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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	caadria2022_199
id	caadria2022_199
authors	Yang, Qing, Cao, Chufan, Li, Haimiao, Qiu, Waishan, Li, Wenjing and Luo, Dan
year	2022
title	Quantifying the Coherence and Divergence of Planned, Visual and Perceived Streets Greening to Inform Ecological Urban Planning
doi	https://doi.org/10.52842/conf.caadria.2022.1.565
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 565-574
summary	This research attempts to combine the fields of urban planning, urban design and cognitive psychology, and propose three corresponding evaluation indicators for urban ecology, and further explore the coherence and divergence between them. This research defines land vegetation coverage, visibility of street green vegetation, and people's green perception as planned green, visual green and perceived green. Specifically, the three measures (i.e., planned, visual and perceived) refer to objectively extracting park lands and canopy areas from land use data, objectively extracting green pixels from street views, and subjectively collected through visual surveys. This study hypothesizes that there could exist large variation between the three measures, which would provide distinct implications for city planners. To test our hypothesis, this study selects Brisbane as the research area, effectively using computer deep learning, data visualization and mathematical statistics methods to achieve an accurate description of the three sets of data, and proposes a comprehensive evaluation of the urban ecological theory system. The results show the credibility and scope of application of the three types of greening, and quantitatively proposed and tested the relevant theories of urban design.
keywords	Urban Green Space, Urban Ecology, Street View Image, Green Perception, Subjective Measure, SDG 3, SDG 11, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_471
id	caadria2022_471
authors	Kim, Taehoon, Hong, Soonmin, Panya, David Stephen, Gu, Hyeongmo, Park, Hyejin, Won, Junghye and Choo, Seungyeon
year	2022
title	Development of Technology for Automatic Extraction of Architectural Plan Wall Lines for Concrete Waste Prediction Using Point Cloud
doi	https://doi.org/10.52842/conf.caadria.2022.2.597
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 597-606
summary	Recently, as more and more projects on residential environment improvement in cities are actively carried out, the cases of demolishing or remodelling buildings has been increasing. Most of the target buildings for such projects are made of concrete. In order to reduce energy use as well as carbon emissions, the amount of concrete used as a building material should be reduced. This is because the concrete is the largest amount of construction waste, which the exact amount of concrete needs to be predicted. The architectural drawings are essential for the estimation and demolition of building waste, but the problem is that most of the old buildings' drawings do not exist. The 3D scanning process was performed to create the plans for such old buildings instead of the conventional method that is long time-consuming and labour-intensive actual measurement. In this study, we scanned 40 old houses that were scheduled to be demolished. The result showed that the 3D scanned drawings' accuracy - 99.2% - was higher than the ones measured by the conventional way. Through the algorithm developed in this study, the various processes of demolition, drawing measurement, and discarding quantity prediction can be solved in one process, thereby reducing work efficiently. And, considering the reliability of the research results, it is possible to reduce the economic loss by predicting the exact amount of waste in advance. After that, if the algorithm, developed in this study, can be further subdivided and supplemented to identify the materials for each part of the old buildings, it will be able to propose an efficient series of processes that distinguish between recyclable materials and wastes and thereby efficiently dispose of them. 0864108000
keywords	Point Cloud, Construction Waste, Parametric Design, Algorithm, Automatic Extraction, SDG 8
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_387
id	caadria2022_387
authors	Robinson, Richard and Park, Hyoung-June
year	2022
title	Learning from Hale: An Educational Augmented Reality Application for an Indigenous Hawaiian Architecture
doi	https://doi.org/10.52842/conf.caadria.2022.1.697
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 697-706
summary	An educational Augmented Reality (AR) application with Head Mount Display (HMD) is developed for the revitalization of the Hales. The proposed application allows a user to have a dynamic learning experience of the Hale by 1) full immersion into an extended reality, 2) enabling the hands-on construction & assembly process with real-time feedback, and 3) visualizing context-specific information and concepts. Through this intact experience, tacit knowledge embedded in the Hawaiian Hale design is delivered. In this paper, the implementation of the proposed application is explained, and the usage of the application is also demonstrated.
keywords	Augmented Reality, Tacit Knowledge, Cultural Heritage, Hale, SDG 4
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_230
id	ecaade2022_230
authors	Sauda, Eric, Karduni, Alireza and Radnia, Noushin
year	2022
title	Architectural User Interface - Synthesizing augmented reality and architecture
doi	https://doi.org/10.52842/conf.ecaade.2022.1.351
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 1, Ghent, 13-16 September 2022, pp. 351–360
summary	Space is the natural setting for augmented reality (AR), suggesting the possibility of an architecture interface. The release of advanced AR devices will introduce new interactive, hybrid spaces infused and organized by information. For computer science, AR is agnostic to its spatial setting, but designing AR and architecture together will allow for exploration of a full range of affordances, feedback mechanism and output/display options. We present design research at Mount Zion Archaeological Park in Jerusalem for a museum and park preserving and explaining the site. Huge amounts of data generated during the excavation are connected to the archaeological record across the region and the world. Our team of architectural & computational designers and faculty from architecture, archeology, and computer science engaged the design of tightly coupled AR interactions and architectural spaces. Our design method allowed designers to visualize and understand simultaneously the design of space and information. We generated 12 designs, using them as the basis for a preliminary set of usability heuristics for an Architecture User Interface.
keywords	Augmented Reality, Interactive Architecture, Design Methods
series	eCAADe
email
last changed	2024/04/22 07:10

_id	cdrf2022_209
id	cdrf2022_209
authors	Yecheng Zhang, Qimin Zhang, Yuxuan Zhao, Yunjie Deng, Feiyang Liu, Hao Zheng
year	2022
title	Artificial Intelligence Prediction of Urban Spatial Risk Factors from an Epidemic Perspective
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_18
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	From the epidemiological perspective, previous research methods of COVID-19 are generally based on classical statistical analysis. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. We take the Spatio-temporal data of people infected with new coronary pneumonia before February 28 in Wuhan in 2020 as the research object. We use kriging spatial interpolation technology and core density estimation technology to establish the epidemic heat distribution on fine grid units. We further examine the distribution of nine main spatial risk factors, including agencies, hospitals, park squares, sports fields, banks, hotels, Etc., which are tested for the significant positive correlation with the heat distribution of the epidemic. The weights of the spatial risk factors are used for training Generative Adversarial Network models, which predict the heat distribution of the outbreak in a given area. According to the trained model, optimizing the relevant environment design in urban areas to control risk factors effectively prevents and manages the epidemic from dispersing. The input image of the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area.
series	cdrf
email
last changed	2024/05/29 14:02

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