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_251
id	ecaade2022_251
authors	Awan, Abeeha, Lombardi, Davide, Ruffino, Paolo and Agkathidis, Asterios
year	2022
title	Efficacy of Gamification on Introductory Architectural Education: a literature review
doi	https://doi.org/10.52842/conf.ecaade.2022.2.553
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. 553–564
summary	Due to their recent popularity and success in fields such as engineering and business, gamification and by extension game design principles demonstrate the ability to teach complex, multi-disciplinary skills in an engaging, entertaining, and effective way. Architectural education especially introductory architectural education is a foundational and fundamental part of a budding architecture student’s career and oftentimes requires the understanding of dynamic systems, spatial reasoning, and experiential learning. The paper posits that gamification and game design principles can utilize certain components such as augmented reality, narrative design, and fun in order to create tools, gamify existing curriculum, and increase retention, engagement, and mastery of the difficult high-tech skillsets required of introductory architects. The paper focuses on reviewing and systematically analyzing research on gamification in education. In particular, it focuses on systematically reviewing and analyzing data from multiple relevant case studies chosen based on the application of technology such as augmented reality, the integration of game design, and the feasibility of gamification in educational environments. This data is examined based on feasibility, accessibility, and effects on information retention and the findings are outlined in a comparative table of methods, tools, and technologies organized based on their suitability. Ultimately, the paper aims to establish a framework for gamifying introductory modules in architectural education and hopes to create a future architectural augmented reality game meant to utilize gamification to help new architectural students.
keywords	Gamification, Game Design, Architectural Education, Educational Games, Retention, Learning
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_166
id	caadria2022_166
authors	Eisenstadt, Viktor, Bielski, Jessica, Mete, Burak, Langenhan, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2022
title	Autocompletion of Floor Plans for the Early Design Phase in Architecture: Foundations, Existing Methods, and Research Outlook
doi	https://doi.org/10.52842/conf.caadria.2022.1.323
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. 323-332
summary	This paper contributes the current research state and possible future developments of AI-based autocompletion of architectural floor plans and shows demand for its establishment in computer-aided architectural design to facilitate decent work, economic growth through accelerating the design process to meet the future workload. Foundations of data representations together with the autocompletion contexts are defined, existing methods described and evaluated in the integrated literature review, and criteria for qualitative and sustainable autocompletion are proposed. Subsequently, we contribute three unique deep learning-based autocompletion methods currently in development for the research project metis-II. They are described in detail from a technical point of view on the backdrop of how they adhere to the proposed criteria for creating our novel AI.
keywords	Artificial Intelligence, Architectural Design, Floor Plan, Autocompletion, SDG 8, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_47
id	ecaade2022_47
authors	Marsillo, Laura, Suntorachai, Nawapan, Karthikeyan, Keshava Narayan, Voinova, Nataliya, Khairallah, Lea and Chronis, Angelos
year	2022
title	Context Decoder - Measuring urban quality through artificial intelligence
doi	https://doi.org/10.52842/conf.ecaade.2022.2.237
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 237–246
summary	Understanding the quality of places during the early design process can improve design decision making and increase not only the chance of effective site development for the place and surroundings but also provide foresight to the mental, physical and environmental well-being of the future occupants. A context can be described differently depending on the designer's studies. However, in order to view the place holistically, various layers should be considered for a cross-disciplinary correlation. This paper proposes a prototypical tool to evaluate the quality of places using machine learning to help cluster and visualise design metrics according to the features provided. By selecting a location in a city, it offers other site contexts with similar characteristics and a similar level of complexity in relation to the surroundings. The tool was initially developed for Naples (Italy) as a case study city and incorporates key indicators related to connectivity of amenities, walkability, urban density, population density, outdoor thermal comfort, popular rate review and sentiment analysis from social media. With current open-source data, these indicators such as OpenStreetMap or social media sentiment can be collected with embedded geotags. These site-specific multilayers were evaluated under the metrics of 3 ranges i.e 400, 800 and 1,200-metre walking distance. This paper demonstrates the potential of using machine learning integrated with computational design tools to visualise the otherwise invisible data for users to interpret any context comprehensively in a holistic approach. Even though this tool is made for Naples, this tool can be extended to other cities across the world. As a result, the tool assists users in understanding not only site-specific location but also draws lines to other neighbourhoods within the city with a similar phenomenon of correlation between key performance indicators.
keywords	Computational Design, Urban Analysis, Machine Learning, Computer Vision, Sentiment Analysis
series	eCAADe
email
last changed	2024/04/22 07:10

_id	cdrf2022_326
id	cdrf2022_326
authors	Zidong Liu, Yan Li, and Xiao Xiao
year	2022
title	Predicting the Vitality of Stores Along the Street Based on Business Type Sequence via Recurrent Neural Network
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_29
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	The rational planning of store types and locations to maximize street vitality is essential in real estate planning. Traditional business planning relies heavily on the subjective experience of developers. Currently, developers have access to low-resolution urban data to support their decision making, and researchers have done much image-based machine learning research from the scale of urban texture. However, there is still a lack of research on the functional layout with shop-level accuracy. This paper uses a sequence-based neural network (RNN) to explore the relationship between the sequence of store types along a street and its commercial vitality. Currently, the use of RNNs in the architectural and urban fields is very rare. We use customer review data of 80streets from O2O platforms to represent the store vitality degree. In the machine learning model, the input is the sequence of store types on the street, and the output is the corresponding sequence of business vitality indexes. After training and evaluation, the model was shown to have acceptable accuracy. We further combined this evaluation model with a genetic algorithm to develop a business planning optimization tool to maximize the overall street business value, thus guiding real estate business planning at a high resolution.
series	cdrf
email
last changed	2024/05/29 14:03

_id	caadria2022_277
id	caadria2022_277
authors	Akbar, Zuardin, Wood, Dylan, Kiesewetter, Laura, Menges, Achim and Wortmann, Thomas
year	2022
title	A Data-Driven Workflow for Modelling Self-Shaping Wood Bilayer, Utilizing Natural Material Variations with Machine Vision and Machine Learning
doi	https://doi.org/10.52842/conf.caadria.2022.1.393
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. 393-402
summary	This paper develops a workflow to train machine learning (ML) models with a small dataset from physical samples to predict the curvatures of self-shaping wood bilayers based on local variations in the grain. In contrast to state-of-the-art predictive models, specifically 1.) a 2D Timoshenko model and 2.) a 3D numerical model with a rheological model, our method accounts for natural and unavoidable material variations. In this paper, we only focus on local grain variations as the main driver for curvatures in small-scale material samples. We extracted a feature matrix from grain images of active and passive layers as a Grey Level Co-Occurrence Matrix and used it as the input for our ML models. We also analysed the impact of grain variations on the feature matrix. We trained and tested several tree-based regression models with different features. The models achieved very accurate predictions for curvatures in each sample (R;0.9) and extend the range of parameters that is incalculable by a Timoshenko model. This research contributes to the material-efficient design of weather-responsive shape-changing wood structures by further leveraging the use of natural material features and explainable data-driven modelling and extends the topic in ML for material behaviour-driven design among the CAADRIA community.
keywords	data-driven model, machine learning, material programming, smart material, timber structure, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2023_270
id	sigradi2023_270
authors	Asevedo, Laíze, Monteiro, Verner, Medeiros, Deisyanne, Rodrigues, Fernanda, Moura, Marcone and Rocha, Thuany
year	2023
title	Parameterization and Gamification in Descriptive Geometry Learning: One Study, Two Scenarios
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 1047–1058
summary	Despite the complexity of parametric modeling, it is possible to apply it in educational context with a simpler approach. The COVID-19 pandemic increased the use of active methodologies in education. Gamification, particularly, should be emphasized regarding its association with parametric modeling. Post-pandemic scenario made possible the reinsertion of traditional practices, thus adding successful learning methods from online teaching. This paper aims to compare the adoption of parameterization and gamification to teach Descriptive Geometry on both teaching scenarios: online and presential. Two experiments were implemented to four Technical Drawing classes - A and B (2021), C and D (2022) -, in high school and technician level. The quantitative results addressed to the efficiency of parametric modeling as a didactic tool, and the qualitative results indicated that the students accepted the experiences of parameterization and gamification, on both scenarios. Nevertheless, there were subtle differences between the results from online and presential scenarios.
keywords	Online learning, presential learning, parameterization, gamification, descriptive geometry
series	SIGraDi
email
last changed	2024/03/08 14:08

_id	sigradi2022_26
id	sigradi2022_26
authors	Asevedo, Laíze; Medeiros, Deisyanne; Paixao, Helder; Pires, Marcelle
year	2022
title	Parameterization and active methodologies in the online learning of Descriptive Geometry
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. 1031–1042
summary	Descriptive Geometry demands abstraction and spatial visualization. In this case, parameterization can be useful as a supporting tool. However, in an online learning scenario, where the students have no access to software, it is necessary to re-think and adapt the use of parametric simulation. The active character from the parametric modeling manipulation can be compensated by adopting active teaching-learning methodologies. This didactic experimental study was applied to two classes in a technical and high school education context, focusing on two subjects: points and lines in descriptive geometry. The study intends to analyze the application of parametrization and active methodologies, mostly gamification, in the online learning of Descriptive Geometry. The results indicate that it is still possible and efficient to apply parametric modeling in online learning articulating with active methodologies, and it hence improves the teaching-learning process.
keywords	Online learning, Parameterization, Active teaching-learning methodologies, Descriptive Geometry, Didactic experiment
series	SIGraDi
email
last changed	2023/05/16 16:57

_id	ecaade2022_218
id	ecaade2022_218
authors	Bank, Mathias, Sandor, Viktoria, Schinegger, Kristina and Rutzinger, Stefan
year	2022
title	Learning Spatiality - A GAN method for designing architectural models through labelled sections
doi	https://doi.org/10.52842/conf.ecaade.2022.2.611
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. 611–619
summary	Digital design processes are increasingly being explored through the use of 2D generative adversarial networks (GAN), due to their capability for assembling latent spaces from existing data. These infinite spaces of synthetic data have the potential to enhance architectural design processes by mapping adjacencies across multidimensional properties, giving new impulses for design. The paper outlines a teaching method that applies 2D GANs to explore spatial characteristics with architectural students based on a training data set of 3D models of material-labelled houses. To introduce a common interface between human and neural networks, the method uses vertical slices through the models as the primary medium for communication. The approach is tested in the framework of a design course.
keywords	AI, Architectural Design, Materiality, GAN, 3D, Form Finding
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ijac202220204
id	ijac202220204
authors	BuHamdan, Samer; Aladdin Alwisy; Thomas Danel; Ahmed Bouferguene; Zoubeir Lafhaj
year	2022
title	The use of reinforced learning to support multidisciplinary design in the AEC industry: Assessing the utilization of Markov Decision Process
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 216–237
summary	While the design practice in the architecture, engineering, and construction (AEC) industry continues to be acreative activity, approaching the design problem from a perspective of the decision-making science hasremarkable potentials that manifest in the delivery of high-performing sustainable structures. These possiblegains can be attributed to the myriad of decision-making tools and technologies that can be implemented toassist design efforts, such as artificial intelligence (AI) that combines computational power and data wisdom.Such combination comes to extreme importance amid the mounting pressure on the AEC industry players todeliver economic, environmentally friendly, and socially considerate structures. Despite the promisingpotentials, the utilization of AI, particularly reinforced learning (RL), to support multidisciplinary designendeavours in the AEC industry is still in its infancy. Thus, the present research discusses developing andapplying a Markov Decision Process (MDP) model, an RL application, to assist the preliminary multidisciplinary design efforts in the AEC industry. The experimental work shows that MDP models can expediteidentifying viable design alternatives within the solutions space in multidisciplinary design while maximizingthe likelihood of finding the optimal design
keywords	Design evaluation, multidisciplinary design, reinforced learning, Markov Decision Process, social impact,architecture, engineering, and construction industry
series	journal
last changed	2024/04/17 14:29

_id	sigradi2022_246
id	sigradi2022_246
authors	Bustos Lopez, Gabriela; Aguirre, Erwin
year	2022
title	Walking the Line: UX-XR Design Experiment for Ephemeral Installations in Pandemic Times
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. 699–710
summary	Throughout COVID 19 Pandemic since 2020, it was necessary to generate instructional strategies including digital platforms for creative processes in architecture. This article exposes an experience that integrates pedagogical, operational, and technical dimensions in architecture virtual teaching. A pedagogical methodology was designed and implemented, fusing User Experience (UX) and Extended Reality (XR) during the architectural design process in a virtual experimental studio. The use of UX-XR as a designing-reviewing strategy in architecture, positively impacted the creative experience of both students and reviewers by enriching the perception of the space and interactively simulating the user experience. A friendly, fun, and socially inclusive environment was generated for learning architecture using synthetic media and Multiuser Virtual Environments (MUVEs). The successful results of the students’ projects by phase are shown, revealing the significance of combining UX and XR, incorporating the metaverse as a canvas to review, recreate, interact, and assess architectural designs.
keywords	User Experience (UX), Extended Reality (XR), Multiuser Virtual Environments (MUVE), Virtual Campus, Usability
series	SIGraDi
email
last changed	2023/05/16 16:56

_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	caadria2022_386
id	caadria2022_386
authors	Cheng, Nancy and Riggio, Mariapaola
year	2022
title	Learning Timber Tectonics through Digital Collaboration
doi	https://doi.org/10.52842/conf.caadria.2022.2.345
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. 345-354
summary	A dual university collaboration challenges students in architecture, wood science and engineering to partner on a timber design and detailing project. Five years of student projects reveal how the mix of backgrounds, design media abilities and design development process impact the learning experience. Team design submissions, individual reflections and observed collaboration activities were analysed along with patterns of design ideation, transformation, and conflict resolution. Learning experiences vary according to the mix of student backgrounds and roles within the team. Teams mixing students of different levels and backgrounds on average performed better than more homogenous teams. While beginners do not have the skills to play a central role in the team, they have the most to learn from more advanced students. Keeping all team members engaged may require giving up some efficiency of a streamlined digital workflow.
keywords	Digital Collaboration, Architectural Education, Integrated Design, SDG 12, SDG17
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	caadria2022_325
id	caadria2022_325
authors	Cui, Qinyu, Zhang, Shuyu and Huang, Yiting
year	2022
title	Retail Commercial Space Clustering Based on Post-carbon Era Context: A Case Study of Shanghai
doi	https://doi.org/10.52842/conf.caadria.2022.1.515
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. 515-524
summary	In the post-carbon era, it has become a development and research trend on adjusting commercial locations to help achieve resource conservation by using big data. This paper uses multi-source urban data and machine learning to make reasonable evaluations and adjustments to commercial district planning. Many relevant factors are affecting urban commercial agglomeration, but how to select the appropriate ones among the many factors is a problem to be considered and studied, while there may be spatial differences in the strength of each influencing factor on commercial agglomeration. Therefore, this paper takes Shanghai, a city with a high economic and commercial development level in China, as an example and identifies the influencing factors through a literature review. Next, this paper uses the machine learning BORUTA algorithm of features selection to screen the influencing factors. It then uses multi-scale geographically weighted regression model (MGWR) to analyse the spatial heterogeneity of factors affecting retail spatial agglomeration. Finally, based on the background of the changing transportation modes and the unchanged social activities in the post-carbon era, the future spatial planning pattern of retail commercial space is discussed to provide particular suggestions for the future location adjustment of urban commerce.
keywords	Business District Hierarchy, Agglomeration Effect, Spatial Variability, Multi-scale Geographically Weighted Regression Model, Machine Learning, Big Data Analysis, SDG 8, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	cdrf2022_3
id	cdrf2022_3
authors	Deli Liu and Keqi Wang
year	2022
title	Spatial Analysis of Villages in Jilin Province Based on Space Syntax and Machine Learning
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_1
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	The development of machine learning technology gives architects and urban planners a new tool that can be used for research and design. The topic of this paper is to analyze the rural space of Jilin Province with the machine learning algorithms and space syntax theory, and to obtain the inherent formation and development laws of rural spatial forms, which can be used as a reference and evaluation system for subsequent rural development, and also can emphasize the locality and continuity of rural development. First, based on geographic information data, researching the connection between the distribution of villages and geographic data at a macro level and to classify them. Then, from each category, selecting one township and use all villages in its area as samples for the more specific study. Spatial features of individual village are extracted based on space syntax theory, and representative spatial features which can as feature values for cluster analysis are selected through comparative analysis. Then classify villages from high-dimensional data and explore their type characteristics. Finally, we hope the result of this study can help provide useful theoretical references for rural construction and nature conservation in the future.
series	cdrf
email
last changed	2024/05/29 14:02

_id	caadria2022_152
id	caadria2022_152
authors	Deshpande, Rutvik, Nisztuk, Maciej, Cheng, Cesar, Subramanian, Ramanathan, Chavan, Tejas, Weijenberg, Camiel and Patel, Sayjel Vijay
year	2022
title	Synthetic Machine Learning for Real-time Architectural Daylighting Prediction
doi	https://doi.org/10.52842/conf.caadria.2022.1.313
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. 313-322
summary	"Synthetic Machine Learning‚ offers a revolutionary leap in real-time environmental analysis for conceptual architectural design. By integrating automatic synthetic data generation, artificial neural network (ANN) training and online deployment, Synthetic Machine Learning offers two main advantages over conventional simulation; First, it reduces the analysis time for a reference simulation from minutes to seconds; Second, it is possible to deploy ANN as a web service in an online design environment, which therein increases accessibility, significantly reducing simulation costs and setup time. The application of Synthetic Machine Learning to perform Daylight Autonomy (DA) and Spatial Daylight Autonomy (sDA) studies to maximise building daylighting for a given use, window to wall ratio, and floorplan arrangement is showcased through a preliminary demonstration work. Comparatively the use of algorithmically generated synthetic data versus real-world data is becoming ubiquitous in other disciplines, the advantages of this approach to the building design process are further discussed.
keywords	Daylight Autonomy, machine learning, building energy performance, synthetic data-sets, SDG 7, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_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_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	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_99
id	ecaade2022_99
authors	Hemmerling, Marco and Salzberger, Max
year	2022
title	InterACT – Laboratory for architecture, crafts, technology
doi	https://doi.org/10.52842/conf.ecaade.2022.1.557
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. 557–566
summary	The InterACT research project focuses on the use of computational design and manufacturing methods in the construction of self-build projects based on wooden structures. The goal is the interdisciplinary development and realization of a prototypical laboratory on the university campus in Cologne. At the intersection of craftsmanship and architecture, the project aims to generate, collect and share interdisciplinary knowledge. The InterACT Lab is intended to function as a hybrid learning and research space, uniting theory and practice. Moreover, the project should make the concept of networked learning and research visible beyond the academic boundaries. The entire development of the project has been set-up as a participative and collaborative learning process, involving students in the conceptual design, decision making and the production of the building components as well as in the assembly of the structure, using digital tools as a common base and connector throughout the process. The paper presents the didactic concept and discusses the findings of the various steps from the early design phase to the realization of a first prototype in scale 1:1.
keywords	Didactics, Architectural Curriculum, Design Build Projects, Open Educational Resources (OER), Wood Construction, Digital Fabrication
series	eCAADe
email
last changed	2024/04/22 07:10

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