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	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	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	ecaade2022_000
id	ecaade2022_000
authors	Pak, Burak, Wurzer, Gabriel and Stouffs, Rudi
year	2022
title	eCAADe 2022 Co-creating the Future: Inclusion in and through Design - Volume 1
doi	https://doi.org/10.52842/conf.ecaade.2022.1
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, 672 p.
summary	Spatial design is becoming an increasingly social, participatory and inclusive practice. In the last decade, ordinary people all around the world have started to claim a shaping power over the processes of urbanization; over the ways in which our cities are made and remade (Harvey, 2013). There has been a resurgence in the number of do-it-yourself cooperatives initiated by non-designer citizens, activists, artists and designers. In parallel to these developments, a plethora of social technologies, tools and platforms have been developed to include a variety of stakeholders in the architectural design, urban design, planning and decision-making processes. Crowdsourcing and crowdfunding applications started to be widely used to tap into the wisdom of the crowd. Novel developments in parametric design and digital fabrication created possibilities for user participation in the making of customized and highly diversified products. With the combination of artificial intelligence and the Internet of Things, smart buildings, autonomous devices, robots and software started to transform into agents and active participants. The attempts to harness collective human and artificial intelligence opened up new avenues for combining practice, research and education. On the other hand, there is a growing concern over the possible negative impact of the digital devices, tools, platforms and agents integrated in the making of our buildings and cities, public, private and collective spaces. Examples of those are the potential exclusion of vulnerable and disadvantaged citizens, transfer of citizen power to the corporations, privatization of personal life and data, as well as spatial exclusion through increased technological control and surveillance.
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_001
id	ecaade2022_001
authors	Pak, Burak, Wurzer, Gabriel and Stouffs, Rudi
year	2022
title	eCAADe 2022 Co-creating the Future: Inclusion in and through Design- Volume 2
doi	https://doi.org/10.52842/conf.ecaade.2022.2
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, 646 p.
summary	Spatial design is becoming an increasingly social, participatory and inclusive practice. In the last decade, ordinary people all around the world have started to claim a shaping power over the processes of urbanization; over the ways in which our cities are made and remade (Harvey, 2013). There has been a resurgence in the number of do-it-yourself cooperatives initiated by non-designer citizens, activists, artists and designers. In parallel to these developments, a plethora of social technologies, tools and platforms have been developed to include a variety of stakeholders in the architectural design, urban design, planning and decision-making processes. Crowdsourcing and crowdfunding applications started to be widely used to tap into the wisdom of the crowd. Novel developments in parametric design and digital fabrication created possibilities for user participation in the making of customized and highly diversified products. With the combination of artificial intelligence and the Internet of Things, smart buildings, autonomous devices, robots and software started to transform into agents and active participants. The attempts to harness collective human and artificial intelligence opened up new avenues for combining practice, research and education. On the other hand, there is a growing concern over the possible negative impact of the digital devices, tools, platforms and agents integrated in the making of our buildings and cities, public, private and collective spaces. Examples of those are the potential exclusion of vulnerable and disadvantaged citizens, transfer of citizen power to the corporations, privatization of personal life and data, as well as spatial exclusion through increased technological control and surveillance.
keywords	Proceedings, Front Matter
series	eCAADe
email
last changed	2024/04/22 07:10

_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_209
id	cdrf2022_209
authors	Yecheng Zhang, Qimin Zhang, Yuxuan Zhao, Yunjie Deng, Feiyang Liu, Hao Zheng
year	2022
title	Artificial Intelligence Prediction of Urban Spatial Risk Factors from an Epidemic Perspective
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_18
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	From the epidemiological perspective, previous research methods of COVID-19 are generally based on classical statistical analysis. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. We take the Spatio-temporal data of people infected with new coronary pneumonia before February 28 in Wuhan in 2020 as the research object. We use kriging spatial interpolation technology and core density estimation technology to establish the epidemic heat distribution on fine grid units. We further examine the distribution of nine main spatial risk factors, including agencies, hospitals, park squares, sports fields, banks, hotels, Etc., which are tested for the significant positive correlation with the heat distribution of the epidemic. The weights of the spatial risk factors are used for training Generative Adversarial Network models, which predict the heat distribution of the outbreak in a given area. According to the trained model, optimizing the relevant environment design in urban areas to control risk factors effectively prevents and manages the epidemic from dispersing. The input image of the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area.
series	cdrf
email
last changed	2024/05/29 14:02

_id	caadria2022_68
id	caadria2022_68
authors	Carta, Silvio, Turchi, Tommaso and Pintacuda, Luigi
year	2022
title	Measuring Resilient Communities: an Analytical and Predictive Tool
doi	https://doi.org/10.52842/conf.caadria.2022.1.615
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. 615-624
summary	This work presents the initial results of an analytical tool designed to quantitatively assess the level of resilience of urban areas. We use Deep Neural Networks to extract features of resilience from a trained model that classifies urban areas using a pre-assigned value range of resilience. The model returns the resilience value for any urban area, indicating the distance between the centre of the selected area and relevant typologies, including green areas, buildings, natural elements and infrastructures. Our tool also indicates the urban morphological characteristics that have a larger impact on the resilience score. In this way we can learn why a neighbourhood is successful (or not) and how to improve its level of resilience. The model employs Convolutional Neural Networks (CNNs) with Keras on Tensorflow for the computation. The outputs are loaded onto a Node.JS environment and bootstrapped with React.js to generate the online demo.
keywords	sustainable cities and communities, resilient communities, CNN, urban morphology, SDG 11, SDG 13
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	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	ecaade2022_366
id	ecaade2022_366
authors	Geropanta, Vasiliki, Karagianni, Anna, Parthenios, Panagiotis, Ampatzoglou, Triantafyllos, Fatouros, Loukas, Simantiraki, Vasiliki, Brokos-Melissaratos, Orestis and Eleftheriadis, Dimitris
year	2022
title	Digitalization of Participatory Greening - The case of UnionYouth in Chania
doi	https://doi.org/10.52842/conf.ecaade.2022.1.469
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. 469–478
summary	The contemporary climate crisis pushed communities of actors, cities and citizens to use smart technology, digital platforms, and data-based intelligence to steer creative solutions for greening in their urban ecosystems. This phenomenon brought about an increasing imperative for citizen participation and inclusion, in the co-design of green infrastructures, suggesting alternative ways to deal with the lack or misuse of public space. In this framework, this paper analyzes the case of ''UnionYouth in Chania'', a project that aims a) to build an environmental awareness strategy for Generation Z, b) to promote capacity-building processes related to climate change and environmental protection, c) actually transform the city public space through participatory processes. Specifically, the project describes the creation of a digital platform and a mobile app consisting of several engagement tools that allow interaction between the digital community of youth, the city's decision-makers, and city greening actors. Therefore, the first part of the paper talks about the necessity of promoting today's participatory processes in the city for climate change mitigation through a literature review that emerged in the last decade. The second part of the paper examines a case study, namely UnionYouth in Chania, a digital collaborative platform that promotes methods for greening the city through district-based, activity-based, and network-based redesign solutions. The third part of the paper brings about interesting reflections on the relationship between the analog and digital world, and how bottom-up processes may be an important tool in city planning. The overall scope of the analysis of the specific case study is to bring insights into the architectural world, as a means to create more bridges with citizens and communities and contribute to their greening understanding.
keywords	Climate Change, Generation Z, Green Infrastructure, Raise Awareness, Mobile Application, Participatory Design, Smart City
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_197
id	ecaade2022_197
authors	Giglio, Andrea, Gorbet, Rob and Beesley, Philip
year	2022
title	Hybrid Soundscape: Human and non-human sounds interactions for a collective installation
doi	https://doi.org/10.52842/conf.ecaade.2022.1.441
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. 441–447
summary	The paper describes a site-specific architectural soundscape installation created during a workshop in August 2021 at the Domaine de Boisbuchet in France. Far from urban noise, participants were attuned to natural, artificial, and human sound spheres, placing them in dialog and interweaving them through emulation, voice recording, and electro-acoustic devices including piezoceramic sensors, small motors, speakers, and embedded electronics. This expository paper includes qualitative descriptions of the spatial sound compositions, the technology that supported them, and the performance into which they were integrated. The results of this event were described by participants as trance-like, with phasing of multiple periodically organized emergent sound phenomena creating a deeply immersive distributed environment. In describing in detail, the tools, processes, outcomes and implications of the workshop, this paper offers an example of a design approach and model that can contribute immersive distributed architectural soundscape design through human and non-human sound interaction.
keywords	Spatial Sound, Hybrid Soundscape, Acoustic Responsive Devices, Human-Nonhuman Sound Interaction, Collective Installation
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_140
id	caadria2022_140
authors	Huang, Shuyi and Zheng, Hao
year	2022
title	Morphological Regeneration of the Industrial Waterfront Based on Machine Learning
doi	https://doi.org/10.52842/conf.caadria.2022.1.475
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. 475-484
summary	The regeneration of the industrial waterfront is a global issue, and its significance lies in transforming the waterfront brownfield into an eco-friendly, hospitable, and vibrant urban space. However, the industrial waterfront naturally has comparatively unmanageable morphological features, including linear shape, irregular waterfront boundary, and separation with urban networks. Therefore, how to subdivide the vacant land and determine the land-use type for each subdivision becomes a challenging problem. Accordingly, this study proposes an application of machine learning models. It allows the generation of morphological elements of the vacant industrial waterfront by comparing the before-and-after scenarios of successful regeneration projects. The data collected from New York City is used as a showcase of this method.
keywords	machine learning, urban morphology, industrial waterfront regeneration, sustainable cities, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	architectural_intelligence2022_5
id	architectural_intelligence2022_5
authors	Jiading Zhong, Jianlin Liu, Yongling Zhao, Jianlei Niu & Jan Carmeliet
year	2022
title	Recent advances in modeling turbulent wind flow at pedestrian-level in the built environment
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00008-7
source	Architectural Intelligence Journal
summary	Pressing problems in urban ventilation and thermal comfort affecting pedestrians related to current urban development and densification are increasingly dealt with from the perspective of climate change adaptation strategies. In recent research efforts, the prime objective is to accurately assess pedestrian-level wind (PLW) environments by using different simulation approaches that have reasonable computational time. This review aims to provide insights into the most recent PLW studies that use both established and data-driven simulation approaches during the last 5 years, covering 215 articles using computational fluid dynamics (CFD) and typical data-driven models. We observe that steady-state Reynolds-averaged Navier-Stokes (SRANS) simulations are still the most dominantly used approach. Due to the model uncertainty embedded in the SRANS approach, a sensitivity test is recommended as a remedial measure for using SRANS. Another noted thriving trend is conducting unsteady-state simulations using high-efficiency methods. Specifically, both the massively parallelized large-eddy simulation (LES) and hybrid LES-RANS offer high computational efficiency and accuracy. While data-driven models are in general believed to be more computationally efficient in predicting PLW dynamics, they in fact still call for substantial computational resources and efforts if the time for development, training and validation of a data-driven model is taken into account. The synthesized understanding of these modeling approaches is expected to facilitate the choosing of proper simulation approaches for PLW environment studies, to ultimately serving urban planning and building designs with respect to pedestrian comfort and urban ventilation assessment.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	caadria2022_210
id	caadria2022_210
authors	Tabi, Salma, Sakai, Yasushi, Tung, Nguyen, Taima, Masahiro, Cheddadi, Aqil and Ikeda, Yasushi
year	2022
title	A Framework for a Gameful Collective Urbanism Based on Tokenized Location Data and Liquid Democracy: Early Prototyping of a Case Study Using E-bikes
doi	https://doi.org/10.52842/conf.caadria.2022.1.585
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 585-594
summary	The participation of citizens in designing their social and built environments is vital for the creation of sustainable cities and communities. However, in practice, collective decision-making remains challenging. Several researchers have proposed innovative models of governance to achieve a more democratic participation. This paper attempts to contribute to this topic from the viewpoint of urban planning. The objectives are twofold. First, to introduce a conceptual framework of a gameful collective process of urbanism based on location data. Second, to present an early stage of prototyping a case study using e-bikes. Research questions are elaborated as follows: How can collective processes of urban planning engage the collective intelligence and the local knowledge of the community? How to utilize technological tools to support new forms of participatory urban governance? The main contribution of this work lies in the combination of the concepts of temporal ownership of public space, tokenization of location data, and liquid democracy, to design a dynamic and gameful decision-making process that promotes collective intelligence.
keywords	Collective urbanism, Liquid democracy, Temporal ownership, Tokenization, Location data, Data dignity, Gameful design, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	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	caadria2022_420
id	caadria2022_420
authors	van Ameijde, Jeroen and Leung, Carson Ka Shut
year	2022
title	UAV-based People Location Tracking and Analysis for the Data-Driven Assessment of Social Activities in Public Spaces
doi	https://doi.org/10.52842/conf.caadria.2022.1.293
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. 293-302
summary	In sustainable high-density cities, public spaces play an important role in supporting social and community health and well-being. Amidst ongoing urbanisation, it is of increasing importance to study public space interaction patterns and placemaking processes that contribute to the quality of life of urban residents. This paper reports on the development of a new methodology for the computational tracking and analysis of social activities in urban spaces, using Computer Vision Object Detection (CVOD) techniques to create digitalised pedestrian trajectory data. Referring to concepts from humanistic geography and time geography, our method offers a new platform for data-driven urban place studies, detecting co-presence and social interaction in relation to urban morphology. This paper focuses on the development of Machine Learning protocols, algorithms for tracing and mapping pedestrian trajectories in a georeferenced photogrammetry model, and computational analysis of co-presence. The resulting workflow forms a foundation for future research around detecting, analysing and quantifying behavioural parameters, to evaluate the ability of public spaces to support social interaction and placemaking.
keywords	Public Space Analysis, Pedestrian Location Tracking, Computer Vision Object Detection, Machine Learning, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_172
id	ecaade2022_172
authors	Vugreshek, Zvonko
year	2022
title	Discrete Differences between Aggregate Systems for Generative Urban and Architectural Design
doi	https://doi.org/10.52842/conf.ecaade.2022.2.029
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 29–38
summary	The activation of aggregate systems, procedural generation, and other models of discrete computation result in different organizations and formal outcomes. Some differences seem blurry but are relevant to understand in order to govern the computational design process in the specific domain. They are developing around empiric principles, are based on discrete automation rule sets, and are intertwined in various ways. The paper presents and describes some differences and communalities between each system. Its goal is to support the computational designer, architect or urban planner in the decision-making process and choice of which system could work best in a given context and to solve a specific problem. An introduction into aggregation or automation will serve as a foundation for the research. The discrete systems Cellular Automata, Wave Function Collapse, Graph-Grammar Aggregation will be described. In this paper, the latter is specified as selection-based-aggregation. Diffusion-Limited Aggregation (DLA), which is regarded as an early translation of natural behaviour into scripted nature will serve as a framework. In a next step potential and utilization of these discrete systems in expanding the language of architectural and urban morphology will be experimentally demonstrated and compared. The paper concludes by suggesting a current state of development and potential adaptation of the methods for broader use within the architectural and urban design paradigm of developing methods for the creation of new computational typologies.
keywords	Discrete Aggregation, Cellular Automata, Procedural Generation, Urban Morphology Generation, Wave Function Collapse
series	eCAADe
email
last changed	2024/04/22 07:10

_id	architectural_intelligence2022_18
id	architectural_intelligence2022_18
authors	Wei Ye, Shuhua Chen, Xiayu Zhao & Weiguo Xu
year	2022
title	Porous space — biomimetic of tafoni in computational design
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00019-4
source	Architectural Intelligence Journal
summary	Porous urban spaces not only improve interactions, but also increase natural ventilation. Weathered rocks are where porous spaces exist in nature. This paper investigates the biomimicry of tafoni, a type of weathered rock that contains pores of varying sizes. The formation of tafoni inspires architectural design, but its complex shape makes manual modeling challenging. The objective of studying the biomimetics of tafoni is to apply its benefits to design applications. Using biomimetic techniques, computation algorithms for tafoni morphogenesis are developed. This paper investigates the inherent characteristics of tafoni and reclassifies them based on architectural geometric elements. It then describes the reclassified tafoni and explains the formation process. This paper develops a 3D evolutionary algorithm and a 2.5D descriptive algorithm based on diagrams. After a comparison, the 2.5D algorithm is chosen because it is more controllable and operable for computational design. This paper also conducts experiments on the results obtained by the 2.5D algorithm to demonstrate its adaptability and architectural design application potential, as well as its application schemes in various design disciplines, including urban planning, architectural design, and landscape design. This paper proposes an algorithm that can be utilized in various fields of computational design. It is computationally efficient while retaining its biological form.
series	Architectural Intelligence
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last changed	2025/01/09 15:00

_id	architectural_intelligence2022_7
id	architectural_intelligence2022_7
authors	Weixin Huang & Luying Wang
year	2022
title	Towards big data behavioral analysis: rethinking GPS trajectory mining approaches from geographic, semantic, and quantitative perspectives
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00011-y
source	Architectural Intelligence Journal
summary	The question regarding the actual usage of built environments is of immense importance in behavioral research. Yet traditional methods of collecting and analyzing data on movements and activities often lack needed accuracy and granularity. Thus, this article reviewed and summarized the applicability of emergent GPS trajectory mining approaches in the field of architecture from geographic, semantic, and quantitative perspectives, respectively. Accordingly, three experiments based on a case study using real GPS trajectory data from visitors to the Palace Museum in China were conducted to examine the usefulness and weakness of the aforementioned approaches. The findings revealed that although all three dimensions of the trajectory mining approaches had the potential to provide useful information for architectural and urban design, the higher the dimensionality in utilizing the data, the more effective the approach was in discovering generalizable knowledge of human behavioral pattern. Furthermore, the results suggested that to gain insights into the typological characteristics of human behaviors related to the built environments, the contribution of trajectory data alone was limited, hence, conventional field surveys and questionnaires which contain information on individual characteristics and spatial features should be used in conjunction. Future research and practical implications were outlined.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	ecaade2022_179
id	ecaade2022_179
authors	Yosifof, Roei, Trossman-Haifler, Yaala and Fisher-Gewirtzman, Dafna
year	2022
title	VR Experiment that Supports the Development of Analytical Tools for Simulating and Predicting Urban Well-Being
doi	https://doi.org/10.52842/conf.ecaade.2022.2.485
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. 485–494
summary	This paper presents a VR experiment, conducted to assess two analytical models – Dynamic Visibility Analysis (DVA) and Dynamic Enclosure Street Section Analysis (DESSA)– that predict human well-being in urban settings. Well-being was measured in seven VR urban environments that differ in morphology. Comparing participants’ preferences in the experiment, measured through the Integrated Well-Being Index (IWI) Questionnaire, to measurements outcomes from the models indicates certain alignments between human preferences and analytical results regarding urban well-being. The results indicate the models’ strengths in predicting human experience. Strong relations between participants’ ranking in the experiment and the analysis results are seen in the highest and lowest rated well-being variables.
keywords	Experiments in VR, Urban Well-being in Dense environments, Visibility Analysis, Enclosure Analysis, Development of Analytical Models
series	eCAADe
email
last changed	2024/04/22 07:10

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