Cumincad : CUMINCAD Papers : Search Results

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	cf2019_048
id	cf2019_048
authors	Argota Sanchez-Vaquerizo, Javier and Daniel Cardoso Llach
year	2019
title	The Social Life of Small Urban Spaces 2.0 Three Experiments in Computational Urban Studies
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 430
summary	This paper introduces a novel framework for urban analysis that leverages computational techniques, along with established urban research methods, to study how people use urban public space. Through three case studies in different urban locations in Europe and the US, it demonstrates how recent machine learning and computer vision techniques may assist us in producing unprecedently detailed portraits of the relative influence of urban and environmental variables on people’s use of public space. The paper further discusses the potential of this framework to enable empirically-enriched forms of urban and social analysis with applications in urban planning, design, research, and policy.
keywords	Data Analytics, Urban Design, Machine Learning, Artificial Intelligence, Big Data, Space Syntax
series	CAAD Futures
email
last changed	2019/07/29 14:18

_id	cf2019_017
id	cf2019_017
authors	Cardoso Llach, Daniel and Javier Argota Sánchez-Vaquerizo
year	2019
title	An Ecology of Conflicts Using Network Analytics to Explore the Data of Building Design
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 131
summary	The scale and socio-technical complexity of contemporary architectural production poses challenges to researchers and practitioners interested in their description and analysis. This paper discusses the novel use of network analysis techniques to study a dataset comprising thousands of design conflicts reported during design coordination of a large project by a group of architects using BIM software. We discuss in detail three approaches to the use of network analysis techniques on these data, showing their potential to offer topological insights about the phenomenon of contemporary architectural design and construction, which complement other forms of architectural analysis.
keywords	Architecture, Network Analysis, Design Ecology, BIM, Data Visualization
series	CAAD Futures
email
last changed	2019/07/29 14:08

_id	cf2019_016
id	cf2019_016
authors	Cardoso Llach, Daniel and Scott Donaldson
year	2019
title	An Experimental Archaeology of CAD Using Software Reconstruction to Explore the Past and Future of ComputerAided Design
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 130
summary	This paper proposes software reconstruction as a method to shed new light into the material, gestural, and sensual dimensions of computer-aided design technologies. Specifically, it shows how by combining historical research and creative prototyping this method can bring us closer to distant ways of seeing, touching, drawing, and designing—while raising new questions about the impact of CAD technologies on present-day architectural practices. It documents the development of two software reconstructions—of Ivan Sutherland’s “Sketchpad” and of Steven A. Coons’s “Coons Patch”—and reflects on the responses they elicited in the context of two exhibitions. The paper shows how software reconstruction can offer access to overlooked aspects of computer-aided design systems, specially their material and sensual dimensions, and how we may explore its broader potential for research, preservation, pedagogy, and speculative design of design technologies.
keywords	Software Reconstruction, Media Archaeology, CAD, Sketchpad, Steven A. Coons, Ivan Sutherland, Computational Design History
series	CAAD Futures
email
last changed	2019/07/29 14:08

_id	cf2019_014
id	cf2019_014
authors	Ferrando, Cecilia; Niccolo Dalmasso, Jiawei Mai, Daniel Cardoso Llach
year	2019
title	Architectural Distant Reading Using Machine Learning to Identify Typological Traits Across Multiple Buildings
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, pp. 114-127
summary	This paper introduces an approach to architectural “distant reading”: the use of computational methods to analyze architectural data in order to derive spatial insights from—and explore new questions concerning—large collections of architectural work. Through a case study comprising a dataset of religious buildings, we show how we may use machine learning techniques to identify typological and functional traits from building plans. We find that spatial structure, rather than local features, is particularly effective in supporting this type of analysis. Further, we speculate on the potential of this computational method to enrich architectural design, research, and criticism by, for example, enabling new ways of thinking about architectural concepts such as typology in ways that reflect gradual variations, rather than sharp distinctions.
keywords	Architectural Analytics, Machine Learning, Classification, Religious buildings, Space Syntax
series	CAAD Futures
email
last changed	2019/07/29 14:08

_id	ecaadesigradi2019_550
id	ecaadesigradi2019_550
authors	Rhee, Jinmo, Cardoso Llach, Daniel and Krishnamurti, Ramesh
year	2019
title	Context-rich Urban Analysis Using Machine Learning - A case study in Pittsburgh, PA
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 3, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 343-352
doi	https://doi.org/10.52842/conf.ecaade.2019.3.343
summary	This paper reports on the analytical potential of machine learning methods for urban analysis. It documents a new method for data-driven urban analysis based on diagrammatic images describing each building in a city in relation to its immediate urban context. By statistically analyzing architectural and contextual features in this new dataset, the method can identify clusters of similar urban conditions and produce a detailed picture of a city's morphological structure. Remapping the clusters from data to 2D space, our method enables a new kind of urban plan that displays gradients of urban similarity. Taking Pittsburgh as a case study we demonstrate this method, and propose "morphological types" as a new category of urban analysis describing a given city's specific set of distinct morphological conditions. The paper concludes with a discussion of the implications of this method and its limitations, as well as its potentials for architecture, urban studies, and computation.
keywords	Urban Morphology; Machine Learning; Architectural Contexts; Urban Analysis; GIS
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:56

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