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	ecaade2017_130
id	ecaade2017_130
authors	Nagakura, Takehiko and Sung, Woongki
year	2017
title	Spatial Typology for BIM - Preassembling for Synthetic Architectural Design
doi	https://doi.org/10.52842/conf.ecaade.2017.1.129
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 1, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 129-136
summary	Contemporary Building Information Modeling (BIM) software provides basic component types such as bathtubs, desks, windows and walls that are available in many varieties of kinds and ready for drag-and-drop into a design project. However, the software is unlikely to provide higher level constructs such as bathrooms or offices as types, and these spatial concepts are largely unframed in the ontology of the building system. This paper looks at these spatial concepts left unframed in BIM as important fabric in the design process, examines how they are represented typologically in conventional design resources such as Neufert Architects' Data, and discusses strategies for embedding them in BIM. Together with abundant published cases of architectural designs, the examples of spatial forms in these resources play a role of Big Data. The paper then demonstrates a prototype of parametric office building typology embedded in BIM and illustrates how such a tool helps an architect to study volumetric layout on a given site. The approach tested leads to an idea of BIM imbued with a massive taxonomic library of preassembled spatial types and takes us a step closer to a symbiotic or synthetic architectural design process.
keywords	Building Information Modeling; Architectural Typology; Design Representation; Big Data; Synthetic Design
series	eCAADe
email	takehiko@mit.edu
last changed	2022/06/07 07:59

_id	acadia17_118
id	acadia17_118
authors	As, Imdat; Nagakura, Takehiko
year	2017
title	Crowdsourcing the Obama Presidential Center: An Alternative Design Delivery Model: Democratizing Architectural Design
doi	https://doi.org/10.52842/conf.acadia.2017.118
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 118-127
summary	In this article, we present crowdsourcing as a design delivery method for publicly funded buildings, and compare it to the traditional Request for Proposals (RFP). We explore the potential of crowdsourcing through the use of an online design competition for the Obama Presidential Center in Chicago, IL, which the authors administered at Arcbazar.com, a crowdsourcing platform. Competition procedures have been applied in architectural practice since antiquity, from the Parthenon and the Hagia Sophia to thousands of seminal buildings around the globe. However, with the advent of digital technologies and outreach to a more interconnected world, crowdsourcing allows even the most mundane design challenges to go through the fair competition protocol. We argue that crowdsourcing can help democratize architectural design acquisition by giving a level playing field to designers, and produce a more just, competitive, and creative design product.
keywords	design methods; information processing; hybrid practices; crowdsourcing
series	ACADIA
email	imdatas@gmail.com
last changed	2022/06/07 07:54

_id	caadria2017_070
id	caadria2017_070
authors	Chen, Nai Chun, Xie, Jenny, Tinn, Phil, Alonso, Luis, Nagakura, Takehiko and Larson, Kent
year	2017
title	Data Mining Tourism Patterns - Call Detail Records as Complementary Tools for Urban Decision Making
doi	https://doi.org/10.52842/conf.caadria.2017.685
source	P. Janssen, P. Loh, A. Raonic, M. A. Schnabel (eds.), Protocols, Flows, and Glitches - Proceedings of the 22nd CAADRIA Conference, Xi'an Jiaotong-Liverpool University, Suzhou, China, 5-8 April 2017, pp. 685-694
summary	In this study we show how Call Detail Record (CDR) can be used to better understand the travel patterns of visitors. We show how Origin-Destination (OD) Interactive Maps can provide transportation information through CDR. We then use aggregation of CDR to show the differences between the travel patterns of visitors from different countries and of different lengths of stay. We also show that visitors move differently during event periods and non-event periods, reflecting the importance of real-time data available by CDR. From CDR, we can gain more detailed and complete information about how tourists move compared to traditional surveys, which can be used to aid smarter transportation systems and urban resource planning.
keywords	Machine Learning; Call Detail Record; Original-Destination Matrix; Urban Design Tool
series	CAADRIA
email	naichun@mit.edu
last changed	2022/06/07 07:55

_id	cf2017_101
id	cf2017_101
authors	Chen, Nai Chun; Zhang, Yan; Stephens, Marrisa; Nagakura, Takehiko; Larson, Kent
year	2017
title	Urban Data Mining with Natural Language Processing: Social Media as Complementary Tool for Urban Decision Making
source	Gülen Çagdas, Mine Özkar, Leman F. Gül and Ethem Gürer (Eds.) Future Trajectories of Computation in Design [17th International Conference, CAAD Futures 2017, Proceedings / ISBN 978-975-561-482-3] Istanbul, Turkey, July 12-14, 2017, pp. 101-109.
summary	The presence of web2.0 and traceable mobile devices creates new opportunities for urban designers to understand cities through an analysis of user-generated data. The emergence of “big data” has resulted in a large amount of information documenting daily events, perceptions, thoughts, and emotions of citizens, all annotated with the location and time that they were recorded. This data presents an unprecedented opportunity to gauge public opinion about the topic of interest. Natural language processing with social media is a novel tool complementary to traditional survey methods. In this paper, we validate these methods using tourism data from Trip-Advisor in Andorra. “Natural language processing” (NLP) detects patterns within written languages, enabling researchers to infer sentiment by parsing sentences from social media. We applied sentiment analysis to reviews of tourist attractions and restaurants. We found that there were distinct geographic regions in Andorra where amenities were reviewed as either uniformly positive or negative. For example, correlating negative reviews of parking availability with land use data revealed a shortage of parking associated with a known traffic congestion issue, validating our methods. We believe that the application of NLP to social media data can be a complementary tool for urban decision making.
keywords	Short Paper, Urban Design Decision Making, Social Media, Natural Language Processing
series	CAAD Futures
email	naichun@mit.edu, ryanz@mit.edu, marissa@mit.edu, takehiko@mit.edu, ekll@mit.edu
last changed	2017/12/01 14:37

_id	acadia17_000
id	acadia17_000
authors	Nagakura, Takehiko; Tibbits, Skylar; Iba?ez, Mariana and Mueller, Caitlin (eds.)
year	2017
title	ACADIA 2017: DISCIPLINES & DISRUPTION
doi	https://doi.org/10.52842/conf.acadia.2017
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), 706 p.
summary	The Proceedings of the ACADIA 2017 conference contains peer reviewed research papers presented at the 37th annual conference of the Association for Computer Aided Design in Architecture. Disciplines & Disruption initiates a dialog about the state of the discipline of architecture and the impact of technology in shaping or disrupting design, methods and cultural fronts. For the past 30 years, distinctive advancements in technologies have delivered unprecedented possibilities to architects and enabled new expressions, performance, materials, fabrication and construction processes. Simultaneously, digital technology has permeated the social fabric around architecture with broad influences ranging from digital preservation to design with the developing world. Driven by technological, data and material advances, architecture now witnesses the moment of disruption, whereby formerly distinct areas of operation become increasingly connected and accessible to architecture's sphere of concerns in ways never before possible. Distinctions between design and making, building and urban scale, architecture and engineering, real and virtual, on site and remote, physical and digital data, professionals and crowds, are diminishing as technology increases the designer's reach far beyond the confines of the drafting board. This conference provides a platform to investigate the shifting landscape of the discipline today, and to help define and navigate the future.
keywords	Computer Aided Design, ACADIA, ACADIA 2017, ACADIA Conference, Architecture
series	ACADIA
email	sjet@mit.edu; takehiko@mit.edu
last changed	2022/06/07 07:49

_id	acadia17_474
id	acadia17_474
authors	Peng, Wenzhe; Zhang, Fan; Nagakura, Takehiko
year	2017
title	Machines’ Perception of Space: Employing 3D Isovist Methods and a Convolutional Neural Network in Architectural Space Classification
doi	https://doi.org/10.52842/conf.acadia.2017.474
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 474- 481
summary	Simple and common architectural elements can be combined to create complex spaces. Different spatial compositions of elements define different spatial boundaries, and each produces a unique local spatial experience to observers inside the space. Therefore an architectural style brings about a distinct spatial experience. While multiple representation methods are practiced in the field of architecture, there lacks a compelling way to capture and identify spatial experiences. Describing an observer’s spatial experiences quantitatively and efficiently is a challenge. In this paper, we propose a method that employs 3D isovist methods and a convolutional neural network (CNN) to achieve recognition of local spatial compositions. The case studies conducted validate that this methodology works well in capturing and identifying local spatial conditions, illustrates the pattern and frequency of their appearance in designs, and indicates peculiar spatial experiences embedded in an architectural style. The case study used small designs by Mies van der Rohe and Aldo van Eyck. The contribution of this paper is threefold. First, it introduces a sampling method based on 3D Isovist that generates a 2D image that can be used to represent a 3D space from a specific observation point. Second, it employs a CNN model to extract features from the sampled images, then classifies their corresponding space. Third, it demonstrates a few case studies where this space classification method is applied to different architectural styles.
keywords	design methods; information processing; AI; machine learning; computer vision; representation
series	ACADIA
email	pwz@mit.edu
last changed	2022/06/07 08:00

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