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	ecaade2022_172
id	ecaade2022_172
authors	Vugreshek, Zvonko
year	2022
title	Discrete Differences between Aggregate Systems for Generative Urban and Architectural Design
doi	https://doi.org/10.52842/conf.ecaade.2022.2.029
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. 29–38
summary	The activation of aggregate systems, procedural generation, and other models of discrete computation result in different organizations and formal outcomes. Some differences seem blurry but are relevant to understand in order to govern the computational design process in the specific domain. They are developing around empiric principles, are based on discrete automation rule sets, and are intertwined in various ways. The paper presents and describes some differences and communalities between each system. Its goal is to support the computational designer, architect or urban planner in the decision-making process and choice of which system could work best in a given context and to solve a specific problem. An introduction into aggregation or automation will serve as a foundation for the research. The discrete systems Cellular Automata, Wave Function Collapse, Graph-Grammar Aggregation will be described. In this paper, the latter is specified as selection-based-aggregation. Diffusion-Limited Aggregation (DLA), which is regarded as an early translation of natural behaviour into scripted nature will serve as a framework. In a next step potential and utilization of these discrete systems in expanding the language of architectural and urban morphology will be experimentally demonstrated and compared. The paper concludes by suggesting a current state of development and potential adaptation of the methods for broader use within the architectural and urban design paradigm of developing methods for the creation of new computational typologies.
keywords	Discrete Aggregation, Cellular Automata, Procedural Generation, Urban Morphology Generation, Wave Function Collapse
series	eCAADe
email
last changed	2024/04/22 07:10

_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	caadria2022_272
id	caadria2022_272
authors	Dong, Zhiyong
year	2022
title	Perceiving Fabric Immersed in Time, an Exploration of Urban Cognitive Capabilities of Neural Networks
doi	https://doi.org/10.52842/conf.caadria.2022.1.263
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. 263-272
summary	City develops gradually with the lapse of time. Cities, as a ‚container‚, are injected new urban elements along the trajectory of the times and the progress of human civilization, constructing the historical structures involved past, present and future. Thus, the cultural information of each era is preserved in the urban fabric together and urban fabric features are complex and rich, which are difficult to capture in traditional design methods. In this paper, we try to use Generative Adversarial Networks (GAN), one of the neural network algorithms, to explore the inner rules of complex urban morphological features and realize the perception of the urban fabric. Neural networks are innovatively applied to the larger and more complex city generation in this experiment. First, we collect European urban fabric as the dataset, then label data to facilitate machine training, use GAN to learn the feature of the dataset by adjusting parameters, and analyze the effect of the generated results. The automatic feature learning capability of the neural networks is used to summarize the inherent patterns and rules in urban development which is difficult for human to discover.
keywords	Deep Learning, Generative Adversarial Networks, Generative Design, Morphology Cognition, Urban Fabric, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	cdrf2022_233
id	cdrf2022_233
authors	Yubo Liu, Zhilan Zhang, and Qiaoming Deng
year	2022
title	Exploration on Diversity Generation of Campus Layout Based on GAN
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_20
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	Previous studies have shown that GAN has made some progress in the generation of campus layout plan, but the result is single output for single input condition. This paper hopes to make some attempts and explorations on the diversity generation of campus planning layout design by machine learning. Based on Pix2Pix model, this paper proposes a method to divide image channels so that both the campus function bubble diagram and the site boundary can both become the input conditions. There is a strong correspondence between the campus functional bubble diagram and the campus layout. The main idea of this study is to control the generated results by changing the input of the campus functional bubble diagram, so that we can have a diversity layout of campus according to the same site conditions. In the experiment, we train thirty samples of campus planning layout design, and finally evaluate the generated results in a qualitative and quantitative way, which proves that the generated results are relatively ideal. This research enables designers to participate in the process of machine learning generative design to control the generation results.
series	cdrf
email
last changed	2024/05/29 14:02

_id	cdrf2022_274
id	cdrf2022_274
authors	Zhiyong Dong and Jinru Lin
year	2022
title	Nolli Map: Interpretation of Urban Morphology Based on Machine Learning
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_24
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	Nolli map is the earliest diagram tool to simplify and quantify urban form, which most intuitively reflects the spatial layout of tangible elements in the city. The urban morphology contains its inherent evolutionary laws. Exploring the inner rules of cities is helpful for people to conduct urban research and design. Unlike the traditional research methods of urban morphology, the neural network algorithm provides us with new ideas for understanding urban morphology. In this experiment, we label 136 European cities samples in the rules of Nolli map as a training set for machine learning. We use Generative Adversarial Networks (GAN) for multiple mapping experiments. The generated images present recognizable and plausible images of the urban fabric. The results show that the machine can learn the inherent laws of complex urban fabrics, which expands a new applied method for the study of urban morphology.
series	cdrf
email
last changed	2024/05/29 14:02

_id	ascaad2022_028
id	ascaad2022_028
authors	Hassan, Sarah
year	2022
title	Adapting Digital Architecture Vocabulary to Reformulate Geometric Compositions of Islamic Facades - Case Study: Proposed Model for Islamic Façade through Digital Vocabulary
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. 463-483
summary	Islamic architectural facades characterized by many distinguished vocabularies that formed its character; as arches, ornaments, al-Muqarnas and mashrabeya etc. However, during the modern era, the Islamic heritage regions faced many changes and transformations of its character, either by new buildings that were built according to modern or unplanned styles, or by random and unplanned developments. However, recently and with the beginning of the twenty first century and with the great breakthrough in the digital tools and techniques, it facilitates new horizons in the architectural form generation. Accordingly, the research focuses on how to investigate the positive impacts of digital technologies on Islamic Architecture. In addition to how to utilize the digital thoughts, vocabulary, and techniques to create and develop a heritage inspired vocabulary that can compromise with the traditional Islamic architecture theme. Through this, the research aims to achieve a systemization of digital design strategies to facilitate the generation of Islamic-inspired façade, to create a new Islamic architecture that can be applied within Islamic heritage regions to connect the modern buildings which located in these regions with the existing Islamic reference. To achieve that, the research starts with studying and discussing the main elements that formed the Islamic facades, to stand on the methods of formations of each element and its function of the Islamic façade, whether it is an intrinsic function or aesthetic function. Consequently, standing on the main digital theories that lead to new architectural vocabulary that can homogenate with Islamic vocabulary, through studying the concept of each digital theory, accordingly how it can be applied theoretically to create a modern façade with an Islamic spirit. The research ends with a case study for a proposed modern building that resembles most of the recent buildings in Al-Azhar Islamic region in Cairo, and how through applying some selected digital theories can result in developing and renovating this facade to match the heritage Islamic surrounding in a new digital way.
series	ASCAAD
email
last changed	2024/02/16 13:24

_id	caadria2022_275
id	caadria2022_275
authors	Sukegawa, Chika, Khajehee, Arastoo, Kawakami, Takuma, Someya, Syunsuke, Hirano, Yuji, Shibuya, Masako, Ito, Koki, Watanabe, Yoshiaki, Wang, Qiang, Inaba, Tooru, Lee, Alric, Hotta, Kensuke, Miyaguchi, Mikita and Ikeda, Yasushi
year	2022
title	Smart Hand for Digital Twin Timber Work -The interactive procedural scanning by industrial arm robot
doi	https://doi.org/10.52842/conf.caadria.2022.2.131
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. 131-140
summary	This paper describes a 3D automated scanning method for building materials, namely "The Interactive Procedural Scanning‚, in a collaborative environment composed of a human worker and a CNC robot. This procedure aims to translate the observation skill of an experienced carpenter into an intelligent robotic system. The system frames its function on the first stage of a traditional timber examination process, called ‚Kidori‚, in which observations and findings are marked on the timber surface to provide hints for the subsequent cutting process. This paper aims to recreate the procedures using an industrial robotic arm, computer vision, and a human worker. A digital twin model of the timber is created with a depth camera serving as a base map to exchange information and receive instruction from the human worker. The margin of a discrepancy between the original processing location and the location of the actual end effector, where the tools are, is minimised in this system.
keywords	3D scanning, computer vision, traditional technique, phycology, machine learning, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	acadia22_224
id	acadia22_224
authors	Coersmeier, Jonas; Nanasca, James; Man Hin, Ivan Yan; Blasetti, Ezio
year	2022
title	Nanotectonica SEM-GAN
source	ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 224-243.
summary	The present study, Nanotectonica SEM-GAN, focuses on two processes for image production, one based in the field of nanotechnology and the other in machine learning: Scanning Electron Microscopy (SEM) and Generative Adversarial Networks (GAN). It establishes commonalities of these routines as they pertain to aesthetics and design methodology, and it explores methods of spatializing and materializing images produced in their interaction. The study of transposing rich image material to three-dimensional geometry and material artifact is considered relevant not only to the particular study at hand, but also to the general problem of image-based machine learning techniques when applied in the spatial design disciplines. A third process, Robotic Incremental Metal Forming (RIMF), advances the aesthetic language of SEM-GAN through the sculptural method of the relief.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:00

_id	ecaade2022_175
id	ecaade2022_175
authors	Di Carlo, Raffaele, Mittal, Divyae and Vesely, Ondrej
year	2022
title	Generating 3D Building Volumes for a Given Urban Context using Pix2Pix GAN
doi	https://doi.org/10.52842/conf.ecaade.2022.2.287
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. 287–295
summary	Our ability to delegate the most intellectually demanding tasks to machines improves with each passing day. Even in the fields of architecture and design, which were previously thought to be exclusive domain of human creativity and flare, we are moving the first steps towards developing models that can capture the patterns, invisible to the naked eye, embedded in the creative process. These patterns reflect ideas and traditions, imprinted in the collective mind over the course of history, that can be improved upon or serve as a cautionary tale for the new generation of designers in their work of designing an equitable, more inclusive future. Generative Adversarial Networks (GANs) give us the opportunity to turn style and design into learnable features that can be used to automatically generate blueprints and layouts. In this study, we attempt to apply this technology to urban design and to the task of generating a building footprint and volume that fits within the surrounding built environment. We do so by developing a Pix2Pix model composed of a ResNet-6 generator and a Patch discriminator, applying it to satellite views of neighborhoods from across the Netherlands, and then turning the resulting 2D generated building footprint into a reusable 3D model. The model is trained using the national cadastral data and TU Delft 3D BAG dataset. The results show that it is possible to predict a building shape compatible in style and height with the surroundings. Although the model can be used for different applications, we use it as an evaluation tool to compare the design alternatives fitting the desired contextual patterns.
keywords	Generative Adversarial Networks, Urban Design, Pix2Pix, Raster Vectorization, 3D Rendering
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_114
id	caadria2022_114
authors	Dong, Zhiyong, Lin, Jinru, Wang, Siqi, Xu, Yijia, Xu, Jiaqi and Liu, Xiao
year	2022
title	Where Will Romance Occur, A New Prediction Method of Urban Love Map through Deep Learning
doi	https://doi.org/10.52842/conf.caadria.2022.1.213
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. 213-222
summary	Romance awakens fond memories of the city. Finding out the relationship between romantic scene and urban morphology, and providing a prediction, can potentially facilitate the better urban design and urban life. Taking the Yangtze River Delta region of China as an example, this study aims to predict the distribution of romantic locations using deep learning based on multi-source data. Specifically, we use web crawlers to extract romance-related messages and geographic locations from social media platforms, and visualize them as romance heatmap. The urban environment and building features associated with romantic information are identified by Pearson correlation analysis and annotated in the city map. Then, both city labelled maps and romance heatmaps are fed into a Generative Adversarial Networks (GAN) as the training dataset to achieve final romance distribution predictions across regions for other cities. The ideal prediction results highlight the ability of deep learning techniques to quantify experience-based decision-making strategies that can be used in further research on urban design.
keywords	Romance Heatmap, Generative Adversarial Networks, Deep Learning, Big Data Analysis, Correlation Analysis, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_275
id	ecaade2022_275
authors	Gan, Amelia Wen Jiun, Guida, George, Kim, Dongyun, Shah, Devashree, Youn, Hyejun and Seibold, Zach
year	2022
title	Modulo Continuo - 5-axis ceramic additive manufacturing applications for evaporative cooling facades modules
doi	https://doi.org/10.52842/conf.ecaade.2022.1.047
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. 47–55
summary	Recent developments in industrial robotics present an increasing degree of control in additive manufacturing, enabling customization of architectural building components at the scale of the individual unit. Combining the affordances of a 6-axis robotic arm, paste- based extrusion, and terracotta clay, Modulo Continuo presents methods for part-customization of evaporative cooling facade modules. The design of the facade modules is developed firstly at the scale of the tectonic unit - as a self-supporting, interlocking modular system of curved modules with an embedded water reservoir for evaporative cooling. Second, this is developed at the scale of the toolpath - in which the density of the infill geometry in the modules is calibrated based on principles of evaporative cooling. This research presents aesthetic and performative opportunities through an exploration of infill patterning and density of modules based on evaporative cooling requirements. To produce each curved module through additive manufacturing, curved CNC milled substrates are used to support the geometry while accommodating clay shrinkage. Furthermore, this paper presents novel digital workflows for the customization of a modular façade system and the generation of variable toolpaths for infill patterns. By developing additive manufacturing methodologies for part- customization, the research presents future opportunities for the digital fabrication of ceramic construction elements.
keywords	Additive Manufacturing, Digital Fabrication, Evaporative Cooling, Ceramics
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_90
id	caadria2022_90
authors	Veloso, Pedro, Rhee, Jinmo, Bidgoli, Ardavan and Ladron de Guevara, Manuel
year	2022
title	Bubble2Floor: A Pedagogical Experience With Deep Learning for Floor Plan Generation
doi	https://doi.org/10.52842/conf.caadria.2022.1.373
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. 373-382
summary	This paper reports a pedagogical experience that incorporates deep learning to design in the context of a recently created course at the Carnegie Mellon University School of Architecture. It analyses an exercise called Bubble2Floor (B2F), where students design floor plans for a multi-story row-house complex. The pipeline for B2F includes a parametric workflow to synthesise an image dataset with pairs of apartment floor plans and corresponding bubble diagrams, a modified Pix2Pix model that maps bubble diagrams to floor plan diagrams, and a computer vision workflow to translate images to the geometric model. In this pedagogical research, we provide a series of observations on challenges faced by students and how they customised different elements of B2F, to address their personal preferences and problem constraints of the housing complex as well as the obstacles from the computational workflow. Based on these observations, we conclude by emphasising the importance of training architects to be active agents in the creation of deep learning workflows and make them accessible for socially relevant and constrained design problems, such as housing.
keywords	Architectural Pedagogy, Deep Learning, Conditional GAN, Space Planning, Floor Plan, SDG 4, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	cdrf2022_314
id	cdrf2022_314
authors	Yuqian Li, Weiguo Xu, and Xingchen Liu
year	2022
title	Research on Architectural Generation Design of Specific Architect's Sketch Based on Image-To-Image Translation
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_28
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	Sketch is a way for architects to communicate with others. Architects record their own ideas through rapid drawing. However, sketches are abstract, vague, and even ambiguous. To this end, architects need to spend a lot of time, through modeling and other means, to present the architectural plan that can be understood by people. However, this method is time-consuming and laborious. Due to the development of deep learning technology, especially convolutional neural networks (CNN) and generative adversarial networks (GAN), they have shown great advantages in the field of image recognition and generation. With the help of these technologies, ambiguous architectural sketches can be directly transformed into architectural scheme drawings, and architects’ creative intentions can be continuously improved and developed, It will be very convenient and efficient. Therefore, based on the image-to-image translation, this paper realizes the mapping from architectural sketches to architectural scheme drawings with the help of CycleGAN. Through the analysis of the architectural generation design results of Frank Gehry's and Alberto Campo Baeza's architectural sketches, firstly, the feasibility of this method is verified. Secondly, it is found that this method can well complete the identification of sketch boundaries. In the generated scheme drawings, it can not only reflect the volume and lighting changes of the building, but also reflect the architect's creative intention and style to a large extent, The side reflects the cognitive ability of this method to architectural design.
series	cdrf
email
last changed	2024/05/29 14:02

_id	ecaade2022_153
id	ecaade2022_153
authors	Zhong, Ximing, Fricker, Pia, Yu, Fujia, Tan, Chuheng and Pan, Yuzhe
year	2022
title	A Discussion on an Urban Layout Workflow Utilizing Generative Adversarial Network (GAN) - With a focus on automatized labeling and dataset acquisition
doi	https://doi.org/10.52842/conf.ecaade.2022.2.583
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. 583–592
summary	Deep Learning (DL) has recently gained widespread attention in the automation of urban layout processes. This study proposes a rule-based and Generative Adversarial Network (GAN) workflow to automatically select and label urban datasets to train customized GAN models for the generation of urban layout proposals. The developed workflow automatically collects and labels urban typology samples from open-source maps. Furthermore, it controls the results of the GAN process with labels and provides real-time urban layout suggestions based on a co-design process. The conducted case study shows that the average value of the GAN results, trained from an automatically generated dataset, meets the site's requirements. The developed co-design strategy allows the architect to control the GAN process and perform iterations on urban layouts. The research addresses the research gap in GAN applications in the field of urban design and planning. Many studies have demonstrated that training the (GAN) model by labeling enables machines to learn urban morphological features and urban layout logic. However, two research gaps remain: (1) The manual filtering of GAN urban sample datasets to fit site-specific design requirements is very time-consuming. (2) Without a suitable data labeling method, it is difficult to manage the GAN process in such a manner to facilitate the meeting of overriding design requirements.
keywords	Deep Learning, Generative Adversarial Network (GAN), Urban Layout Process, Automatic Dataset Construction, Co-design
series	eCAADe
email
last changed	2024/04/22 07:10

_id	cdrf2022_304
id	cdrf2022_304
authors	Anni Dai
year	2022
title	Co-creation: Space Reconfiguration by Architect and Agent Simulation Based Machine Learning
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_27
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	This research is a manifestation of architectural co-creation between agent simulation based machine learning and an architect’s tacit knowledge. Instead of applying machine learning brains to agents, the author reversed the idea and applied machine learning to buildings. The project used agent simulation as a database, and trained the space to reconfigure itself based on its distance to the nearest agents. To overcome the limitations of machine learning model’s simplified solutions to complicated architectural environments, the author introduced a co-creation method, where an architect uses tacit knowledge to overwatch and have real-time control over the space reconfiguration process. This research combines both the strength of machine learning’s data-processing ability and an architect’s tacit knowledge. Through exploration of emerging technologies such as machine learning and agent simulation, the author highlights limitations in design automation. By combining an architect’s tacit knowledge with a new generation design method of agent simulation based machine learning, the author hopes to explore a new way for architects to co-create with machines.
series	cdrf
email
last changed	2024/05/29 14:02

_id	sigradi2022_87
id	sigradi2022_87
authors	Berdos, Georgios (Yorgos); Dounas, Theodore
year	2022
title	Deciphering CryptoArchitecture: The architectural design studio as a vehicle for exploring the integration between blockchain and architectural design
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. 543–554
summary	In this paper we are using the outputs of research led architectural design studios, originally articulated as pedagogical projects at the Edinburgh School of Architecture and Landscape Architecture, University of Edinburgh and the Robert Gordon University in Aberdeen to explore and evaluate the possible transformative effects of blockchain/Decentralised Ledger technologies and decentralisation in the architecture discipline. After describing the pedagogical studio experiments, we frame a classification of relevant thematic clusters, where distributed ledger technologies can be applied to architectural design and thinking. The design studio, is treated as a methodological research tool, appropriate for the identification of existing knowledge gaps around the topic. The emerging classifications in terms of blockchain and decentralisation implementation is used to synthesise a map of future pedagogical and research activity.
keywords	Blockchain, Cryptoeconomics, Tokenization, Pedagogy, Codesign
series	SIGraDi
email
last changed	2023/05/16 16:56

_id	ijac202220203
id	ijac202220203
authors	Dzieduszyñski, Tomasz
year	2022
title	Machine learning and complex compositional principles in architecture: Application of convolutional neural networks for generation of context-dependent spatial compositions
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 196–215
summary	A substantial part of architectural and urban design involves processing of compositional interdependenciesand contexts. This article attempts to isolate the problem of spatial composition from the broader category ofsynthetic image processing. The capacity of deep convolutional neural networks for recognition and utilization of complex compositional principles has been demonstrated and evaluated under three scenariosvarying in scope and approach. The proposed method reaches 95.1%–98.5% efficiency in the generation ofcontext-fitting spatial composition. The technique can be applied for the extraction of compositionalprinciples from the architectural, urban, or artistic contexts and may facilitate the design-related decisionmaking by complementing the required expert analysis
keywords	Spatial composition, architecture, convolutional neural network, ordering principles, machine learning, image generation, design, CAAD
series	journal
last changed	2024/04/17 14:29

_id	ecaade2022_217
id	ecaade2022_217
authors	Panagiotidou, Vasiliki and Koerner, Andreas
year	2022
title	From Intricate to Coarse and Back - A voxel-based workflow to approximate high-res geometries for digital environmental simulations
doi	https://doi.org/10.52842/conf.ecaade.2022.1.491
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. 491–500
summary	Digital environmental simulations can present a computational bottleneck concerning the complexity of geometry. Therefore, a series of workarounds, ranging from cloud-based solutions to machine learning simulations as surrogate simulations are conventionally applied in practice. Concurrently, contemporary advances in procedural modelling in architecture result in design concepts with high polygon counts. This leads to an ever- increasing resolution discrepancy between design and analysis models. Responding to this problem, this research presents a step-by-step approximation workflow for handling and transferring high-resolution geometries between procedural modelling and environmental simulation software. The workflow is intended to allow designers to quickly assess a design’s interaction with environmental parameters such as airflow and solar radiation and further articulate them. A controllable voxelization procedure is applied to approximate the original geometry and therefore reduce the resolution. Controllable in this context refers to the user’s ability to locally adjust the voxel resolution to fit design needs. After export and simulation, 3d results are imported back into the design environment. The colour properties are re-mapped onto the original high- resolution geometry following a weighted proximity technique. The developed data transfer pipeline allows designers to integrate environmental analysis during initial design steps, which is essential for accessibility in the design profession. This can help to environmentally inform generative designs as well as to make simulation workflows more accessible when working with a wider range of geometries. In this, it reduces the perceived discrepancy between the concept and simulation model. This eases the use and allows a wider audience of users to develop co-creation processes between computation, architecture, and environment.
keywords	Simulation, Accessibility, Computation, Environmental Data, Workflow
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_346
id	ecaade2022_346
authors	Sartorius, Marie P. and von Both, Petra
year	2022
title	Rule-Based Design for the Integration of Humanoid Assistance Robotics into the Living Environment of Senior Citizens
doi	https://doi.org/10.52842/conf.ecaade.2022.2.367
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. 367–376
summary	The following paper deals with the hypothesis that in a few years, the everyday lives of seniors and those in need of care will be considerably facilitated by using humanoid assistance robots to improve and prolong their independent lives. The interdisciplinary research project JuBOT of the Karl-Zeiss Foundation deals with developing and applying AI-supported humanoid robots in seniors living spaces. In the research area of architecture, several questions arise in the sense of co-design: Which requirements and design rules can be derived and generalised to develop suitable typologies? And how can these requirements of robotics and buildings be integrated into a digital design process? In this matter, a master thesis identified and categorised areas of action for the assistance robot through a user- and function-based analysis process. Afterwards, a proposal for a generalisable typology for residential modules has been developed, applied, and evaluated. In addition to gaining architectural knowledge, the JuBOT project is also about implementing a suitable digital design process. Thus, the identified planning requirements must be implemented in a BIM-based checking process (ModelCheck).
keywords	Senior Residence, Accessibility, Care Concept, Living Concept, Architectural Psychology, Assistant Robot, BIM, Building Information Modeling, ModelCheck
series	eCAADe
email
last changed	2024/04/22 07:10

_id	cdrf2022_488
id	cdrf2022_488
authors	Tomás Vivanco, Juan Eduardo Ojeda, Philip Yuan
year	2022
title	Regression-Based Inductive Reconstruction of Shell Auxetic Structures
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_42
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	This article presents the design process for generating a shell-like structure from an activated bent auxetic surface through an inductive process based on applying deep learning algorithms to predict a numeric value of geometrical features. The process developed under the Material Intelligence Workflow applied to the development of (1) a computational simulation of the mechanical and physical behaviour of an activated auxetic surface, (2) the generation of a geometrical dataset composed of six geometric features with 3,000 values each, (3) the construction and training of a regression Deep Neuronal Network (DNN) model, (4) the prediction of the geometric feature of the auxetic surface's pattern distance, and (5) the reconstruction of a new shell based on the predicted value. This process consistently reduces the computational power and simulation time to produce digital prototypes by integrating AI-based algorithms into material computation design processes.
series	cdrf
email
last changed	2024/05/29 14:03

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