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	acadia20_170
id	acadia20_170
authors	Li, Peiwen; Zhu, Wenbo
year	2020
title	Clustering and Morphological Analysis of Campus Context
doi	https://doi.org/10.52842/conf.acadia.2020.2.170
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. 170-177.
summary	“Figure-ground” is an indispensable and significant part of urban design and urban morphological research, especially for the study of the university, which exists as a unique product of the city development and also develops with the city. In the past few decades, methods adapted by scholars of analyzing the figure-ground relationship of university campuses have gradually turned from qualitative to quantitative. And with the widespread application of AI technology in various disciplines, emerging research tools such as machine learning/deep learning have also been used in the study of urban morphology. On this basis, this paper reports on a potential application of deep clustering and big-data methods for campus morphological analysis. It documents a new framework for compressing the customized diagrammatic images containing a campus and its surrounding city context into integrated feature vectors via a convolutional autoencoder model, and using the compressed feature vectors for clustering and quantitative analysis of campus morphology.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	sigradi2021_302
id	sigradi2021_302
authors	Bueno, Ernesto, Reis Balsini, André and Verde Zein, Ruth
year	2021
title	Analysis by Algorithmic Modeling of Historiographical Data on Modern and Contemporary Brazilian Architecture
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 737–748
summary	Are historiographic diagrams valid instruments for gauging the main constituent aspects of historiographic documentation of a body of architectural production? The paper aims to discuss the results obtained by algorithmic modeling and three-dimensional visualization of historiographic data. The analysis method proposes a diagrammatic approach to the research object, established from the fundamentals originally described by Zein (2020). The diagrams were created using the algorithmic modeling software Grasshopper, which allowed us to combine a precise recording of data with an original approach to its interpretation. From the data collected, Cartesian coordinates were established for the generation of curves and interpolation surfaces representative of the computed aspects of certain historiographic narratives. With wide application possibilities, the resulting algorithmic diagrams establish a new model for data analysis and visualization, which stands as a consistent alternative to other more commonly used digital bibliometric tools.
keywords	Análise de dados, Big Data, Visualizaçao de dados, Historiografia, Arquitetura moderna brasileira
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	ijac202018103
id	ijac202018103
authors	Kimm, Geoff
year	2020
title	Actual and experiential shadow origin tagging: A 2.5D algorithm for efficient precinct-scale modelling
source	International Journal of Architectural Computing vol. 18 - no. 1, 41-52
summary	This article describes a novel algorithm for built environment 2.5D digital model shadow generation that allows identities of shadowing sources to be efficiently precalculated. For any point on the ground, all sources of shadowing can be identified and are classified as actual or experiential obstructions to sunlight. The article justifies a 2.5D raster approach in the context of modelling of architectural and urban environments that has in recent times shifted from 2D to 3D, and describes in detail the algorithm which builds on precedents for 2.5D raster calculation of shadows. The algorithm is efficient and is applicable at even precinct scale in low-end computing environments. The simplicity of this new technique, and its independence of GPU coding, facilitates its easy use in research, prototyping and civic engagement contexts. Two research software applications are presented with technical details to demonstrate the algorithm’s use for participatory built environment simulation and generative modelling applications. The algorithm and its shadow origin tagging can be applied to many digital workflows in architectural and urban design, including those using big data, artificial intelligence or community participative processes.
keywords	2.5D raster, actual and experiential shadow origins, generative techniques, participatory built environment simulation, reactive scripting for design
series	journal
email
last changed	2020/11/02 13:34

_id	ecaade2023_227
id	ecaade2023_227
authors	Moorhouse, Jon and Freeman, Tim
year	2023
title	Towards a Genome for Zero Carbon Retrofit of UK Housing
doi	https://doi.org/10.52842/conf.ecaade.2023.2.197
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 197–206
summary	The United Kingdom has some of the worst insulated housing stock in Northern Europe. This is in part due to the age of housing in the UK, with over 90% being built before 1990 [McCrone 2017, Piddington 2020]. Moreover, 85% of current UK housing will still be in use in 2050 by which stage their Government are targeting Net Carbon Zero [Eyre 2019]. Domestic energy use accounts for around 25% of UK carbon emissions. The UK will need to retrofit 20 million dwellings in order to meet this target. If this delivery were evenly spread, it would equate to over 2,000 retrofit completions each day. Government-funded initiatives are stimulating the market, with upwards of 60,000 social housing retrofits planned for 2023, but it is clear that a system must be developed to enable the design and implementation of housing-stock improvement at a large scale.This paper charts the 20-year development of a digital approach to the design for low-carbon domestic retrofit by architects Constructive Thinking Studio Limited and thence documents the emergence of a collaborative approach to retrofit patterns on a National scale. The author has led the Research and Development stream of this practice, developing a Building Information Modelling methodology and integrated Energy Modelling techniques to optimise design for housing retrofit [Georgiadou 2019, Ben 2020], and then inform a growing palette of details and a database of validated solutions [Moorhouse 2013] that can grow and be used to predict options for future projects [D’Angelo 2022]. The data is augmented by monitoring energy and environmental performance, enabling a growing body of knowledge that can be aligned with existing big data to simulate the benefits of nationwide stock improvement. The paper outlines incremental case studies and collaborative methods pivotal in developing this work The proposed outcome of the work is a Retrofit Genome that is available at a national level.
keywords	Retrofit, Housing, Zero-Carbon, BIM, Big Data, Design Genome
series	eCAADe
email
last changed	2023/12/10 10:49

_id	caadria2020_354
id	caadria2020_354
authors	Tomarchio, Ludovica, He, Peijun, Herthogs, Pieter and Tuncer, Bige
year	2020
title	Cultural-Smart City: Establishing New Data-informed Practices to Plan Culture in Cities
doi	https://doi.org/10.52842/conf.caadria.2020.2.081
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 81-90
summary	The idea of the Creative City has encouraged planners to develop cultural policies to support creative economies, city branding, urban identity and urban quality. On the other side, the concept of Smart City introduced the possibility to create, collect and analyse data to inform decisions on cities. The two city agendas overlap in different ways, creating a Smart cultural city nexus, that propose similar goals and mixed methodologies, like the possibility to inform planning processes with big data-based technologies. In line with this direction, we introduced conceptual and methodological tools: the first tool is the definition of Hybrid Art Spaces, the second tool is the Singapore Art Maps (SAM), which uses social media data to locate art venues in cities (Tomarchio et al. 2016); the third tool is the Social Media Art Model, which establishes a relationship between social media production and art venues features. While these tools have already shown interesting analytics outcomes (Tomarchio et al. 2016), it is important to validate their utility among practitioners and to set protocols of practices. This paper presents results from semi-structured interviews and a focus group, as a first step towards assessing the usefulness of our three tools for cultural planning practice.
keywords	social media; art; cultural planning; urban planning
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	caadria2025_809
id	caadria2025_809
authors	Chakraborty, Shilpi and Fukuda, Tomohiro
year	2025
title	Bridging Past and Present - Space syntax as a tool for digital heritage: A comprehensive literature review for integrating spatial analysis in heritage conservations
source	Dagmar Reinhardt, Nicolas Rogeau, Christiane M. Herr, Anastasia Globa, Jielin Chen, Taro Narahara (eds.), ARCHITECTURAL INFORMATICS - Proceedings of the 30th CAADRIA Conference, Tokyo, 22-29 March 2025, Volume 4, pp. 325–334
summary	This study examines the integration of space syntax into digital heritage practices to address key challenges in preserving historic urban landscapes. The research focuses on three primary issues: the lack of social and cultural integration, low user engagement, and insufficient interdisciplinary collaboration. The research question explores how space syntax can enhance the preservation of both spatial and cultural characteristics in heritage management. A systematic literature review was conducted across 5,694 documents published between 1983 and 2024, utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology for rigorous data collection. The results reveal a substantial increase in relevant publications, with a 64% rise in the 1990s to 2000s and a 498% increase from 2000 to 2010. Geographic analysis shows significant contributions from Italy (16.6%) and China (13.9%). Keyword and thematic analyses highlight the growing intersection of space syntax with urban heritage preservation and cultural management. Key findings include the ability of space syntax to improve environmental management (r = 0.62, p < 0.01), digital modeling accuracy (85% by 2020), and community. The research advances heritage conservation by integrating space syntax with digital practices, proposing a framework for sustainable urban development with future focus on real-time monitoring and sustainable planning.
keywords	Cultural heritage, Conceptual framework, Design Research Methodology, Sustainable development
series	CAADRIA
email
last changed	2025/04/18 12:27

_id	ecaade2024_4
id	ecaade2024_4
authors	Irodotou, Louiza; Gkatzogiannis, Stefanos; Phocas, Marios C.; Tryfonos, George; Christoforou, Eftychios G.
year	2024
title	Application of a Vertical Effective Crank–Slider Approach in Reconfigurable Buildings through Computer-Aided Algorithmic Modelling
doi	https://doi.org/10.52842/conf.ecaade.2024.1.421
source	Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 421–430
summary	Elementary robotics mechanisms based on the effective crank–slider and four–bar kinematics methods have been applied in the past to develop architectural concepts of reconfigurable structures of planar rigid-bar linkages (Phocas et al., 2020; Phocas et al., 2019). The applications referred to planar structural systems interconnected in parallel to provide reconfigurable buildings with rectangular plan section. In enabling structural reconfigurability attributes within the spatial circular section buildings domain, a vertical setup of the basic crank–slider mechanism is proposed in the current paper. The kinematics mechanism is integrated on a column placed at the middle of an axisymmetric circular shaped spatial linkage structure. The definition of target case shapes of the structure is based on a series of numerical geometric analyses that consider certain architectural and construction criteria (i.e., number of structural members, length, system height, span, erectability etc.), as well as structural objectives (i.e., structural behavior improvement against predominant environmental actions) aiming to meet diverse operational requirements and lightweight construction. Computer-aided algorithmic modelling is used to analyze the system's kinematics, in order to provide a solid foundation and enable rapid adaptation for mechanisms that exhibit controlled reconfigurations. The analysis demonstrates the implementation of digital parametric design tools for the investigation of the kinematics of the system at a preliminary design stage, in avoiding thus time-demanding numerical analysis processes. The design process may further provide enhanced interdisciplinary performance-based design outcomes.
keywords	Reconfigurable Structures, Spatial Linkage Structures, Kinematics, Parametric Associative Design
series	eCAADe
email
last changed	2024/11/17 22:05

_id	ecaade2020_497
id	ecaade2020_497
authors	Kim, Eunsu, Rosenwasser, David and Garcia del Castillo Lopez, Jose Luis
year	2020
title	Urban Emotion - The interrogation of social media and its implications within urban context
doi	https://doi.org/10.52842/conf.ecaade.2020.2.475
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 475-482
summary	This paper presents social media as an analytical tool, helping to transform public policy-making, alongside urban needs by dissecting and evaluating human perception. Using emotion analysis on data gathered from a social media platform, experiments are developed to bring new value to architectural and civic narratives. Emotions from texts collected within social media platforms are extracted and mapped alongside tagged locations to gain a greater understanding of how public spaces are utilized. This project develops a new analytical layer within our built environment, working alongside the urban fabric, mechanical systems, and digital infrastructure. It is offered as an interactive tool for policymakers and designers to glean feedback, creating an informed conversation between citizens and decision-makers. Whereas social media platforms such as Twitter and Yelp have been referenced in past academic contexts, this project moves further by producing quantified emotions, painting a differentiated result from what purely semantic data could deliver.
keywords	Social Media; Mapping; Natural Language Processing
series	eCAADe
email
last changed	2022/06/07 07:52

_id	ecaade2020_018
id	ecaade2020_018
authors	Sato, Gen, Ishizawa, Tsukasa, Iseda, Hajime and Kitahara, Hideo
year	2020
title	Automatic Generation of the Schematic Mechanical System Drawing by Generative Adversarial Network
doi	https://doi.org/10.52842/conf.ecaade.2020.1.403
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 403-410
summary	In the front-loaded project workflow, mechanical, electrical, and plumbing (MEP) design requires precision from the beginning of the design phase. Leveraging insights from as-built drawings during the early design stage can be beneficial to design enhancement. This study proposes a GAN (Generative Adversarial Networks)-based system which populates the fire extinguishing (FE) system onto the architectural drawing image as its input. An algorithm called Pix2Pix with the improved loss function enabled such generation. The algorithm was trained by the dataset, which includes pairs of as-built building plans with and without FE equipment. A novel index termed Piping Coverage Rate was jointly proposed to evaluate the obtained results. The system produces the output within 45 seconds, which is drastically faster than the conventional manual workflow. The system realizes the prompt engineering study learned from past as-built information, which contributes to further the data-driven decision making.
keywords	Generative Adversarial Network; MEP; as-built drawing; automated design; data-driven design
series	eCAADe
email
last changed	2022/06/07 07:57

_id	sigradi2024_104
id	sigradi2024_104
authors	Spiegelhalter, Thomas
year	2024
title	Integrating AI-SynBio-Digital Twin Futures in Coastal Urban Resilience
source	Herrera, Pablo C., Gómez, Paula, Estevez, Alberto T., Torreblanca-Díaz, David A. Biodigital Intelligent Systems - Proceedings of the XXVIII Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2024) - ISBN 978-9915-9635-2-5, iBAG-UIC Barcelona, Spain, 13-15 November 2024, pp. 2361–2372
summary	Our research at the Generative AI-SynBio Infrastructures Design Studio, supported by the National Science Foundation (NSF US), Intelligent Europe, and the EU Belmont/Horizon 2020 programs, proposes a pioneering investigation into applying bio-digital intelligent systems in design. Over six years of research, we have targeted low-lying coastal regions, creating bio-inspired code-driven growth and adaptation scenarios with an open-access integrated decision support system app for scenarios from 2018 to 2100. This research interlaces advanced tools and methodologies, including Generative AI, Machine Learning, and Generative Adversarial Networks, merged with natural and synthetic biology data ecosystems. This amalgamation fosters evolutionary growth design algorithms and techniques pivotal for developing resilient, adaptive, and carbon-positive urban landscapes, specifically addressing challenges like sea-level rise, soil subsidence, hurricane-driven storm surges, and heatwaves in low-lying areas of Miami, Fort Myers-Sanibel Island, USA, and Genoa, Italy.
keywords	Bio-Digital Intelligent Systems, Infrastructural Resilience, Generative Adversarial Networks, Synthetic Biology, Evolutionary Algorithms
series	SIGraDi
email
last changed	2025/07/21 11:50

_id	ecaade2024_60
id	ecaade2024_60
authors	Wan, Zijun; Sun, Shuaibing; Meng, Fanjing; Yan, Yu
year	2024
title	How Augment Reality Support Public Participation in the Urban Design Decision-Making: A ten - year literature review
doi	https://doi.org/10.52842/conf.ecaade.2024.2.455
source	Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 2, pp. 455–464
summary	Emerging applications of AR have demonstrated its powerful visualization capabilities, which is a potential solution to enhance public participation in the urban design process. However, there is still a lack of complete understanding of how AR gets involved in this decision-making process. Therefore, this paper reviews 33 empirical studies relating to the topic through the four steps of “PRISMA”. The results indicate that the quantity and quality of research is increasing yearly. As AR technology progresses, the techniques and research methods used in those studies show a trend toward diversification and customization; this has also led to a shift in the scale of urban design from large and abstract to small and concrete. In terms of content, the topics have gradually changed from “people group” to “technology”, and then to “environment”. Notably, a small number of cases in tangible interaction and multi-user collaboration have emerged from 2020 — areas showing great promise. In terms of user assessments, most studies give positive feedback, but there are currently concerns about problems in poor AR visualizations, privacy risks, and the social inequality caused by technical affordance.
keywords	Augment reality, Urban design and planning, Public participation, Collaborative and participative design, Design decision-making
series	eCAADe
email
last changed	2024/11/17 22:05

_id	ecaade2020_075
id	ecaade2020_075
authors	Yoffe, Hatzav, Plaut, Pnina, Fried, Shaked and J. Grobman, Yasha
year	2020
title	Enriching the Parametric Vocabulary of Urban Landscapes - A framework for computer-aided performance evaluation of sustainable development design models
doi	https://doi.org/10.52842/conf.ecaade.2020.1.047
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 47-56
summary	Three decades past since the adoption of sustainability rating systems (SRS) by the Architecture Engineering and Construction industry (AEC) as standard methods for sustainable development evaluation. Nevertheless, these methods still suffer from a low adoption and implementation rate due to their manual, labor-intensive, expert dependent, and time-demanding process. The partial success of urban development evaluation puts forth the question: Are there faster, more accurate quantitative methods for advancing sustainability evaluation? The paper describes a prototype workflow for evaluating the performance of urban landscape design in a single digital workflow, based on ecological key indicator criteria. Grasshopper and Python parametric platforms were used to translate the criteria into quantitative spatial metrics. This study demonstrates optimized biomass measurement in two urban scales in line with the SITES rating system for landscape development: (XS) site development and (XL) neighborhood scale. The measured biomass density is used as a positive indication of ecosystem services capacity in the development site. The framework's quantitative workflow contributes to additional spatial feedbacks compared to the original numeric-based rating system method. Through these, composition and configuration metrics such as ecological connectivity, edge contrast, and patch shape can be visualized, measured, and compared. The metrics, which indicate performance characteristics of the design, generate new opportunities for data-rich sustainability evaluations of urban landscapes, using a single computer-aided workflow.
keywords	Sustainable development; Urban landscape
series	eCAADe
email
last changed	2022/06/07 07:57

_id	ecaade2020_089
id	ecaade2020_089
authors	Ardic, Sabiha Irem, Kirdar, Gulce and Lima, Angela Barros
year	2020
title	An Exploratory Urban Analysis via Big Data Approach: Eindhoven Case - Measuring popularity based on POIs, accessibility and perceptual quality parameters
doi	https://doi.org/10.52842/conf.ecaade.2020.2.309
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 309-318
summary	The cities are equipped with the data as a result of the individuals' sharings and application usage. This significant amount of data has the potential to reveal relations and support user-centric decision making. The focus of the research is to examine the relational factors of the neighborhoods' popularity by implementing a big data approach to contribute to the problem of urban areas' degradation. This paper presents an exploratory urban analysis for Eindhoven at the neighborhood level by considering variables of popularity: density and diversity of points of interest (POI), accessibility, and perceptual qualities. The multi-sourced data are composed of geotagged photos, the location and types of POIs, travel time data, and survey data. These different datasets are evaluated using BBN (Bayesian Belief Network) to understand the relationships between the parameters. The results showed a positive and relatively high connection between popularity - population change, accessibility by walk - density of POIs, and the feeling of safety - social cohesion. For further studies, this approach can contribute to the decision-making process in urban development, specifically in real estate and tourism development decisions to evaluate the land prices or the hot-spot touristic places.
keywords	big data approach; neighborhood analysis; popularity; point of interest (POI); accessibility; perceptual quality
series	eCAADe
email
last changed	2022/06/07 07:54

_id	caadria2020_012
id	caadria2020_012
authors	Chatzi, Anna-Maria and Wesseler, Lisa-Marie
year	2020
title	OGOS+ - A Tool to Visualize Densification potential
doi	https://doi.org/10.52842/conf.caadria.2020.1.773
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 773-782
summary	OGOS+ is a GIS data-based tool, which would offer urban planners, architects, and researchers visualisations of potential building mass in the form of 3D models. It compares the height of existing buildings to the maximum permitted height by German zoning law and calculates the potential building mass. To ensure minimum building footprints it only calculates the densification potential on top of existing buildings. It summarises information of the building potential for future utilisation. The goal is an increase of urban density achieved with micro interventions.
keywords	Urban densification; City Information Modeling and GIS; Big Data and Analytics in Architecture
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	caadria2020_223
id	caadria2020_223
authors	Guo, Qi and Mei, Hongyuan
year	2020
title	Research on Spatial Distribution and Performance Evaluation of Mass Sports Facilities Based on Big Data of Social Media - A Case Study of Harbin
doi	https://doi.org/10.52842/conf.caadria.2020.1.537
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 537-546
summary	The extensive application of Python script provides a new opportunity for the research on spatial distribution of mass sports facilities. The traditional way to obtain geography information of POI is by the crawler of API open platform, which needs accurate search content. Therefore, it is difficult to obtain the geography information of the mass sports facilities, which do not have specific category name. The paper took Harbin City in China as an example, combined the social network address text crawler and map websites crawler, accurately obtained the geographic information of mass sports facilities, and used ArcGIS to realize the visualization of the spatial distribution information. Combined with the information of Harbin population distribution, the paper evaluated the quantity spatial distribution and type spatial distribution of mass sports facilities by Lorentz curve and Global Moran's I, aiming to evaluate the health service performance of existing mass sports facilities and provide reference for the design and planning of sports facilities. The paper draws the conclusion that the distribution of mass sports buildings in Harbin is relatively average with the population distribution and the clustering of sports function types of mass sports buildings is obvious.
keywords	mass sports facilities; spatial distribution; crawler; Lorentz curve; Global Moranâ€™s I
series	CAADRIA
email
last changed	2022/06/07 07:49

_id	ecaade2020_113
id	ecaade2020_113
authors	Li, Yunqin, Yabuki, Nobuyoshi, Fukuda, Tomohiro and Zhang, Jiaxin
year	2020
title	A big data evaluation of urban street walkability using deep learning and environmental sensors - a case study around Osaka University Suita campus
doi	https://doi.org/10.52842/conf.ecaade.2020.2.319
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 319-328
summary	Although it is widely known that the walkability of urban street plays a vital role in promoting street quality and public health, there is still no consensus on how to measure it quantitatively and comprehensively. Recent emerging deep learning and sensor network has revealed the possibility to overcome the previous limit, thus bringing forward a research paradigm shift. Taking this advantage, this study explores a new approach for urban street walkability measurement. In the experimental study, we capture Street View Picture, traffic flow data, and environmental sensor data covering streets within Osaka University and conduct both physical and perceived walkability evaluation. The result indicates that the street walkability of the campus is significantly higher than that of municipal, and the streets close to large service facilities have better walkability, while others receive lower scores. The difference between physical and perceived walkability indicates the feasibility and limitation of the auto-calculation method.
keywords	walkability; WalkScore; deep learning; Street view picture; environmental sensor
series	eCAADe
email
last changed	2022/06/07 07:51

_id	acadia20_160
id	acadia20_160
authors	Sun, Yunjuan; Jiang, Lei; Zheng, Hao
year	2020
title	A Machine Learning Method of Predicting Behavior Vitality Using Open Source Data
doi	https://doi.org/10.52842/conf.acadia.2020.2.160
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. 160-168.
summary	The growing popularity of machine learning has provided new opportunities to predict certain behaviors precisely by utilizing big data. In this research, we use an image-based neural network to explore the relationship between the built environment and the activity of bicyclists in that environment. The generative model can produce heat maps that can be used to predict quantitatively the cycling and running activity in a given area, and then use urban design to enhance urban vitality in that area. In the machine learning model, the input image is a plan view of the built environment, and the output image is a heat map showing certain activities in the corresponding area. After it is trained, the model yields output (the predicted heat map) at an acceptable level of accuracy. The heat map shows the levels and conditions of the subject activity in different sections of the built environment. Thus, the predicted results can help identify where regional vitality can be improved. Using this method, designers can not only predict the behavioral heat distribution but also examine the different interactions between behaviors and aspects of the environment. The extent to which factors might influence behaviors is also studied by generating a heat map of the modified plan. In addition to the potential applications of this approach, its limitations and areas for improvement are also proposed.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ijac202018304
id	ijac202018304
authors	Aagaard, Anders Kruse and Niels Martin Larsen
year	2020
title	Developing a fabrication workflow for irregular sawlogs
source	International Journal of Architectural Computing vol. 18 - no. 3, 270-283
summary	In this article, we suggest using contemporary manufacturing technologies to integrate material properties with architectural design tools, revealing new possibilities for the use of wood in architecture. Through an investigative approach, material capacities and fabrication methods are explored and combined towards establishing new workflows and architectural expressions, where material, fabrication and result are closely interlinked. The experimentation revolves around discarded, crooked oak logs, doomed to be used as firewood due to their irregularity. This project treats their diverging shapes differently by offering unique processing to each log informed by its particularities. We suggest here a way to use the natural forms and properties of sawlogs to generate new structures and spatial conditions. In this article, we discuss the scope of this approach and provide an example of a workflow for handling the discrete shapes of natural sawlogs in a system that involve the collection of material, scanning/digitisation, handling of a stockpile, computer analysis, design and robotic manufacturing. The creation of this specific method comes from a combination of investigation of wood as a material, review of existing research in the field, studies of the production lines in the current wood industry and experimentation through our in-house laboratory facilities. As such, the workflow features several solutions for handling the complex and different shapes and data of natural wood logs in a highly digitised machining and fabrication environment. This up-cycling of discarded wood supply establishes a non-standard workflow that utilises non-standard material stock and leads to a critical articulation of today’s linear material economy. The project becomes part of an ambition to reach sustainable development goals and technological innovation in global and resource-intensive architecture and building industry.
keywords	Natural wood, robotic fabrication, computation, fabrication, research by design
series	journal
email
last changed	2020/11/02 13:34

_id	acadia20_456
id	acadia20_456
authors	Alali, Jiries; Negar Kalantar, Dr.; Borhani, Alireza
year	2020
title	Casting on a Dump
doi	https://doi.org/10.52842/conf.acadia.2020.1.456
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. 456-463.
summary	“Casting on a dump” focuses on finding accessible, low-tech fabrication methodologies that allow for the construction of parametrically designed nonstandard modular cast panels. Such an approach adopts a computational design framework using a single low-tech and low-energy fabrication device to create nonrepetitive volumetric panels cast in situ. The design input for these panels is derived from design preferences and environmental control data. The technique expands upon easy to fabricate and cast methods, targeting less-developed logistical settings worldwide, and thus responding to imminent needs related to climate, available resources, and the economy.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_228
id	acadia20_228
authors	Alawadhi, Mohammad; Yan, Wei
year	2020
title	BIM Hyperreality
doi	https://doi.org/10.52842/conf.acadia.2020.1.228
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. 228-236.
summary	Deep learning is expected to offer new opportunities and a new paradigm for the field of architecture. One such opportunity is teaching neural networks to visually understand architectural elements from the built environment. However, the availability of large training datasets is one of the biggest limitations of neural networks. Also, the vast majority of training data for visual recognition tasks is annotated by humans. In order to resolve this bottleneck, we present a concept of a hybrid system—using both building information modeling (BIM) and hyperrealistic (photorealistic) rendering—to synthesize datasets for training a neural network for building object recognition in photos. For generating our training dataset, BIMrAI, we used an existing BIM model and a corresponding photorealistically rendered model of the same building. We created methods for using renderings to train a deep learning model, trained a generative adversarial network (GAN) model using these methods, and tested the output model on real-world photos. For the specific case study presented in this paper, our results show that a neural network trained with synthetic data (i.e., photorealistic renderings and BIM-based semantic labels) can be used to identify building objects from photos without using photos in the training data. Future work can enhance the presented methods using available BIM models and renderings for more generalized mapping and description of photographed built environments.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

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