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_120
id	caadria2022_120
authors	Lin, Yuxin
year	2022
title	Rhetoric, Writing, and Anexact Architecture: The Experiment of Natural Language Processing (NLP) and Computer Vision (CV) in Architectural Design
doi	https://doi.org/10.52842/conf.caadria.2022.1.343
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. 343-352
summary	This paper presents a novel language-driven and artificial intelligence-based architectural design method. This new method demonstrates the ability of neural networks to integrate the language of form through written texts and has the potential to interpret the texts into sustainable architecture under the topic of the coexistence between technologies and humans. The research merges natural language processing, computer vision, and human-machine interaction into a machine learning-to-design workflow. This article encompasses the following topics: 1) an experiment of rethinking writing in architecture through anexact form as rhetoric; 2) an integrative machine learning design method incorporating Generative Pre-trained Transformer 2 model and Attentional Generative Adversarial Networks for sustainable architectural production with unique spatial feeling; 3) a human-machine interaction framework for model generation and detailed design. The whole process is from inexact to exact, then finally anexact, and the key result is a proof-of-concept project: Anexact Building, a mixed-use building that promotes sustainability and multifunctionality under the theme of post-carbon. This paper is of value to the discipline since it applies current and up-to-date digital tools research into a practical project.
keywords	Rhetoric and writing, Natural Language Processing, Computer Vision, GPT-2, AttnGAN, Human-computer Interaction, Architectural Design, Post-carbon, SDG3, SDG11
series	CAADRIA
email
last changed	2022/07/22 07:34

_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_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	ascaad2022_102
id	ascaad2022_102
authors	Turki, Laila; Ben Saci, Abdelkader
year	2022
title	Generative Design for a Sustainable Urban Morphology
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. 434-449
summary	The present work concerns the applications of generative design for sustainable urban fabric. This represents an iterative process that involves an algorithm for the generation of solar envelopes to satisfy solar and density constraints. We propose in this paper to explore a meta-universe of human-machine interaction. It aims to design urban forms that offer solar access. This being to minimize heating energy expenditure and provide solar well-being. We propose to study the impact of the solar strategy of building morphosis on energy exposure. It consists of determining the layout and shape of the constructions based on the shading cut-off time. This is a period of desirable solar access. We propose to define it as a balance between the solar irradiation received in winter and that received in summer. We rely on the concept of the solar envelope defined since the 1970s by Knowles and its many derivatives (Koubaa Turki & al., 2020). We propose a parametric model to generate solar envelopes at the scale of an urban block. The generative design makes it possible to create a digital model of the different density solutions by varying the solar access duration. The virtual environment created allows exploring urban morphologies resilient both to urban densification and better use of the context’s resources. The seasonal energy balance, between overexposure in summer and access to the sun in winter, allows reaching high energy and environmental efficiency of the buildings. We have developed an algorithm on Dynamo for the generation of the solar envelope by shading exchange. The program makes it possible to detect the boundaries of the parcels imported from Revit, establish the layout of the building, and generate the solar envelopes for each variation of the shading cut-off time. It also calculates the FAR1 and the FSI2 from the variation of the shading cut-off time for each parcel of the island. We compare the solutions generated according to the urban density coefficients and the solar access duration. Once the optimal solution has been determined, we export the results back into Revit environment to complete the BIM modelling for solar study. This article proposes a method for designing buildings and neighbourhoods in a virtual environment. The latter acts upstream of the design process and can be extended to the different phases of the building life cycle: detailed design, construction, and use.
series	ASCAAD
email
last changed	2024/02/16 13:38

_id	ecaade2022_127
id	ecaade2022_127
authors	Yang, Donglai, Wang, Likai and Ji, Guohua
year	2022
title	Optimization-Assisted Building Design - Cases study of design optimization based on real-world projects
doi	https://doi.org/10.52842/conf.ecaade.2022.1.609
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. 609–618
summary	Computational design optimization has been widely considered a promising technique to help designers address complex design challenges regarding building performance. However, a barrier to applying it to real-world projects is the difficulty in incorporating functional requirements and constraints into the design optimization process. In response, this study presents an optimization-assisted design approach for early-stage architectural design. The approach combines the application of EvoMass, an integrated building mass design generation and optimization tool, and the soft constraint strategy. The combination allows designers to integrate various design requirements and constraints into the optimization, which makes it produce results with higher practical values. To demonstrate the efficacy of the approach, two case studies are presented, which show that the application of optimization facilitates designers to better formulate the design problem and rapidly investigate different design directions for exploration and information extraction.
keywords	Generative Design, Optimization, Design Exploration, Design Process, EvoMass, Computational Design, Building Performance
series	eCAADe
email
last changed	2024/04/22 07:10

_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	ecaade2022_16
id	ecaade2022_16
authors	Bailey, Grayson, Kammler, Olaf, Weiser, Rene, Fuchkina, Ekaterina and Schneider, Sven
year	2022
title	Performing Immersive Virtual Environment User Studies with VREVAL
doi	https://doi.org/10.52842/conf.ecaade.2022.2.437
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. 437–446
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 learns the relationship between building geometry, typology, and construction type with the Global Warming potential (GWP) in tons of C02 equivalent (tCO2e). The first one, a regression model, can 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	Pre-Occupancy Evaluation, Immersive Virtual Environment, Wayfinding, User Centered Design, Architectural Study 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	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	sigradi2022_274
id	sigradi2022_274
authors	Diacodimitri, Alekos; Rebecchini, Federico
year	2022
title	Drawing with bare hands. A hand-gesture based drawing experience with motion sensors.
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. 969–980
summary	One of the features that makes analog drawing so fascinating is its manual component. The path of a graphite line on the sheet, the different pressure of the stroke, the texture described by an entropic movement, the unexpected residues of dirt, each of these things virtually refers to the intervention of a hand. This research unfolds around the idea of using the movement and gestures of the hands as a basis for the generation of forms for architectural design. Not three-dimensional models, but flat shapes, two-dimensional digital drawings generated by the author's gestures. Through a Leap Motion sensor and a digital drawing program, all the various forms of freehand drawing were explored, finding an interesting result in the field of free shape generation linked to hand gestures. The result of this experience is a different way of seeing gestures as a generative tool of architectural forms, to be used into architectural design process.
keywords	Media art, Digital drawing, Shape generation, Gesture, Motion sensors
series	SIGraDi
email
last changed	2023/05/16 16:57

_id	ecaade2022_195
id	ecaade2022_195
authors	Garcia, Sara and Leitao, António
year	2022
title	Interfaces for Design Space Exploration
doi	https://doi.org/10.52842/conf.ecaade.2022.1.331
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. 331–340
summary	A Generative Design System (GDS) allows for the generation and exploration of a wide number of design alternatives and for the automation of analysis and optimization processes. Algorithmic Design (AD) tools effectively support the development of GDSs, but they tend to be less appealing for the usage of such systems, as they rely on complex algorithmic descriptions of the design that quickly become overwhelming for less experienced programmers. The usage of GDSs is facilitated by Design Space Exploration Interfaces (DSEIs), which allows users to iteratively explore, visualize, and select design alternatives among the multidimensional design space defined by the GDS. DSEIs promote the collaboration between designers, clients, and end-users, making GDSs more interactive and more accessible. DSEIs rely on graphical user interfaces that relieve users from the burden of dealing with AD programs. The creation of such interfaces can be automated, so that they can be quickly created and modified. Although AD-based DSEIs exist for at least three decades, they have been more intensively researched and commercialized over the past eight years. In this article, existing AD-based DSEIs are reviewed, characterized, and compared according to several criteria, such as: navigation, visualization, search, ranking, grouping, filtering, and selection techniques. From this comparative study, a set of guidelines for the development of DSEIs is proposed.
keywords	Design Space Exploration, Algorithmic Design, Graphical User Interface, Data Visualization
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ijac202220202
id	ijac202220202
authors	Garcia, Sara; António Leitao
year	2022
title	Navigating Design Spaces: Finding Designs, Design Collections, and Design Subspaces
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 176–195
summary	Generative design systems can generate a wide panoply of solutions, from which designers search for those thatbest suit their interests. However, without guidance, this search can be highly inefficient, and many interestingsolutions may remain unexplored. This problem is mitigated with automated exploration methods. Still, the onestypically provided by generative design tools are mostly based on black-box methods that drastically reduce therole of the designer, while more straightforward white-box mechanisms are dispersedly found in specificapplications. This paper proposes the Navigator tool, which gathers a set of white-box mechanisms that automate the generation of default, random, similar and hybrid designs and design subspaces, while also supportingthe generation of design collections. The proposed mechanisms were tested with two generative systems thatcreate, respectively, tower and chair designs. We expect that, by providing understandable mechanisms fornavigating design spaces, designers can become more engaged in the search process
keywords	Generative design, design space exploration, randomness, hybridity, similarity
series	journal
last changed	2024/04/17 14:29

_id	ecaade2022_65
id	ecaade2022_65
authors	Halici, Süheyla Müge and Gül, Leman Figen
year	2022
title	Utilizing Generative Adversarial Networks for Augmenting Architectural Massing Studies: AI-assisted Mixed Reality
doi	https://doi.org/10.52842/conf.ecaade.2022.1.323
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. 323–330
summary	A technique for architectural massing studies in Mixed Reality (MR) is described. Generative Adversarial Networks let an object appear to have a different material than it actually has. The benefits during design are twofold. From one side the congruence between shape and material are subject to verification in real-time. From the other side, the designer is liberated from the usual restrictions and biases as to shape that are inevitable due to the mechanical properties of a mock-up. This is referred to as artificial intelligence assisted MR (AI-A MR) in this work. The technique consists of two steps: based on preparing synthetic data in Rhino/Grasshopper to be trained with an image-to- image translation model and implemented to the trained model in MR design environment. Next to the practical merits, a contribution of the work with respect to MR methodology is that it exemplifies the solution of some persistent tracking and registration problems.
keywords	Hybrid Design Environment, Dynamic Design Models, Mixed Reality, Generative Adversarial Networks, Image-to-Image Translation, Tracking
series	eCAADe
email
last changed	2024/04/22 07:10

_id	sigradi2022_115
id	sigradi2022_115
authors	Munoz, Patricia Laura
year	2022
title	Curve folding in form generation with digital fabrication
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. 1265–1276
summary	The combination of traditional morphological knowledge with digital manufacturing possibilities is fundamental for Design. Understanding how to create unthinkable shapes, when old boundaries are removed, opens generative possibilities for everyday objects. On the other hand, pre-digital knowledge provides conceptual resources to organize this new possibility. This interaction allows critical appropriation to take place. This research aims to incorporate curved folding through laser cutting as a tool to generate new forms, considering the value of pre-digital knowledge and of the benefits of digital fabrication in this area, compared to traditional cutting dies. The analysis of geometric aspects was the initial activity, defining the design variables for two kinds of patterns. Later different laminar materials were tested to determine the advantages and obstacles in each case. Finally, some of the results were implemented in products and verified in instructional activities with Industrial Design undergraduate students.
keywords	Digital Craft, Morphology, Design, Curve-folding, Materials
series	SIGraDi
email
last changed	2023/05/16 16:57

_id	ecaade2022_384
id	ecaade2022_384
authors	Naboni, Roberto, Breseghello, Luca and Sanin, Sandro
year	2022
title	Environment-Aware 3D Concrete Printing through Robot-Vision
doi	https://doi.org/10.52842/conf.ecaade.2022.2.409
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. 409–418
summary	In the 2020s, large scale 3D concrete printing (3DCP) is one of the most important areas of development for research and industry in construction automation. However, the available technology fails to adapt to the complexity of a real construction site and building process, oversimplifying design, production, and products to fit the current state of technology. We hypothesise that by equipping printing machinery with sensing devices and adaptive design algorithms we can radically expand the range of applications and effectiveness of 3DCP. In this paper we prove this concept through a full-scale design-to- fabrication experiment, SENS-ENV, consisting of three main phases: (i) we equip and calibrate an existing robotic setup for 3DCP with a camera which collects geometric data; (ii) building upon the collected information, we use environment-aware generative design algorithms to conceive a toolpath design tailored for the specific environment with a quasi-real-time workflow; (iii) we successfully prove this approach with a number of fabrication test-elements printed on unknown environment configurations and by monitoring the fabrication process to apply printing corrections. The paper describes the implementation and the successful experiments in terms of technology setup, process development, and documenting the outcomes. SENS-ENV opens a new agenda for context-aware autonomous additive construction robots.
keywords	3D Concrete Printing, Robot Vision, Environment Mapping, Adaptive Design
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ijac202220309
id	ijac202220309
authors	Okhoya, Victor W; Marcelo Bernal; Athanassios Economou; Nirvik Saha; Robert Vaivodiss; Tzu-Chieh K Hong; John Haymaker
year	2022
title	Generative workplace and space planning in architectural practice
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 3, pp. 645–672
summary	Generative design is emerging as an important approach for design exploration and design analysis in architectural practice. At the interior design scale, although many approaches exist, they do not meet many requirements for implementing generative design in practice. These requirements include the need for enduser accessible tools and skills, rapid execution, the use of standard inputs and outputs, and being scalable and reusable. In this paper, we describe a hybrid process that uses both space allocation and shape grammar algorithms to solve workplace and space planning interior design problems. Space allocation algorithms partition spaces according to program requirements while shape grammar automates the placement of inventory and the production of high-resolution drawings. We evaluate using three real world example projects how this hybrid approach meets the identified requirements of generative space planning in architectural practice
keywords	Generative design, shape grammar, space allocation, space planning
series	journal
last changed	2024/04/17 14:30

_id	ascaad2022_063
id	ascaad2022_063
authors	Ozman, Gizem; Selcuk, Semra
year	2022
title	Generating Mass Housing Plans through GANs: A case in TOKI, Turkey
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. 17-29
summary	Nowadays, Machine Learning (ML) is frequently used in almost all disciplines having an intersection with technology. Recently, architects are using existing plan data sets in architecture through Deep Learning (DL) algorithms of big data to achieve generative and non-existent plan models by using ML. Especially, Generative Adversarial Neural Networks (GANs), one of the deep learning algorithms, have been in use in the creation of generative models for architectural studies. Within the scope of this paper, architectural drawings were generated by using GANs. This generation method allows for the training of spatial layout planning to networks and for the generation of plans that do not exist in the dataset. Architectural drawings of TOKI (Housing Development Administration of the Republic of Türkiye) mass housing projects were used as datasets. In line with studies already carried out, this study attempts to create a method for further processing of the research. In this study, the differences between the plan typologies generated with raster images and the reality relations in visual productions between graph-based plan layout productions were evaluated. In this context, 157 plan datasets were obtained by multiplying plans which were spatially correlated with the RGB settings of 21 plan typologies. As a result of this research, it has been determined that the spatial layout planning of the HouseGAN algorithm provides TOK?'s current plan typologies of generation together with bubble diagrams. HouseGAN was trained using its dataset and the outputs obtained were realistic background images.
series	ASCAAD
email
last changed	2024/02/16 13:29

_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	ijac202220406
id	ijac202220406
authors	Pibal, Sophia S.; Konstantin Khoss; Iva Kovacic
year	2022
title	Framework of an algorithm-aided BIM approach for modular residential building information models
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 4, pp. 777–800
summary	The digital transformation of the construction industry and the lack of integration of digital technologies in design and construction processes are the motivation for this research. BIM solutions enable new levels of design processes and provide platforms for computational design and novel approaches in the AEC industry. In computational design parametric, generative or algorithmic procedures are utilized to support, optimize, or replace manual processes. The combination of BIM and generative, parametric or algorithmic design forms a hybrid that aims to combine the advantages of both concepts and allows for generative design processes with the creation of BIM objects containing metadata. Along with the digital transformation and novel approaches in the AEC industry, modular construction aims to shift from mass production to mass customization and maximize opportunities for cost-effective, economical, and sustainable buildings. This paper addresses the approach of generating building information models using algorithm-aided design combined with BIM at an early design stage for modular multi-story residential buildings that are affordable and sustainable. In this study, we present the framework of an algorithm-aided BIM approach, from the concept of the generative algorithm to the evaluation approach and the proof of concept as the test of the framework
keywords	Building information modeling, algorithm-aided design, algorithm-aided building information modeling, modular construction, mass customization
series	journal
last changed	2024/04/17 14:30

_id	sigradi2022_53
id	sigradi2022_53
authors	Stuart-Smith, Robert; Danahy, Patrick
year	2022
title	3D Generative Design for Non-Experts: Multiview Perceptual Similarity with Agent-Based Reinforcement 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. 115–126
summary	Advances in additive manufacturing allow architectural elements to be fabricated with increasingly complex geometrical designs, however, corresponding 3D design software requires substantial knowledge and skill to operate, limiting adoption by non-experts or people with disabilities. Established non-expert approaches typically constrain geometry, topology, or character to a pre-established configuration, rather than aligning to figural and aesthetic characteristics defined by a user. A methodology is proposed that enables a user to develop multi-manifold designs from sketches or images in several 3d camera projections. An agent-based design approach responds to computer vision analysis (CVA) and Deep Reinforcement Learning (RL) to design outcomes with perceptual similarity to user input images evaluated by Structural Similarity Indexing (SSIM). Several CVA and RL ratios were explored in training models and tested on untrained images to evaluate their effectiveness. Results demonstrate a combination of CVA and RL motion behavior can produce meshes with perceptual similarity to image content.
keywords	Generative Design, Machine Learning, Agent-Based Systems, Non-Expert Design
series	SIGraDi
email
last changed	2023/05/16 16:55

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