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_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	ecaade2022_273
id	ecaade2022_273
authors	Zhuang, Xinwei
year	2022
title	Rendering Sketches - Interactive rendering generation from sketches using conditional generative adversarial neural network
doi	https://doi.org/10.52842/conf.ecaade.2022.1.517
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. 517–524
summary	Architects use sketches in the early design phase to organize and elaborate their initial ideas, and those initial sketches often support ideation for the final design. However, the sketches in the early design phase tend to be abstract and hard to interpret. Minimal prior works provide tools for quick visualization of the initial sketch. This study provides a scheme for architects and designers to generate preliminary renderings in the early design stage. In this study, we use conditional generative adversarial networks (cGAN) as the frame and introduces an updater network to the existing cGAN to support the iterative design process. A sketch serves as input to see the rendering and update the sketch based on the generated renderings by adding more resolution and details. The network is able to generate a reasonable rendering from the single-image network, and is able to update the renderings iteratively via the updater network. The dataset is collected from residential buildings exclusively, but the architectural categories can be expanded to other types of buildings in the future. Results show that the proposed scheme is able to provide reasonable renderings from sketches, and the generated rendering can be updated with a higher level of details within a second if the user provides a more detailed sketch. The contribution of this study includes introducing an updater network to the existing algorithm to enable iterative input and provides an alternative enhancement approach to the resolution of the generated image.
keywords	Computer Aided Design, Early Design Phase, Conditional Generative Adversarial Neural Network, Human Computer Interaction
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2024_186
id	caadria2024_186
authors	Huang, Jingfei and Tu, Han
year	2024
title	Inconsistent Affective Reaction: Sentiment of Perception and Opinion in Urban Environments
doi	https://doi.org/10.52842/conf.caadria.2024.2.395
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 395–404
summary	The ascension of social media platforms has transformed our understanding of urban environments, giving rise to nuanced variations in sentiment reaction embedded within human perception and opinion, and challenging existing multidimensional sentiment analysis approaches in urban studies. This study presents novel methodologies for identifying and elucidating sentiment inconsistency, constructing a dataset encompassing 140,750 Baidu and Tencent Street view images to measure perceptions, and 984,024 Weibo social media text posts to measure opinions. A reaction index is developed, integrating object detection and natural language processing techniques to classify sentiment in Beijing Second Ring for 2016 and 2022. Classified sentiment reaction is analysed and visualized using regression analysis, image segmentation, and word frequency based on land-use distribution to discern underlying factors. The perception affective reaction trend map reveals a shift toward more evenly distributed positive sentiment, while the opinion affective reaction trend map shows more extreme changes. Our mismatch map indicates significant disparities between the sentiments of human perception and opinion of urban areas over the years. Changes in sentiment reactions have significant relationships with elements such as dense buildings and pedestrian presence. Our inconsistent maps present perception and opinion sentiments before and after the pandemic and offer potential explanations and directions for environmental management, in formulating strategies for urban renewal.
keywords	Urban Sentiment, Affective Reaction, Social Media, Machine Learning, Urban Data, Image Segmentation.
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	cdrf2022_199
id	cdrf2022_199
authors	Jingming Li
year	2022
title	Using Text Understanding to Create Formatted Semantic Web from BIM
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_17
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	The application of BIM in the building life cycle needs to be continuous. The information collected and accumulated in the early stages should flow to the subsequent phases. However, BIM applications currently focus on collision inspection, compliance inspection, and engineering calculation, few models can be successively used in the following stages. Remodeling is required in the operation and maintenance period, resulting in waste. Meanwhile, some of the information accumulated by BIM might be frequently used in the operation and maintenance stage, while some data are relatively rarely used. The semantic web can help manage building information at all stages. But the generation of a semantic web is mostly manually completed. It is necessary to standardize the repeated semantic description in the model and convert BIM into a standard semantic model for information indexing, reducing the resource consumption of model loading and optimizing the efficiency of the operation and maintenance system. When the existing research transforms from BIM to the semantic web, there will be a lack of information and descriptions of the ownership relationship between entities due to the limitation of formats. To realize the standard transformation from BIM to the semantic web, this work proposes a method of using Natural Language Processing (NLP) to understand the text and infer the relationship between entities according to the knowledge map. First, the entities are extracted from BIM, such as air conditioning unit, electric lamp, fan, etc., if the name of the extracted entity is irregular, the names are translated with the help of NLP and Ontology (such as brick or haystack) to obtain the standard definition. By comparing the complete knowledge graph (such as the knowledge graph of the air conditioning system), the relationships can be deduced, and then a standardized semantic model can be generated.
series	cdrf
email
last changed	2024/05/29 14:02

_id	ijac202220303
id	ijac202220303
authors	Kirdar, Gulce; Gulen Cagdas
year	2022
title	A decision support model to evaluate liveability in the context of urban vibrancy
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 3, pp. 528–552
summary	Liveability can be accepted as an umbrella term covering all the factors that make a place to live. We recognize the versatility of urban liveability and focus on the vibrancy aspect. Regarding the literature, we compile variables affecting urban liveability under the economic, image, and use value of place. This article aims to present a data-driven decision support system to evaluate different dimensions of vibrancy-focused liveability. We adopt a knowledge discovery process to handle the complexity of the liveability concept. This study develops a conditional-based relationship network of vibrancy parameters through the Bayesian Belief Network (BBN). Then, we assess the BBN’s correlations with statistics and causal relations with the survey in this study.These results mostly agree with the findings of the relevant literature. The economic value results show that the high density, diversity and accessibility add a premium to the land value of properties. The use value results also demonstrate that the diversity and density of activities, cultural attributes, and high accessibility support place attractiveness. The selected streetscape variables improve image value, except for building enclosure and condition. The study has the potential for urban planners to vitalize neighborhoods by considering urban activities and urban physical attributes
keywords	liveability, vibrancy, knowledge discovery process, big data, locative data, Bayesian belief network
series	journal
last changed	2024/04/17 14:29

_id	caadria2022_42
id	caadria2022_42
authors	Chen, Jielin and Stouffs, Rudi
year	2022
title	Robust Attributed Adjacency Graph Extraction Using Floor Plan Images
doi	https://doi.org/10.52842/conf.caadria.2022.2.385
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. 385-394
summary	Architectural design solutions are intrinsically structured information with a broad range of interdependent scopes. Compared to conventional 2D Euclidean data such as orthographic drawings and perspectives, non-Euclidean data (e.g., attributed adjacency graphs) can be more effective and accurate for representing 3D architectural design information, which can be useful for numerous design tasks such as spatial analysis and reasoning, and practical applications such as floor plan parsing and generation. Thus, getting access to a matching attributed adjacency graph dataset of architectural design becomes a necessity. However, the task of conveniently acquiring attributed adjacency graphs from existing architectural design solutions still remains an open challenge. To this end, this project leverages state-of-the-art image segmentation techniques using an ensemble learning scheme and proposes an end-to-end framework to efficiently extract attributed adjacency graphs from floor plan images with diverse styles and varied levels of complexity, aiming at addressing generalization issues of existing approaches. The proposed graph extraction framework can be used as an innovative tool for advancing design research infrastructure, with which we construct a large-scale attributed adjacency graph dataset of architectural design using floor plan images retrieved in bulk. We have open sourced our code and dataset.
keywords	attributed adjacency graph, floor plan segmentation, ensemble learning, architectural dataset, 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_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	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	architectural_intelligence2022_9
id	architectural_intelligence2022_9
authors	Roland Snooks
year	2022
title	Behavioral tectonics: agentBody prototypes and the compression of tectonics
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00007-8
source	Architectural Intelligence Journal
summary	This research demonstrates the development of a tectonic approach to architecture through an experimental, iterative methodology. It is a synthetic approach where tectonics and form are engaged in a non-hierarchical negotiation. An architecture where expression, ornament, structure and their spatial consequences are intertwined and inseparable. The design research posited here has been conducted over the past nine years through the sustained development of a series of architectural tectonic experiments called the agentBody Prototypes. These prototypes reify an ambition to compress surface, structure and ornament into a single irreducible assemblage. The agentBody Prototypes are a series of fourteen proto-architectural projects, or fragments, with lead design by Roland Snooks, and research, development and fabrication by the RMIT Architecture \| Tectonic Formation Lab. The paper describes the wider context of this work and includes a brief chronological overview of this trajectory, followed by a series of observations drawn from critical reflection. This paper attempts to draw out the architectural design implications that have emerged through a specific interaction of algorithmic design, and robotic fabrication.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	caadria2022_139
id	caadria2022_139
authors	Ataman, Cem, Tuncer, Bige and Perrault, Simon
year	2022
title	Asynchronous Digital Participation in Urban Design Processes: Qualitative Data Exploration and Analysis With Natural Language Processing
doi	https://doi.org/10.52842/conf.caadria.2022.1.383
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. 383-392
summary	This paper aims to improve the usability of qualitative urban big data sources by utilizing Natural Language Processing (NLP) as a promising AI-based technique. In this research, we designed a digital participation experiment by deploying an open-source and customizable asynchronous participation tool, "Consul Project‚, with 47 participants in the campus transformation process of the Singapore University of Technology and Design (SUTD). At the end of the data collection process with several debate topics and proposals, we analysed the qualitative data in entry scale, topic scale, and module scale. We investigated the impact of sentiment scores of each entry on the overall discussion and the sentiment scores of each introduction text on the ongoing discussions to trace the interaction and engagement. Furthermore, we used Latent Dirichlet Allocation (LDA) topic modelling to visualize the abstract topics that occurred in the participation experiment. The results revealed the links between different debates and proposals, which allow designers and decision makers to identify the most interacted arguments and engaging topics throughout participation processes. Eventually, this research presented the potentials of qualitative data while highlighting the necessity of adopting new methods and techniques, e.g., NLP, sentiment analysis, LDA topic modelling, to analyse and represent the collected qualitative data in asynchronous digital participation processes.
keywords	Urban Design, Digital Participation, Qualitative Urban Data, Natural Language Processing (NLP), Sentiment Analysis, LDA Topic Modelling, SDG 10, SDG 11.
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ijac202220310
id	ijac202220310
authors	Castro Henriques, Goncalo; Pedro Maciel Xavier; Victor de Luca Silva; Luca Rédua Bispo; Joao Victor Fraga
year	2022
title	Computation for Architecture, hybrid visual and textual language: Research developments and considerations about the implementation of structural imperative and object-oriented paradigms
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 3, pp. 673–687
summary	In the fourth industrial revolution, programming promises to be a fundamental subject like mathematics, science, languages or the arts. Architects design more than buildings developing innovative methods and they are among the pioneers in visual programming development. However, after more than 10 years of visual programming in architecture, despite the fast-learning curve, visual programming presents considerable limitations to solve complex problems. To overcome limitations, the authors propose to associate the advantages of visual and textual languages in Python. The article addresses an ongoing research study to implement Computational Methods in Architectural Education. The authors began by describing the general goal of this project, and of this article in particular. This article focuses on the implementation of two disciplines ‘Computation for Architecture in Python’ I and II. The first discipline uses programming based on the construction of functions in the imperative language, implemented in the text editor, in visual programming, using Grasshopper methods. The second discipline, which is under development, intends to teach object-oriented programming. The results of the first discipline are encouraging; despite reported difficulties in programming fundamentals, such as lists, loops and recursion. The development of the second discipline, in object-oriented programming, deals with the concepts of classes and objects, and more abstract principles such abstraction, inheritance, polymorphism or encapsulation. This paradigm allows building robust programs, but requires a more in-depth syntax. The article reports this ongoing research on this new paradigm of object-oriented language, expanding the application of a hybrid visual-textual language in Architecture
keywords	computation, textual programming, visual programming, imperative programming, object oriented programming
series	journal
last changed	2024/04/17 14:30

_id	architectural_intelligence2022_12
id	architectural_intelligence2022_12
authors	Matias del Campo
year	2022
title	Deep House - datasets, estrangement, and the problem of the new
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00013-w
source	Architectural Intelligence Journal
summary	The purpose of this article is to discuss the application of artificial intelligence (AI) in the design of the Deep House project (Fig. 1), an attempt to use estrangement as a method to emancipate a house from a canonical approach to the progressive design of a one-family house project. The main argument in this text is that the results created by Artificial Neural Networks (ANNs), whether in the form of GANs, CNNs, or other networks, generate results that fall into the category of Estranged objects. In this article, I would like to offer a possible definition of what architecture in this plateau of thinking represents and how it differentiates from previous attempts to use estrangement to explain the phenomena observed when working with NNs in architecture design. A potpourri of thoughts that demonstrate the intellectual tradition of exploring estrangement, especially in theater and literature, that ultimately circles back to its implications for architecture, particularly in light of the application of AI.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	ascaad2022_044
id	ascaad2022_044
authors	Shah, Syed; Petzold, Frank
year	2022
title	Research Data Management and a System Design to Semi-Automatically Complete Integrated Data Management Plans [Position Paper]
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. 577-593
summary	Data is an integral part of modern scientific work. Good research data management (RDM) and the communication of the related information is extremely an important matter. It is not only crucial for the ongoing research and its claims but also for the future uses of data. In recent years some guiding principles, e.g. FAIR principles and initiatives at the national and international level, e.g. NFDI, NFDI4Ing have also been founded to improve RDM. The data and its metadata are often handled in file system like structures which are versioned and logged. The information relating to the data handling are documented in data management plan (DMP). DMPs are also usually managed in similar file structures. These are made available in editable document formats as well as online free-text editable forms to which users are required to keep updating manually. These are isolated documents which have neither direct relation to data for verification nor are common to understand with consistency. In this paper, research data management of large-scale interdisciplinary projects is presented. On one hand it introduces, contemporary practices of RDM and on the other hand it helps researchers to determine the features of RDM system in the situations when it comes to select or develop a system for the same purpose. It further introduces a system design for semi-automatic completion of DMP functions in collaborative environment a.k.a. virtual research environment (VRE). It is assumed that the proposed system will assist and enable users to update semi-automatically integrated DMP during all phases of data life cycle. Direct relation to the data for verification, common understanding and consistency will also be maintainable.
series	ASCAAD
email
last changed	2024/02/16 13:29

_id	ijac202321201
id	ijac202321201
authors	Steinfeld, Kyle
year	2023
title	Clever little tricks: A socio-technical history of text-to-image generative models
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 2, 211–241
summary	The emergence of text-to-image generative models (e.g., Midjourney, DALL-E 2, Stable Diffusion) in the summer of 2022 impacted architectural visual culture suddenly, severely, and seemingly out of nowhere. To contextualize this phenomenon, this text offers a socio-technical history of text-to-image generative systems. Three moments in time, or “scenes,” are presented here: the first at the advent of AI in the middle of the last century; the second at the “reawakening” of a specific approach to machine learning at the turn of this century; the third that documents a rapid sequence of innovations, dubbed “clever little tricks,” that occurred across just 18 months. This final scene is the crux, and represents the first formal documentation of the recent history of a specific set of informal innovations. These innovations were produced by non-affiliated researchers and communities of creative contributors, and directly led to the technologies that so compellingly captured the architectural imagination in the summer of 2022. Across these scenes, we examine the technologies, application domains, infrastructures, social contexts, and practices that drive technical research and shape creative practice in this space.
keywords	Machine learning, text-to-image, socio-technical study, generative AI
series	journal
last changed	2024/04/17 14:30

_id	sigradi2022_258
id	sigradi2022_258
authors	Taºdelen, Hanife Sümeyye; Gül, Leman Figen
year	2022
title	The analysis of architectural discourse in the context of computational public opinion: Data mining of Google map reviews
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. 101–112
summary	New media platforms and mapping tools are created by digital communities, and their representations influence public opinion. Crowdsourcing platforms such as Twitter, Instagram, Google search engines and Maps have no such limitations or boundaries as in the physical space, these platforms are creating new virtual public places. To assess and forecast the accessibility and aesthetic issues of urban spaces in Istanbul, text and image data from Google Maps were employed. In this study, we searched at the reviews of certain public/semi-public places by using text mining and image analytics tools. Recently designed or renovated 11 public buildings and open places were chosen. The main findings of this exploratory study are that; 1) the level of being public can be understood from the crowdsourcing, 2) image analytics of crowdsourced visual data can assist to identify the aesthetic quality, and 3) the accessibility capacity of public spaces can be identified.
keywords	Data Analytics, Public Spaces, Architectural Criticism, Collaborative Map, Accessibility-Aesthetic issues
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	sigradi2022_220
id	sigradi2022_220
authors	Torreblanca-Díaz, David A.
year	2022
title	Biodigital Product Design Through Additive Fabrication Technologies: Beer Tap Handles Project
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. 1225–1236
summary	Biomimicry is a new transdisciplinary science that studies the models of nature to solve human problems with a systemic approach; design based on nature has had a significant evolution in recent decades thanks to digital technologies advantage, especially digital fabrication and parametric software. This text presents the process of design, experimentation and fabrication of beer tap handles series based on morphological patterns from nature. The project followed this methodological sequence (1) Design problem (2) Selection of biological referents (3) Morphologic synthesis (4) Analysis of thicknesses and stress (5) Detailed design (6) Fabrication of 1:1 scale prototype through Fused Deposition Modelling technology -FDM- (7) User testing (8) Conclusions and improvement proposal. The digital design and fabrication process were effective, the prototypes worked and reached the project goals, the users perceived that the beer tap handles are comfortable, functional and have an attractive appearance.
keywords	Biomimicry, Bio-informed disciplines, Parametric design, Additive fabrication technologies, Fused deposition modelling technology
series	SIGraDi
email
last changed	2023/05/16 16:57

_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	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	caadria2022_411
id	caadria2022_411
authors	Yang, Xuyou, Bao, Ding Wen, Yan, Xin and Zhao, Yucheng
year	2022
title	OptiGAN: Topological Optimization in Design Form-Finding With Conditional GANs
doi	https://doi.org/10.52842/conf.caadria.2022.1.121
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. 121-130
summary	With the rapid development of computers and technology in the 20th century, the topological optimisation (TO) method has spread worldwide in various fields. This novel structural optimisation approach has been applied in many disciplines, including architectural form-finding. Especially Bi-directional Evolutionary Structural Optimisation (BESO), which was proposed in the 1990s, is widely used by thousands of engineers and architects worldwide to design innovative and iconic buildings. To integrate topological optimisation with artificial intelligence (AI) algorithms and to leverage its power to improve the diversity and efficiency of the BESO topological optimisation method, this research explores a non-iterative approach to accelerate the topology optimisation process of structures in architectural form-finding via conditional generative adversarial networks (GANs), which is named as OptiGAN. Trained with topological optimisation results generated through Ameba software, OptiGAN is able to predict a wide range of optimised architectural and structural designs under defined conditions.
keywords	BESO (bi-directional evolutionary structural optimisation), Artificial Intelligence, Deep Learning, Topological Optimisation, Form-Finding, GAN (Generative Adversarial Networks), SDG 12, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

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