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	caadria2022_210
id	caadria2022_210
authors	Tabi, Salma, Sakai, Yasushi, Tung, Nguyen, Taima, Masahiro, Cheddadi, Aqil and Ikeda, Yasushi
year	2022
title	A Framework for a Gameful Collective Urbanism Based on Tokenized Location Data and Liquid Democracy: Early Prototyping of a Case Study Using E-bikes
doi	https://doi.org/10.52842/conf.caadria.2022.1.585
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. 585-594
summary	The participation of citizens in designing their social and built environments is vital for the creation of sustainable cities and communities. However, in practice, collective decision-making remains challenging. Several researchers have proposed innovative models of governance to achieve a more democratic participation. This paper attempts to contribute to this topic from the viewpoint of urban planning. The objectives are twofold. First, to introduce a conceptual framework of a gameful collective process of urbanism based on location data. Second, to present an early stage of prototyping a case study using e-bikes. Research questions are elaborated as follows: How can collective processes of urban planning engage the collective intelligence and the local knowledge of the community? How to utilize technological tools to support new forms of participatory urban governance? The main contribution of this work lies in the combination of the concepts of temporal ownership of public space, tokenization of location data, and liquid democracy, to design a dynamic and gameful decision-making process that promotes collective intelligence.
keywords	Collective urbanism, Liquid democracy, Temporal ownership, Tokenization, Location data, Data dignity, Gameful design, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_317
id	caadria2022_317
authors	Grugni, Francesco, Voltolina, Marco and Cattaneo, Tiziano
year	2022
title	Use of Object Recognition AI in Community and Heritage Mapping for the Drafting of Sustainable Development Strategies Suitable for Individual Communities, With Case Studies in China, Albania and Italy
doi	https://doi.org/10.52842/conf.caadria.2022.1.717
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. 717-726
summary	In order to plan effective strategies for the sustainable development of individual communities, as prescribed by the United Nations‚ Sustainable Development Goal 11, it is necessary for designers and policy makers to gain a deep awareness of the bond that connects people to their territory. AI-driven technologies, and specifically Object Recognition algorithms, are powerful tools that can be used to this end, as they make it possible to analyse huge amounts of pictures shared on social media by residents and visitors of a specific area. A model of the emotional, subjective point of view of the members of the community is thus generated, giving new insights that can support traditional techniques such as surveys and interviews. For the purposes of this research, three case studies have been considered: the neighbourhood around Siping Road in Shanghai, China; the village of Moscopole in southeastern Albania; the rural area of Oltrep Pavese in northern Italy. The results demonstrate that a conscious use of AI-driven technologies does not necessarily imply homogenisation and flattening of individual differences: on the contrary, in all three cases diversities tend to emerge, making it possible to recognise and enhance the individuality of each community and the genius loci of each place.
keywords	sustainable communities, artificial intelligence, object recognition, social media, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2022_15
id	sigradi2022_15
authors	Jiang, Wanzhu; Wang, Jiaqi
year	2022
title	Autonomous Collective Housing Platform: Digitization, Fluidization and Materialization of Ownership
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 15–26
summary	New social phenomena like digital nomads urge an upgrade in housing ownership. This research proposes an autonomous housing platform that shapes residential communities into adaptive and reconfigurable systems, framing a cycle of digitalization, fluidization and materialization of housing ownership. Specifically, the interactive interface carries the flexible ownership model that uses virtual space voxels as digital currency; the artificial intelligence algorithm drives the multilateral ownership negotiation and circulation, and modular robots complete the mapping from ownership status to real spaces. Taking project TESSERACT as a case study, we verified the feasibility of this method and presented expected co-living scenarios: the spaces and ownership are constantly adjusted according to demands and are always in the closest interaction with users. By exploring the ownership evolution, this research guides an integrated and inclusive housing system paradigm, triggering critical evaluation of traditional models and providing new ideas for solving housing problems in the post-digital era.
keywords	Agent-Based Systems, Digital Platform, Housing Ownership, Space Planning Algorithm, Discrete Material System
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	caadria2022_507
id	caadria2022_507
authors	Bolojan, Daniel, Vermisso, Emmanouil and Yousif, Shermeen
year	2022
title	Is Language All We Need? A Query Into Architectural Semantics Using a Multimodal Generative Workflow
doi	https://doi.org/10.52842/conf.caadria.2022.1.353
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. 353-362
summary	This project examines how interconnected artificial intelligence (AI)-assisted workflows can address the limitations of current language-based models and streamline machine-vision related tasks for architectural design. A precise relationship between text and visual feature representation is problematic and can lead to "ambiguity‚ in the interpretation of the morphological/tectonic complexity of a building. Textual representation of a design concept only addresses spatial complexity in a reductionist way, since the outcome of the design process is co-dependent on multiple interrelated systems, according to systems theory (Alexander 1968). We propose herewith a process of feature disentanglement (using low level features, i.e., composition) within an interconnected generative adversarial networks (GANs) workflow. The insertion of natural language models within the proposed workflow can help mitigate the semantic distance between different domains and guide the encoding of semantic information throughout a domain transfer process.
keywords	Neural Language Models, GAN, Domain Transfer, Design Agency, Semantic Encoding, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ascaad2022_085
id	ascaad2022_085
authors	Cicek, Selen; Koc, Mustafa; Korukcu, Berfin
year	2022
title	Urban Map Generation in Artist's Style using Generative Adversarial Networks (GAN)
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 264-282
summary	Artificial Intelligence is a field that is able to learn from existing data to synthesize new ones using deep learning methods. Using Artificial Neural Networks that process big datasets, complex tasks and challenges become easily resolved. As the zeitgeist suggests, it is possible to produce novel outcomes for future projections by applying various machine learning algorithms on the generated data sets. In that context, the focus of this research is exploring the reinterpretation of 21st century urban plans with familiar artist styles using different subtypes of deep-learning-based generative adversarial networks (GAN) algorithms. In order to explore the capabilities of urban map transformation with machine learning approaches, two different GAN algorithms which are cycleGAN and styleGAN have been applied on the two main data sets. First data set, the urban data set, contains 50 cities urban plans in .jpeg format collected according to the diversity of the urban morphologies. Whereas the second data set is composed of four well-known artist’s paintings, that belong to various artistic movements. As a result of training the same data sets with different GAN algorithms and epoch values were compared and evaluated. In this respect, the study not only investigates the reinterpretation of stylistic urban maps and shows the discoverability of new representation techniques, but also offers a comparison of the use of different image to image translation GAN algorithms.
series	ASCAAD
email
last changed	2024/02/16 13:29

_id	ascaad2022_004
id	ascaad2022_004
authors	Falih, Zahraa; Mahdavinejad, Mohammadjavad; Tarawneh, Deyala; Al-Mamaniori, Hamza
year	2022
title	Solar Energy Control Strategy using Interactive Modules
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 117-138
summary	The concept of interactive canopy emerged as a notable manifestation of smart buildings in architectural endeavors, using artificial intelligence applications in computational architecture, interactive canopies came as a potential response for living organisms to combat external environmental changes as well as reduce energy consumption in buildings. This research aims to explore architecture with higher efficiency through the impact of environmentally technological factors on the design form by introducing solar energy into the design process through the implementation of interactive curtains that interact with the sun in the form of an umbrella. The main objective of the umbrellas is to protect the users from the sun's harmful rays. After designing an interactive cell using Grasshopper, the methodology follows an analytical and experimental approach, the analytical section is summarized by conducting a case study of multiple models and analyzing the techniques used in these models to discover the significant advantages and disadvantages of the design. While the experimental section demonstrates the mechanism for implementing the interactive modules. The research suggests that by designing an interactive canopy that responds to external changes and senses solar radiation in ways that when the intensity of solar radiation increases and the sun is perpendicular to the dynamic units, will lead to maintaining a more balanced level of illumination. The work efficiency is studied by simulating it by Climate Studio.
series	ASCAAD
email
last changed	2024/02/16 13:24

_id	caadria2022_231
id	caadria2022_231
authors	Kim, Frederick Chando and Huang, Jeffrey
year	2022
title	Deep Architectural Archiving (DAA), Towards a Machine Understanding of Architectural Form
doi	https://doi.org/10.52842/conf.caadria.2022.1.727
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. 727-736
summary	With the ‚digital turn‚, machines now have the intrinsic capacity to learn from big data in order to understand the intricacies of architectural form. This paper explores the research question: how can architectural form become machine computable? The research objective is to develop "Deep Architectural Archiving‚ (DAA), a new method devised to address this question. DAA consists of the combination of four distinct steps: (1) Data mining, (2) 3D Point cloud extraction, (3) Deep form learning, as well as (4) Form mapping and clustering. The paper discusses the DAA method using an extensive dataset of architecture competitions in Switzerland (with over 360+ architectural projects) as a case study resource. Machines learn the particularities of forms using 'architectural' point clouds as an opportune machine-learnable format. The result of this procedure is a multidimensional, spatialized, and machine-enabled clustering of forms that allows for the visualization of comparative relationships among form-correlated datasets that exceeds what the human eye can generally perceive. Such work is necessary to create a dedicated digital archive for enhancing the formal knowledge of architecture and enabling a better understanding of innovation, both of which provide architects a basis for developing effective architectural form in a post-carbon world.
keywords	artificial intelligence, deep learning, architectural form, architectural competitions, architectural archive, 3D dataset, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2022_168
id	sigradi2022_168
authors	Koh, Immanuel
year	2022
title	Palette2Interior Architecture: From Syntactic and Semantic Colour Palettes to Generative Interiors with Deep Learning
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 187–198
summary	Colour palettes have long played a significant role in not only capturing design ambience (e.g., as mood boards), but more significantly, in translating an abstract intuition into an explicit ordering mechanism for design representation and synthesis, whether it is in the discipline of graphic design, interior design or architectural design. Might this difficult process of design synthesis from a low-dimensional colour input domain to a high-dimensional spatial design output domain be computationally mapped? Using today’s generative adversarial networks (GANs), the paper aims to investigate this plausibility, and in doing so, hoping to envision an AI-augmented design workflow and tooling. Newly-created datasets are made procedurally and used to train three different types of deep learning models in the specific context of generating living room interior layouts. The results suggest that a combination of syntactic and semantic generative processes is necessary for a critical appropriation of such AI models
keywords	Machine Learning, Artificial Intelligence, Deep Neural Networks, Colour Palette, Interior Design
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	ecaade2022_176
id	ecaade2022_176
authors	Kotov, Anatolii, Starke, Rolf and Vukorep, Ilija
year	2022
title	Spatial Agent-based Architecture Design Simulation Systems
doi	https://doi.org/10.52842/conf.ecaade.2022.2.105
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. 105–112
summary	This paper presents case studies and analysis of agent-based reinforcement learning (RL) systems towards practical applications for specific architecture/engineering tasks using Unity 3D-based simulation methods. Finding and implementing sufficient abstraction for architecture and engineering problems to be solved by agent-based systems requires broad architectural knowledge and the ability to break down complex problems. Modern artificial intelligence (AI) and machine learning (ML) systems based on artificial neural networks can solve complex problems in different domains such as computer vision, language processing, and predictive maintenance. The paper will give a theoretical overview, such as more theoretical abstractions like zero-sum games, and a comparison of presented games. The application section describes a possible categorization of practical usages. From more general applications to more narrowed ones, we explore current possibilities of RL application in the field of relatable problems. We use the Unity 3D engine as the basis of a robust simulation environment.
keywords	AI Aided Architecture, Reinforcement Learning, Agent Simulation
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_47
id	ecaade2022_47
authors	Marsillo, Laura, Suntorachai, Nawapan, Karthikeyan, Keshava Narayan, Voinova, Nataliya, Khairallah, Lea and Chronis, Angelos
year	2022
title	Context Decoder - Measuring urban quality through artificial intelligence
doi	https://doi.org/10.52842/conf.ecaade.2022.2.237
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. 237–246
summary	Understanding the quality of places during the early design process can improve design decision making and increase not only the chance of effective site development for the place and surroundings but also provide foresight to the mental, physical and environmental well-being of the future occupants. A context can be described differently depending on the designer's studies. However, in order to view the place holistically, various layers should be considered for a cross-disciplinary correlation. This paper proposes a prototypical tool to evaluate the quality of places using machine learning to help cluster and visualise design metrics according to the features provided. By selecting a location in a city, it offers other site contexts with similar characteristics and a similar level of complexity in relation to the surroundings. The tool was initially developed for Naples (Italy) as a case study city and incorporates key indicators related to connectivity of amenities, walkability, urban density, population density, outdoor thermal comfort, popular rate review and sentiment analysis from social media. With current open-source data, these indicators such as OpenStreetMap or social media sentiment can be collected with embedded geotags. These site-specific multilayers were evaluated under the metrics of 3 ranges i.e 400, 800 and 1,200-metre walking distance. This paper demonstrates the potential of using machine learning integrated with computational design tools to visualise the otherwise invisible data for users to interpret any context comprehensively in a holistic approach. Even though this tool is made for Naples, this tool can be extended to other cities across the world. As a result, the tool assists users in understanding not only site-specific location but also draws lines to other neighbourhoods within the city with a similar phenomenon of correlation between key performance indicators.
keywords	Computational Design, Urban Analysis, Machine Learning, Computer Vision, Sentiment Analysis
series	eCAADe
email
last changed	2024/04/22 07:10

_id	acadia23_v1_242
id	acadia23_v1_242
authors	Noel, Vernelle A.
year	2023
title	Carnival + AI: Heritage, Immersive virtual spaces, and Machine Learning
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 1: Projects Catalog of the 43rd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 242-245.
summary	Built on a Situated Computations framework, this project explores preservation, reconfiguration, and presentation of heritage through immersive virtual experiences, and machine learning for new understandings and possibilities (Noel 2020; 2017; Leach and Campo 2022; Leach 2021). Using the Trinidad and Tobago Carnival - hereinafter referred to as Carnival - as a case study, Carnival + AI is a series of immersive experiences in design, culture, and artificial intelligence (AI). These virtual spaces create new digital modes of engaging with cultural heritage and reimagined designs of traditional sculptures in the Carnival (Noel 2021). The project includes three virtual events that draw on real events in the Carnival: (1) the Virtual Gallery, which builds on dancing sculptures in the Carnival and showcases AI-generated designs; (2) Virtual J’ouvert built on J’ouvert in Carnival with AI-generated J’ouvert characters specific; and (3) Virtual Mas which builds on the masquerade.
series	ACADIA
type	project
email
last changed	2024/04/17 13:58

_id	ijac202220308
id	ijac202220308
authors	Rodrigues, Ricardo C; Rovenir B Duarte
year	2022
title	Generating floor plans with deep learning: A cross-validation assessment over different dataset sizes
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 3, pp. 630–644
summary	The advent of deep learning has enabled a series of opportunities; one of them is the ability to tackle subjective factors on the floor plan design and make predictions though spatial semantic maps. Nonetheless, the amount available of data grows exponentially on a daily basis, in this sense, this research seeks to investigate deep generative methods of floor plan design and its relationship between data volume, with training time, quality and diversity in the outputs; in other words, what is the amount of data required to rapidly train models that return optimal results. In our research, we used a variation of the Conditional Generative Adversarial Network algorithm, that is, Pix2pix, and a dataset of approximately 80 thousand images to train 10 models and evaluate their performance through a series of computational metrics. The results show that the potential of this data-driven method depends not only on the diversity of the training set but also on the linearity of the distribution; therefore, high-dimensional datasets did not achieve good results. It is also concluded that models trained on small sets of data (800 images) may return excellent results if given the correct training instructions (Hyperparameters), but the best baseline to this generative task is in the mid-term, using around 20 to 30 thousand images with a linear distribution. Finally, it is presented standard guidelines for dataset design, and the impact of data curation along the entire process
keywords	Dataset Reduction, Pix2pix, Artificial Intelligence, Deep Generative Models, GANs
series	journal
last changed	2024/04/17 14:30

_id	ascaad2022_060
id	ascaad2022_060
authors	Senem, Mehmet; Koc, Mustafa; Tuncay, Hayriye; As, Imdat
year	2022
title	Using Deep Learning to Generate Front and Backyards in Landscape Architecture
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 2-16
summary	The use of artificial intelligence (AI) engines in the design disciplines is a nascent field of research, which became very popular over the last decade. In particular, deep learning (DL) and related generative adversarial networks (GANs) proved to be very promising. While there are many research projects exploring AI in architecture and urban planning, e.g., in order to generate optimal floor layouts, massing models, evaluate image quality, etc., there are not many research projects in the area of landscape architecture - in particular the design of two-dimensional garden layouts. In this paper, we present our work using GANs to generate optimal front- and backyard layouts. We are exploring various GAN engines, e.g., DCGAN, that have been successfully used in other design disciplines. We used supervised and unsupervised learning utilizing a massive dataset of about 100,000 images of front- and backyard layouts, with qualitative and quantitative attributes, e.g., idea and beauty scores, as well as functional and structural evaluation scores. We present the results of our work, i.e., the generation of garden layouts, and their evaluation, and speculate on how this approach may help landscape architects in developing their designs. The outcome of the study may also be relevant to other design disciplines.
series	ASCAAD
email
last changed	2024/02/16 13:29

_id	architectural_intelligence2022_4
id	architectural_intelligence2022_4
authors	Yihui Li, Wen Gao & Borong Lin
year	2022
title	From type to network: a review of knowledge representation methods in architecture intelligence design
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00006-9
source	Architectural Intelligence Journal
summary	With the rise of the next generation of artificial intelligence driven by knowledge and data, the research on knowledge representation in architecture is also receiving widespread attention from the academia. This paper sorts out the evolution of architectural knowledge representation methods in the history of architecture, and summarizes three progressive representation frameworks of their development with type, pattern and network. By searching these three keywords in the Web of Science Core Collection among 4867 publications from 1990 to 2021, the number of publications in the past 5 years raised more than 50%, which show significant research interest in architecture industry in recent years. Among them, the first two are static declarative knowledge representation methods, while the network-based knowledge representation method also includes procedural knowledge representation methods and provides a way for knowledge association. This means the network representation has more advantage in terms of the logical completeness of knowledge representation, and accounts for 67% of the current research on knowledge representation in architecture. In the context of the rapid development of artificial intelligence, this method can realize the construction of architectural knowledge system and greatly improve the work efficiency of the building industry. On the other hand, in the face of carbon-neutral sustainable development scenarios, using knowledge representation, building performance knowledge and design knowledge could be expressed in a unified manner, and a personalized and efficient workflow for performance-oriented scheme design and optimization would be achieved.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	ascaad2022_024
id	ascaad2022_024
authors	Yonder, Veli
year	2022
title	Using Artificial Neural Networks and Space Syntax Techniques to Understand Mass Housing Design Parameters
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 283-299
summary	The design of mass housing is a complex process that involves the use of a large number of components and parameters. The field of design has unavoidably been changed by the impact of digitalization, which has resulted in the proliferation of computational design models, data structures, artificial intelligence, and an algorithmic way of thinking. Artificial neural networks, space syntax methodologies, predefined rules will help shape the steps of the schematic design process and establish certain limitations. Within the confines of this research, predefined guidelines were used to bring about geometric variances in the design of mass houses. Both traditional and digital instruments were utilized in the process. Methodologies based on artificial neural network models and space syntax techniques were utilized to investigate case studies and develop prototypes. The artificial neural network model is designed to understand the factors affecting mass housing design parameters. The importance percentages of the parameters were determined according to the outputs of this model. Besides, methodologies based on space syntax have had a significant impact, both on decision-making processes and on feedback-based design. In this study, several digital tools were used to analyze such as visibility graph analyzes, node-based techniques, and isovist analysis. In the section devoted to the conclusion, the comparison of the various prototypes that were obtained, the findings of the space syntax analysis, and the various stages of model development are discussed.
series	ASCAAD
email
last changed	2024/02/16 13:24

_id	ecaade2022_234
id	ecaade2022_234
authors	Afsar, Secil, Estévez, Alberto T., Abdallah, Yomna K., Turhan, Gozde Damla, Ozel, Berfin and Doyuran, Aslihan
year	2022
title	Activating Co-Creation Methodologies of 3D Printing with Biocomposites Developed from Local Organic Wastes
doi	https://doi.org/10.52842/conf.ecaade.2022.1.215
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. 215–224
summary	Compared to the take-make-waste-oriented linear economy model, the circular model has been studied since the 1980s. Due to consumption-oriented lifestyles along with having a tendency of considering waste materials as trash, studies on sustainable materials management (SMM) have remained at a theoretical level or created temporary and limited impacts. To ensure SMM supports The European Green Deal, there is a necessity of developing top-down and bottom-up strategies simultaneously, which can be metaphorized as digging a tunnel from two different directions to meet in the middle of a mountain. In parallel with the New European Bauhaus concept, this research aims to create a case study for boosting bottom-up and data-driven methodologies to produce short-loop products made of bio-based biocomposite materials from local food & organic wastes. The Architecture departments of two universities from different countries collaborated to practice these design democratization methodologies using data transfer paths. The 3D printable models, firmware code, and detailed explanation of working with a customized 3D printer paste extruder were shared using online tools. Accordingly, the bio-based biocomposite recipe from eggshell, xanthan gum, and citric acid, which can be provided from local shops, food & organic wastes, was investigated concurrently to enhance its printability feature for generating interior design elements such as a vase or vertical gardening unit. While sharing each step from open-source platforms with adding snapshots and videos allows further development between two universities, it also makes room for other researchers/makers/designers to replicate the process/product. By combining modern manufacturing and traditional crafting methods with materials produced with DIY techniques from local resources, and using global data transfer platforms to transfer data instead of products themselves, this research seeks to unlock the value of co-creative design practices for SMM.
keywords	Sustainable Materials Management, Co-Creation, Food Waste, 3D Printing, New European Bauhaus
series	eCAADe
email
last changed	2024/04/22 07:10

_id	acadia22pr_124
id	acadia22pr_124
authors	Ago, Viola; Tursack, Hans
year	2022
title	Understorey - A Pavilion in Parts
source	ACADIA 2022: Hybrids and Haecceities [Projects Catalog of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-7-4]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 124-129.
summary	In the summer of 2018, our collaboration was awarded a University Design Fellowship from the Exhibit Columbus organization to design, fabricate, and build a large pavilion in Columbus, Indiana as part of a biannual contemporary architecture exhibition. Our proposal for the competition was a pavilion that would double as an ecological education center. Our inspiration for this program was triggered in part by our reading of Jane Bennett’s materialist philosophy outlined in her book Vibrant Matter (2009). Through Bennett’s lens, our design rendered our site’s context as an animate field, replete with pre-existing material composites that we wanted to celebrate through a series of displays, information boards, and artificial lighting. In this, the installation would feature samples of local plants, minerals, and rocks, indigenous to Southern Indiana.
series	ACADIA
type	project
email
last changed	2024/02/06 14:06

_id	caadria2022_391
id	caadria2022_391
authors	Burden, Alan, Donovan, Jared, Caldwell, Glenda and Belek Fialho Teixeira, Muge
year	2022
title	Hybrid Digital Crafts With Collaborative Robotics
doi	https://doi.org/10.52842/conf.caadria.2022.2.021
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. 21-30
summary	Bespoke manufacturers that fabricate for architecture and design rely on skill artisans such as patternmakers to remain profitable. Collaborative robotics and augmented reality (AR) offer new technological options and approaches that integrate with existing artisan techniques. Can these technologies provide productive and practical assistance to skilled handcraft artisans? This research presents an original approach to robotic fabrication using AR robot control, and artisan techniques to fabricate an original design. The method includes documenting artisan ethnography, designing a custom cutting end effector and an AR control interface, utilising the capabilities of the robot fabricating system. The research outcome is a hybrid digital craft approach to collaborative robotic patternmaking and handcrafting. The fabrication system reduced the amount of time and physical exertion of designing and cutting out patterns from various materials. This demonstrates that robotic tools can expand rather than replace the capability of existing artisan occupations, helping to strengthen resilience in local industries and promote new innovations.
keywords	Collaborative robotic fabrication, hybrid digital craft, artisan manufacturing, augmented reality, SDG9.
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_367
id	ecaade2022_367
authors	Doumpioti, Christina and Huang, Jeffrey
year	2022
title	Field Condition - Environmental sensibility of spatial configurations with the use of machine intelligence
doi	https://doi.org/10.52842/conf.ecaade.2022.2.067
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. 67–74
summary	Within computational environmental design (CED), different Machine Learning (ML) models are gaining ground. They aim for time efficiency by automating simulation and speeding up environmental performance feedback. This study suggests an approach that enhances not the optimization but the generative aspect of environmentally driven ML processes in architectural design. We follow Stan Allen's (2009) idea of 'field conditions' as a bottom-up phenomenon according to which form and space emerge from local invisible and dynamic connections. By employing parametric modeling, environmental analysis data, and conditional Generative Adversarial Networks [cGAN] we introduce a generative approach in design that reverses the typical design process of going from formal interpretation to analysis and encourages the emergence of spatial configurations with embedded environmental intelligence. We call it Intensive-driven Environmental Design Computation [IEDC], and we employ it in a case study on a residential building typology encountered in the Mediterranean. The paper describes the process, emphasizing dataset preparation as the stage where the logic of field conditions is established. The proposed research differentiates from cGAN models that offer automatic environmental performance predictions to one that spatial predictions stem from dynamic fields.
keywords	Field Architecture, Environmental Design, Generative Design, Machine Learning, Residential Typologies
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_93
id	caadria2022_93
authors	Feng, Jiajia, Liang, Yuebing, Hao, Qi, Xu, Ke and Qiu, Waishan
year	2022
title	POI Data Versus Land Use Data, Which Are Most Effective in Modelling Theft Crimes?
doi	https://doi.org/10.52842/conf.caadria.2022.1.425
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. 425-434
summary	Alleviating crime and improving urban safety is important for sustainable development of society. Prior studies have used either land use data or point-of-interests (POI) data to represent urban functions and investigate their associations with urban crime. However, inconsistent and even contrary results were yielded between land use and POI data. There is no agreement on which is more effective. To fill this gap, we systematically compare land use and POI data regarding their strength as well as the divergence and coherence in profiling urban functions for crime studies. Three categories of urban function features, namely the density, fraction, and diversity, are extracted from POI and land use data, respectively. Their global and local strength are compared using ordinary least square (OLS) regression and geographically weighted regression (GWR), with a case study of Beijing, China. The OLS results indicate that POI data generally outperforms land use data. The GWR models reveal that POI Density is superior to other indicators, especially in areas with concentrated commercial or public service facilities. Additionally, Land Use Fraction performs better for large-scale functional areas like green space and transportation hubs. This study provides important reference for city planners in selecting urban function indicators and modelling crimes.
keywords	POI, Land Use, Urban Functions, Theft crime, Predictive Power, SDG 16
series	CAADRIA
email
last changed	2022/07/22 07:34

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