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	ecaade2020_193
id	ecaade2020_193
authors	Alymani, Abdulrahman, Jabi, Wassim and Corcoran, Padraig
year	2020
title	Machine Learning Methods for Clustering Architectural Precedents - Classifying the relationship between building and ground
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. 643-652
doi	https://doi.org/10.52842/conf.ecaade.2020.1.643
summary	Every time an object is built, it creates a relationship with the ground. Architects have a full responsibility to design the building by taking the ground into consideration. In the field of architecture, using data mining to identify any unusual patterns or emergent architectural trends is a nascent area that has yet to be fully explored. Clustering techniques are an essential tool in this process for organising large datasets. In this paper, we propose a novel proof-of-concept workflow that enables a machine learning computer system to cluster aspects of an architect's building design style with respect to how the buildings in question relate to the ground. The experimental workflow in this paper consists of two stages. In the first stage, we use a database system to collect, organise and store several significant architectural precedents. The second stage examines the most well-known unsupervised learning algorithm clustering techniques which are: K-Means, K-Modes and Gaussian Mixture Models. Our experiments demonstrated that the K-means clustering algorithm method achieves a level of accuracy that is higher than other clustering methods. This research points to the potential of AI in helping designers identify the typological and topological characteristics of architectural solutions and place them within the most relevant architectural canons
keywords	Machine Learning; Building and Ground Relationship; Clustering Algorithms; K-means cluster Algorithms
series	eCAADe
email
last changed	2022/06/07 07:54

_id	cdrf2019_199
id	cdrf2019_199
authors	Ana Herruzo and Nikita Pashenkov
year	2020
title	Collection to Creation: Playfully Interpreting the Classics with Contemporary Tools
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_19
summary	This paper details an experimental project developed in an academic and pedagogical environment, aiming to bring together visual arts and computer science coursework in the creation of an interactive installation for a live event at The J. Paul Getty Museum. The result incorporates interactive visuals based on the user’s movements and facial expressions, accompanied by synthetic texts generated using machine learning algorithms trained on the museum’s art collection. Special focus is paid to how advances in computing such as Deep Learning and Natural Language Processing can contribute to deeper engagement with users and add new layers of interactivity.
series	cdrf
email
last changed	2022/09/29 07:51

_id	acadia20_708
id	acadia20_708
authors	Charbel, Hadin; López Lobato, Déborah
year	2020
title	Between Signal and Noise
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. 708-718.
doi	https://doi.org/10.52842/conf.acadia.2020.1.708
summary	Climate change continues to have noticeable and accelerated impacts on various territories. Previously predictable and recognizable patterns used by humans and nonhumans alike are perpetually being altered, turning localized signals into noise and effectively disrupting indigenous modes of life. While the use of certain technologies such as data collection, machine learning, and automation can render these otherwise patternless information streams into intelligible content, they are generally associated as being “territorializing,” as an increase in resolution generally lends itself to control, exploitation, and colonization. Contrarily, indigenous groups with long-lasting relationships that have evolved over time have distinct ways of reading and engaging with their contexts, developing sustainable practices that, while effective, are often overlooked as being compatible with contemporary tools. This paper examines how the use of traditionally territorializing technologies can be paired with indigenous knowledge and protocols in order to operate between signal and noise, rendering perverse changes in the landscape comprehensible while also presenting their applications as a facet for sociopolitical, cultural, and ecological adaptation. A methodology defined as “decoding” and “recoding” presents four distinct case studies in the Arctic, addressing various scales and targets with the aim of disrupting current trends in order to grant and/or retain autonomy through what can be read as a form of preservation via augmented adaptation.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_272
id	acadia20_272
authors	del Campo, Matias; Carlson, Alexandra; Manninger, Sandra
year	2020
title	How Machines Learn to Plan
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. 272-281.
doi	https://doi.org/10.52842/conf.acadia.2020.1.272
summary	This paper strives to interrogate the abilities of machine vision techniques based on a family of deep neural networks, called generative adversarial neural networks (GANs), to devise alternative planning solutions. The basis for these processes is a large database of existing planning solutions. For the experimental setup of this paper, these plans were divided into two separate learning classes: Modern and Baroque. The proposed algorithmic technique leverages the large amount of structural and symbolic information that is inherent to the design of planning solutions throughout history to generate novel unseen plans. In this area of inquiry, aspects of culture such as creativity, agency, and authorship are discussed, as neural networks can conceive solutions currently alien to designers. These can range from alien morphologies to advanced programmatic solutions. This paper is primarily interested in interrogating the second existing but uncharted territory.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_320
id	acadia20_320
authors	Fang, Zhihao; Wu, Yuning; Hassonjee, Ammar; Bidgoli, Ardavan; Cardoso-Llach PhD, Daniel
year	2020
title	Towards a Distributed, Robotically Assisted Construction Framework
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. 320-329.
doi	https://doi.org/10.52842/conf.acadia.2020.1.320
summary	In this paper we document progress towards an architectural framework for adaptive and distributed robotically assisted construction. Drawing from state-of-the-art reinforcement learning techniques, our framework allows for a variable number of robots to adaptively execute simple construction tasks. The paper describes the framework, demonstrates its potential through simulations of pick-and-place and spray-coating construction tasks conducted by a fleet of drones, and outlines a proof-of-concept experiment. With these elements the paper contributes to current research in architectural and construction robotics, particularly to efforts towards more adaptive and hybrid human-machine construction ecosystems. The code is available at: https://github.com/c0deLab/RAiC
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_658
id	acadia20_658
authors	Ho, Brian
year	2020
title	Making a New City Image
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. 658-667.
doi	https://doi.org/10.52842/conf.acadia.2020.1.658
summary	This paper explores the application of computer vision and machine learning to streetlevel imagery of cities, reevaluating past theory linking urban form to human perception. This paper further proposes a new method for design based on the resulting model, where a designer can identify areas of a city tied to certain perceptual qualities and generate speculative street scenes optimized for their predicted saliency on labels of human experience. This work extends Kevin Lynch’s Image of the City with deep learning: training an image classification model to recognize Lynch’s five elements of the city image, using Lynch’s original photographs and diagrams of Boston to construct labeled training data alongside new imagery of the same locations. This new city image revitalizes past attempts to quantify the human perception of urban form and improve urban design. A designer can search and map the data set to understand spatial opportunities and predict the quality of imagined designs through a dynamic process of collage, model inference, and adaptation. Within a larger practice of design, this work suggests that the curation of archival records, computer science techniques, and theoretical principles of urbanism might be integrated into a single craft. With a new city image, designers might “see” at the scale of the city, as well as focus on the texture, color, and details of urban life.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2022_161
id	ecaade2022_161
authors	Kharbanda, Kritika, Papadopoulou, Iliana, Pouliou, Panagiota, Daw, Karim, Belwadi, Anirudh and Loganathan, Hariprasath
year	2022
title	LearnCarbon - A tool for machine learning prediction of global warming potential from abstract designs
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 2, Ghent, 13-16 September 2022, pp. 601–610
doi	https://doi.org/10.52842/conf.ecaade.2022.2.601
summary	The new construction that is projected to take place between 2020 and 2040 plays a critical role in embodied carbon emissions. The change in material selection is inversely proportional to the budget, as the project progresses. Given the fact that early-stage design processes often do not include environmental performance metrics, there is an opportunity to investigate a toolset that enables early-stage design processes to integrate this type of analysis into the preferred workflow of concept designers. The value here is that early-stage environmental feedback can inform the crucial decisions that are made in the beginning, giving a greater chance for a building with better environmental performance in terms of its life cycle. This paper presents the development of a tool called LearnCarbon, as a plugin of Rhino3d, used to educate architects and engineers in the early stages about the environmental impact of their design. It facilitates two neural networks trained with the Embodied Carbon Benchmark Study by Carbon Leadership Forum, which learn the relationship between building geometry, typology, and structure with the Global Warming potential in tCO2e. The first one, a regression model, is able to predict the GWP based on the massing model of a building, along with information about typology and location. The second one, a classification model, predicts the construction type given a massing model and target GWP. LearnCarbon can help improve the building life cycle impact significantly, through early predictions of the structure’s material, and can be used as a tool for facilitating sustainable discussions between the architect and the client.
keywords	Machine Learning, Carbon Emissions, LCA, Rhino Plug-in
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2020_163
id	caadria2020_163
authors	Koh, Immanuel
year	2020
title	The Augmented Museum - A Machinic Experience with Deep Learning
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. 639-648
doi	https://doi.org/10.52842/conf.caadria.2020.2.639
summary	Today we witness a shift in the role with which museum used to play -- from one that was simply a spatial container filled with physical artworks on display, to one that is now layered with the digital/online version of the artworks themselves. Deep learning algorithms have become an important means to process such large datasets of digital artworks in providing an alternative curatorial practice (biased/unbiased), and consequentially, augmenting the navigation of the museum's physical spaces. In collaboration with a selection of museums, a series of web/mobile applications have been made to investigate the potential of such machinic inference, as well as interference of the physical experience.
keywords	Machine Learning; Deep Learning; Experience Design; Artificial Intelligence
series	CAADRIA
email
last changed	2022/06/07 07:51

_id	acadia20_170
id	acadia20_170
authors	Li, Peiwen; Zhu, Wenbo
year	2020
title	Clustering and Morphological Analysis of Campus Context
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.
doi	https://doi.org/10.52842/conf.acadia.2020.2.170
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	ecaade2020_130
id	ecaade2020_130
authors	Markusiewicz, Jacek and Gortazar Balerdi, Ander
year	2020
title	LOTI - Using Machine Learning to simulate subjective opinions in design.
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. 439-448
doi	https://doi.org/10.52842/conf.ecaade.2020.1.439
summary	The objective of the workshop described in the article was to redesign a chair called Loti. In a subjective opinion shared by the authors and the participants of the workshop, the chair seems plagiarism of a famous chair by Ray and Charles Eames. The authors centralised the workshop on the use of computational tools for assessing subjective opinions. The authors and the participants created a method for detecting plagiarism and implemented it in the process of design. They created a parametric model of the chair that allowed changing the chair's components with variables. Using this model, the participants generated multiple variations and surveyed other students to assess which of the versions seemed plagiarism. With the information obtained from the survey, we trained a neural network to relate the variables with the level of plagiarism. We linked the parametric model with the neural network to create a tool that informs the user about the probability of committing plagiarism in real-time. The participants used the tool for designing new chairs to evaluate the efficiency of the method.
keywords	parametric design; machine learning; interfaces
series	eCAADe
email
last changed	2022/06/07 07:59

_id	acadia20_178
id	acadia20_178
authors	Meeran, Ahmed; Conrad Joyce, Sam
year	2020
title	Machine Learning for Comparative Urban Planning at Scale: An Aviation Case Study
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. 178-187.
doi	https://doi.org/10.52842/conf.acadia.2020.1.178
summary	Aviation is in flux, experiencing 5.4% yearly growth over the last two decades. However, with COVID-19 aviation was hard hit. This, along with its contribution to global warming, has led to louder calls to limit its use. This situation emphasizes how urban planners and technologists could contribute to understanding and responding to this change. This paper explores a novel workflow of performing image-based machine learning (ML) on satellite images of over 1,000 world airports that were algorithmically collated using European Space Agency Sentinel2 API. From these, the top 350 United States airports were analyzed with land use parameters extracted around the airport using computer vision, which were mapped against their passenger footfall numbers. The results demonstrate a scalable approach to identify how easy and beneficial it would be for certain airports to expand or contract and how this would impact the surrounding urban environment in terms of pollution and congestion. The generic nature of this workflow makes it possible to potentially extend this method to any large infrastructure and compare and analyze specific features across a large number of images while being able to understand the same feature through time. This is critical in answering key typology-based urban design challenges at a higher level and without needing to perform on-ground studies, which could be expensive and time-consuming.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_130
id	acadia20_130
authors	Newton, David
year	2020
title	Anxious Landscapes
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. 130-137.
doi	https://doi.org/10.52842/conf.acadia.2020.2.130
summary	Advances in the field of machine learning over the last decade have revolutionized artificial intelligence by providing a flexible means to build analytic, predictive, and generative models from large datasets, but the allied design disciplines have yet to apply these tools at the urban level to draw analytic insights on how the built environment might impact human health. Previous research has found numerous correlations between the built environment and both physical and mental health outcomes—suggesting that the design of our cities may have significant impacts on human health. Developing methods of analysis that can provide insight on the correlations between the built environment and human health could help the allied design disciplines shape our cities in ways that promote human health. This research addresses these issues and contributes knowledge on the use of deep learning (DL) methods for urban analysis and mental health, specifically anxiety. Mental health disorders, such as anxiety, have been estimated to account for the largest proportion of global disease burden. The methods presented allow architects, planners, and urban designers to make use of large remote-sensing datasets (e.g., satellite and aerial images) for design workflows involving analysis and generative design tasks. The research also contributes insight on correlations between anxiety prevalence and specific urban design features—providing actionable intelligence for the planning and design of the urban fabric.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ijac202018404
id	ijac202018404
authors	Paul Nicholas, Gabriella Rossi, Ella Williams, Michael Bennett and Tim Schork
year	2020
title	Integrating real-time multi-resolution scanning and machine learning for Conformal Robotic 3D Printing in Architecture
source	International Journal of Architectural Computing vol. 18 - no. 4, 371–384
summary	Robotic 3D printing applications are rapidly growing in architecture, where they enable the introduction of new materials and bespoke geometries. However, current approaches remain limited to printing on top of a flat build bed. This limits robotic 3D printing’s impact as a sustainable technology: opportunities to customize or enhance existing elements, or to utilize complex material behaviour are missed. This paper addresses the potentials of conformal 3D printing and presents a novel and robust workflow for printing onto unknown and arbitrarily shaped 3D substrates. The workflow combines dual-resolution Robotic Scanning, Neural Network prediction and printing of PETG plastic. This integrated approach offers the advantage of responding directly to unknown geometries through automated performance design customization. This paper firstly contextualizes the work within the current state of the art of conformal printing. We then describe our methodology and the design experiment we have used to test it. We lastly describe the key findings, potentials and limitations of the work, as well as the next steps in this research.
keywords	Conformal printing, robotic fabrication, 3D scanning, neural networks, industry 4.0
series	journal
email
last changed	2021/06/03 23:29

_id	acadia20_000
id	acadia20_000
authors	Slocum, Brian; Ago, Viola; Doyle, Shelby; Marcus, Adam; Yablonina, Maria; del Campo, Matias (eds.)
year	2020
title	ACADIA 2020: Distributed Proximities
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. 747 p.
doi	https://doi.org/10.52842/conf.acadia.2020.1
summary	This year’s conference included panels dedicated to the discussion of Ecology and Ethics, Data and Bias, Automation and Agency, Culture and Access, and Labor and Practice, followed by a closing discussion on Speculation and Critique. Conceived as a series of conversations, these are intended to encourage a different type of critical, issues-focused discourse as well as the contextualization of the community’s production within that discourse. The work published here foregrounds these themes while interweaving them with the presentation of the computational design expertise of the ACADIA community, with topics including architectures of care, augmented construction, robotics, programmable matter, biological interactions, machine learning, and disrupted practices, among many others, and panoramas spanning from the nano to the urban. At a time of profound disruption brought about by the global pandemic and coinciding with important sociopolitical events, Distributed Proximities seeks to provide a platform for the continuity of technical discourse while amplifying the space for a dialogue that also recognizes the impacts of the social in all aspects of the research.
series	ACADIA
last changed	2023/10/22 12:06

_id	acadia21_76
id	acadia21_76
authors	Smith, Rebecca
year	2021
title	Passive Listening and Evidence Collection
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 76-81.
doi	https://doi.org/10.52842/conf.acadia.2021.076
summary	In this paper, I present the commercial, urban-scale gunshot detection system ShotSpotter in contrast with a range of ecological sensing examples which monitor animal vocalizations. Gunshot detection sensors are used to alert law enforcement that a gunshot has occurred and to collect evidence. They are intertwined with processes of criminalization, in which the individual, rather than the collective, is targeted for punishment. Ecological sensors are used as a “passive” practice of information gathering which seeks to understand the health of a given ecosystem through monitoring population demographics, and to document the collective harms of anthropogenic change (Stowell and Sueur 2020). In both examples, the ability of sensing infrastructures to “join up and speed up” (Gabrys 2019, 1) is increasing with the use of machine learning to identify patterns and objects: a new form of expertise through which the differential agendas of these systems are implemented and made visible. I trace the differential agendas of these systems as they manifest through varied components: the spatial distribution of hardware in the existing urban environment and / or landscape; the software and other informational processes that organize and translate the data; the visualization of acoustical sensing data; the commercial factors surrounding the production of material components; and the apps, platforms, and other forms of media through which information is made available to different stakeholders. I take an interpretive and qualitative approach to the analysis of these systems as cultural artifacts (Winner 1980), to demonstrate how the political and social stakes of the technology are embedded throughout them.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_282
id	acadia20_282
authors	Steinfeld, Kyle
year	2020
title	Drawn, Together
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. 282-289.
doi	https://doi.org/10.52842/conf.acadia.2020.1.282
summary	Changes in the media through which design proceeds are often associated with the emergence of novel design practices and new subjectivities. While the dynamic between design tools and design practices is complex and nondeterministic, there are moments when rapid development in one of these areas catalyzes changes in the other. The nascent integration of machine learning (ML) processes into computer-aided design suggests that we are in just such a moment. It is in this context that an undergraduate research studio was conducted at UC Berkeley in the spring of 2020. By introducing novice students to a set of experimental tools (Steinfeld 2020) and processes based on ML techniques, this studio seeks to uncover those original practices or new subjectivities that might thereby arise. We describe here a series of small design projects that examine the applicability of such tools to early-stage architectural design. Specifically, we document the integration of several conditional text-generation models and conditional image-generation models into undergraduate architectural design pedagogy, and evaluate their use as “creative provocateurs” at the start of a design. After surveying the resulting student work and documenting the studio experience, we conclude that the approach taken here suggests promising new modalities of design authorship, and we offer reflections that may serve as a useful guide for the more widespread adoption of machine-augmented design tools in architectural practice.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_240
id	caadria2020_240
authors	Stojanovic, Djordje and Vujovic, Milica
year	2020
title	How to Share a Home - Towards Predictive Analysis for Innovative Housing Solutions
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. 547-556
doi	https://doi.org/10.52842/conf.caadria.2020.1.547
summary	Renewed interest in cohousing solutions is driven by the rapid population growth and a lack of affordable housing in many cities across the world. The home share has become more prevalent in recent years due to the cost benefits and social gains it provides. While it involves challenges primarily concerned with the usage of communal areas, the viability of this housing model increases with the advancement of technology enabling new tools for analysis and optimisation of spatial usage. This paper introduces a method of sensor application in the occupancy analysis to provide grounding for future studies and the implementation of advanced computational methods. The study focuses on the underexplored potential of the communal spaces and provides a method for the measuring of specific aspects of their usage. The study applies principles of mathematical set theory, to give a more conclusive understanding of how communal areas are used, and therefore contributes to the improvement of housing design. Presented outcomes include an algorithmic chart and a blueprint of a behavioural model.
keywords	Cohousing; Housing share; Post Occupancy Evaluation; Machine Learning ; Predictive Analysis
series	CAADRIA
email
last changed	2022/06/07 07:56

_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
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.
doi	https://doi.org/10.52842/conf.acadia.2020.2.160
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	acadia20_001
id	acadia20_001
authors	Yablonina, Maria; Marcus, Adam; Doyle, Shelby; del Campo, Matias; Ago, Viola; Slocum, Brian (eds.)
year	2020
title	ACADIA 2020: Distributed Proximities (Volume II: Projects)
source	ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. 337 p.
doi	https://doi.org/10.52842/conf.acadia.2020.2
summary	Volume II of the ACADIA 2020 Conference Proceedings contains the collection of Peer-Reviewed and Curated Projects presented during this year’s conference exhibition as well as essays from the winners of this year’s ACADIA Awards of Excellence. This volume also includes submissions from two new formats for this year’s conference—Videos and Field Notes—output from the conference’s fifteen Workshops, and documentation of the “Architects and PPE (Personal Protective Equipment)” panel held during the ACADIA 2020 conference. The circumstances of 2020 provided an opportunity to reflect upon practices and priorities. This work highlights diverse, ad hoc adaptations—academia fragmented, distributed research, bottom-up fabrication—that demonstrate the resilience and ingenuity of the computational design community in the face of crisis. The work published here foregrounds these themes while interweaving them with the presentation of the computational design expertise of the ACADIA community, with topics including architectures of care, augmented construction, robotics, programmable matter, biological interactions, machine learning, and disrupted practices, among many others, and panoramas spanning from the nano to the urban. At a time of profound disruption brought about by the global pandemic and coinciding with important sociopolitical events, Distributed Proximities seeks to provide a platform for the continuity of technical discourse while amplifying the space for a dialogue that also recognizes the impacts of the social in all aspects of the research.
series	ACADIA
last changed	2023/10/22 12:06

_id	ecaade2020_007
id	ecaade2020_007
authors	Yu, De
year	2020
title	Reprogramming Urban Block by Machine Creativity - How to use neural networks as generative tools to design space
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. 249-258
doi	https://doi.org/10.52842/conf.ecaade.2020.1.249
summary	The democratization of design requires balancing all sorts of factors in space design. However, the traditional way to organize spatial relationships cannot deal with such complex design objectives. Can one find another form of creativity rather the human brain to design space? As Margaret Boden mentioned, "computers and creativity make interesting partners with respect to two different projects." This paper addresses whether machine creativity in the form of neural networks could be considered as a powerful generative tool to reprogram urban block in order to meet multi-users' needs. It tested this theory in a specific block model called Agri-tecture, a new architectural form combing farming with the urban built environment. Specifically, the machine empowered by Generative Adversarial Network designed spatial layouts based on learning the existing cases. Nevertheless, since the machine can hardly avoid errors, architects need to intervene and verify the machine's work. Thus, a synergy between human creativity and machine creativity is called for.
keywords	machine creativity; Generative Adversarial Network; spatial layout; creativity combination; Agri-tecture
series	eCAADe
email
last changed	2022/06/07 07:57

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