Grammars (SGG), spatial-semantic analytics.
series	CAAD Futures
email
last changed	2015/06/29 07:55

_id	acadia20_238
id	acadia20_238
authors	Zhang, Hang
year	2020
title	Text-to-Form
doi	https://doi.org/10.52842/conf.acadia.2020.1.238
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 238-247.
summary	Traditionally, architects express their thoughts on the design of 3D architectural forms via perspective renderings and standardized 2D drawings. However, as architectural design is always multidimensional and intricate, it is difficult to make others understand the design intention, concrete form, and even spatial layout through simple language descriptions. Benefiting from the fast development of machine learning, especially natural language processing and convolutional neural networks, this paper proposes a Linguistics-based Architectural Form Generative Model (LAFGM) that could be trained to make 3D architectural form predictions based simply on language input. Several related works exist that focus on learning text-to-image generation, while others have taken a further step by generating simple shapes from the descriptions. However, the text parsing and output of these works still remain either at the 2D stage or confined to a single geometry. On the basis of these works, this paper used both Stanford Scene Graph Parser (Sebastian et al. 2015) and graph convolutional networks (Kipf and Welling 2016) to compile the analytic semantic structure for the input texts, then generated the 3D architectural form expressed by the language descriptions, which is also aided by several optimization algorithms. To a certain extent, the training results approached the 3D form intended in the textual description, not only indicating the tremendous potential of LAFGM from linguistic input to 3D architectural form, but also innovating design expression and communication regarding 3D spatial information.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

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