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_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	ecaade2022_448
id	ecaade2022_448
authors	Papanikolaou, Kyratsoula-Tereza, Liapi, Katherine and Sibetheros, Ioannis
year	2022
title	Environmental Impact Assessment and Visualization of Rain-Water Best Management Practices for Urban Blocks - An "architect-friendly" simulation model
doi	https://doi.org/10.52842/conf.ecaade.2022.2.075
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. 75–82
summary	In order to implement stormwater best management practices (BMPs) in urban blocks in Greece and other cities with warm and dry climates, such as green roofs, porous pavements etc., it is crucial that architects are able to assess their environmental impact during the design process in an efficient and simple way, without the requirement of an in depth understanding of the complex hydrological processes. To achieve the above, an “architect-friendly” computer-based model, under development by the authors, is presented. The model can be used as a decision support tool by allowing an assessment of the efficacy of non-conventional, water-sensitive, stormwater management strategies in an urban environment, measured by the stormwater runoff mitigation and temperature decrease. Wind flow simulation data from an external CFD model can be integrated into the proposed model, in order to visualize wind flow patterns in selected urban blocks. The user is able to select different stormwater BMPs from a BMP library and apply them on the 3D urban block model, in order to achieve an improved “water sensitive” state. The ENVI-MET plugin for Rhino is used for simulating temperature decrease and the SCS Curve Number method for determining stormwater runoff reduction, caused by each BMP application. The visualization of the results in the graphical interface of the Grasshopper programming environment facilitates the study of the environmental impact of stormwater BMPs in urban blocks and the comparison of different stormwater management scenarios. Several urban blocks in Athens will be used as case studies to test the proposed model and assess the efficiency of the visualization process.
keywords	Stormwater Best Management Practices, Urban Blocks, Runoff Mitigation, Temperature Reduction, Decision Support Tool, Environmental Impact Visualization
series	eCAADe
email
last changed	2024/04/22 07:10

_id	sigradi2022_252
id	sigradi2022_252
authors	Sousa, Megg; Paio, Alexandra
year	2022
title	A new approach to design patterns for small public spaces: behaviors, processes and elements.
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. 1123–1134
summary	Considering urban design as a manipulation of a complex web of nodes and connections with simultaneous interactions leads us to the systemic thinking that can be applied to the design of public spaces. The development of design pattern systems and urban toolboxes can be identified by different authors who seek to systematize urban elements from systemic thinking, considering the new urban challenges. The objective of this article, therefore, is to propose the start of a new systematization of design patterns for small public spaces based on three main classes: behaviors, processes, and elements. The methodology can be summarized as (a) theoretical foundation and literature review; (b) organization of the system into classes and attributes; (c) development of the patterns and relations. As a result, we have a friendly representation of the system which mainly discusses how new tools and people behaviors can interact with urban elements in small public spaces.
keywords	Smart cities, Urban toolbox, Systemic thinking, Small public spaces, Pattern language
series	SIGraDi
email
last changed	2023/05/16 16:57

_id	caadria2022_267
id	caadria2022_267
authors	Toohey, Gabrielle, Nguyen, Tommy Bao Nghi, Vilppola, Ritva, Qiu, Waishan, Li, Wenjing and Luo, Dan
year	2022
title	Data-Driven Evaluation of Streets to Plan for Bicycle Friendly Environments: A Case Study of Brisbane Suburbs
doi	https://doi.org/10.52842/conf.caadria.2022.1.243
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. 243-252
summary	Empirical cycling data from across the world illustrates the many barriers that car-dependent cities face when implementing cycling programs and infrastructure. Most studies focus on physical criteria, while perception criteria are less addressed. The correlations between the two are still largely unknown. This paper introduces a methodology that utilises computer vision analysis techniques to evaluate 15,383 Google Street View Images (SVI) of Brisbane City against both physical and perception cycling criteria. The study seeks to better understand correlations between the quality of a street environment and an urban area's 'bicycle-friendliness'. PSPNet Image Segmentation is utilised against SVIs to determine the percentage of an image corresponding with objects and the environment related to specific cycling factors. For physical criteria, these images are then further analysed by Masked RCNN processes. For perception criteria, subjective ranking of the images is undertaken using Machine Learning (ML) techniques to score images based on survey data. The methodology effectively allows for current findings in cycling research to be further utilised in combination via computer visioning (CV) and ML applications to measure different physical elements and urban design qualities that correspond with bicycle-friendliness. Such findings can assist targeted design strategies for cities to encourage the use of safer and more sustainable modes of transport.
keywords	Bicycle-friendly, Quality Streetscapes, Active Living, Visual Assessment, Computer Visioning, Machine Learning, SDG 3, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_291
id	caadria2022_291
authors	Zhang, Qiyan, Li, Biao, Mo, Yichen, Chen, Yulong and Tang, Peng
year	2022
title	A Web-based Interactive Tool for Urban Fabric Generation: A Case Study of Chinese Rural Context
doi	https://doi.org/10.52842/conf.caadria.2022.1.625
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. 625-634
summary	The design of rural fabric is significant for making sustainable communities and requires innovative design models and prospective work paths. This paper presents an interactive tool based on the web to generate block fabric that responds to the Chinese rural context, consisting of streets, plots, and buildings. The tool is built upon the Browser/Server (B/S) architecture, allowing users to access the generation system via the web simply and to have interactive control over the generation process in a user-friendly way. The underlying tensor field and rule-based system are adopted in the backend to model the fabric subject to multiple factors, with rules extracted from the rural design prototype. The system aims to integrate the procedural model with practical design constraints in the rural context, such as patterns, natural boundaries, elevations, planning structure, and existing streets. The proposed framework supports extensions to different urban or suburban areas, inspiring the promising paths of remote cooperation and generative design for sustainable cities and communities.
keywords	Generative Design, Web-based Tool, Urban Fabric, Rural Context, Procedural Modeling, Tensor Field, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_33
id	caadria2022_33
authors	Alva, Pradeep, Mosteiro-Romero, Martin, Miller, Clayton and Stouffs, Rudi
year	2022
title	Digital Twin-Based Resilience Evaluation of District-Scale Archetypes
doi	https://doi.org/10.52842/conf.caadria.2022.1.525
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. 525-534
summary	District-scale energy demand models can be powerful tools for understanding interactions in complex urban areas and optimising energy systems in new developments. The process of coupling characteristics of urban environments with simulation software to achieve accurate results is nascent. We developed a digital twin through a web map application for a 170ha district-scale university campus as a pilot. The impact on the built environment is simulated with pandemic (COVID-19) and climate change scenarios. The former can be observed through varying occupancy rates and average cooling loads in the buildings during the lockdown period. The digital twin dashboard was built with visualisations of the 3D campus, real-time data from sensors, energy demand simulation results from the City Energy Analyst (CEA) tool, and occupancy rates from WiFi data. The ongoing work focuses on formulating a resilience assessment metric to measure the robustness of buildings to these disruptions. This district-scale digital twin demonstration can help in facilities management and planning applications. The results show that the digital twin approach can support decarbonising initiatives for cities.
keywords	Digital twin, City Information Modelling, Planning Support System, energy demand model, SGD 11, SGD 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	cdrf2022_293
id	cdrf2022_293
authors	Amal Algamdey, Aleksander Mastalski, Angelos Chronis, Amar Gurung, Felipe Romero Vargas, German Bodenbender, and Lea Khairallah
year	2022
title	AI Urban Voids: A Data-Driven Approach to Urban Activation
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_26
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	With the development of digital technologies, big urban data is now readily available online. This opens the opportunity to utilize new data and create new relationships within multiple urban features for cities. Moreover, new computational design techniques open a new portal for architects and designers to reinterpret this urban data and provide much better-informed design decisions. The “AI Urban Voids'' project is defined as a data-driven approach to analyze and predict the strategic location for urban uses in the addition of amenities within the city. The location of these urban amenities is evaluated based on predictions and scores followed by a series of urban analyses and simulations using K-Means clustering. Furthermore, these results are then visualized in a web-based platform; likewise, the aim is to create a tool that will work on a feedback loop system that constantly updates the information. This paper explains the use of different datasets from Five cities including Melbourne, Sydney, Berlin, Warsaw, and Sao Paulo. Python, Osmx libraries and K-means clustering open the way to manipulate large data sets by introducing a collection of computational processes that can override traditional urban analysis.
series	cdrf
email
last changed	2024/05/29 14:02

_id	caadria2022_47
id	caadria2022_47
authors	An, Yudi
year	2022
title	Impact of Covid-19 on Associations between Land Use and Bike-Sharing Usage
doi	https://doi.org/10.52842/conf.caadria.2022.1.605
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. 605-614
summary	Bike-sharing as a human-centred, zero-emission, sustainable, alternative, and easily accessible transport mode has been implemented globally and consistently contributing to communities and the environment by alleviating consumption of natural sources, traffic congestion, and air pollution, which is considered a solution for future cities. The appearance of Covid-19 significantly impacts public transportation modes, including the bike-sharing system. The intention of this study was to investigate the spatiotemporal impact of the Covid-19 pandemic on associations between urban factors and bike-sharing usage in Los Angeles, United States, by analysing a sizeable actual trip dataset and employing geographically weighted regression (GWR) models. GWR was conducted for examining the varying spatial association between bike infrastructure, public transport, and urban land use factors, and bike-sharing trip volume. The results indicated that bike-sharing usage significantly decreased during the pandemic and essential service as restaurant was found consistently and positively associated with bike-sharing use. GWR provided clear spatial patterns of bike usage based on urban land use and big user databases. The outcomes of this study could inspire policymakers and shared mobility operators to support these safe, sustainable transport alters (such as rebalancing bike stations), help city resilience, and shape a sustainable future of mobility in the post-Covid-19 era.
keywords	Bike-Sharing, Covid-19, Land Use, Geographically Weighted Regression, Big Data, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	cdrf2022_150
id	cdrf2022_150
authors	Ana Zimbarg
year	2022
title	Mapping Plant Microclimates on Building Envelope Using Environmental Analysis Tools
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_13
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	Can we build our cities not only for humans but also for all living systems? How can we consider other species occupants of the built environment? Planning cities as an element of the natural domain can reshape our relationship with nature and help redefine sustainability in architecture. Although current design strategies of reducing energy use does not rectify past/continuing im-balances in the natural environment. Landscape architect John Tillman Lyle expanded the regenerative design concept based on a range of ecological concepts. The environment's complexity, and the urge to use resources smartly, encouraged him to think about architecture and the environment as a whole system. John Lyle's regenerative design strategies scaffold a conceptual framework of treating the building as part of the landscape. Environmental tools such as Ladybug can map out the different conditions surrounding the building's envelope. This information can assist in selecting and populating a building façade with suitable plant species. The framework presents the building as a feature in the landscape, creating microclimatic conditions for various plant habitats. This conceptual workflow has the potential to become a tool to include regenerative principles in the urban context.
series	cdrf
email
last changed	2024/05/29 14:02

_id	sigradi2022_102
id	sigradi2022_102
authors	Barreto, Joao; Becker, Newton; Guedes, Joana; Cidrack, Renata
year	2022
title	A Parametric Approach to Efficient Implementation of Green Infrastructure in the Urban Field.
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. 249–260
summary	Water availability has a key role in their process of occupation. However, accelerated urbanization had several detrimental impacts, increasing the vulnerability of urban communities. Because of the limitations of traditional planning, an alternative approach is emerging to respond to the constant changes in the landscape. Now, green infrastructure (GI), an ecosystem-based approach (EbA), is being used combined with traditional solutions to increase the resilience of the cities. In this paper, we proposed the use of an algorithm to determine the best place to implement GI. The algorithm used the inputs to develop a multi-criteria analysis capable of translating urban complexity. Results show that the GI solution can’t be efficiently implemented without context evaluation. However, the algorithm has the potential to become an informative tool in the decision-making process of urban planning.
keywords	Parametric Analysis, Bioretention, Sustainable Design, Green Infrastructure, Water Resources
series	SIGraDi
email
last changed	2023/05/16 16:55

_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_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	ijac202220205
id	ijac202220205
authors	Charitonidou, Marianna
year	2022
title	Urban scale digital twins in data-driven society: Challenging digital universalism in urban planning decision-making
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 238–253
summary	The article examines the impact of the virtual public sphere on how urban spaces are experienced andconceived in our data-driven society. It places particular emphasis on urban scale digital twins, which arevirtual replicas of cities that are used to simulate environments and develop scenarios in response to policyproblems. The article also investigates the shift from the technical to the socio-technical perspective withinthe field of smart cities. Despite the aspirations of urban scale digital twins to enhance the participation ofcitizens in the decision-making processes relayed to urban planning strategies, the fact that they are based on alimited set of variables and processes makes them problematic. The article aims to shed light on the tensionbetween the real and the ideal at stake during this process of abstracting sets of variables and processes in thecase of urban scale digital twins
keywords	Data-driven society, urban scale digital twins, digital universalism, democracy, big data, cyber–physical–social ecosystems, sovereignty, socio-technical perspective, smart cities, mobility justice, data-driven decisionmaking
series	journal
last changed	2024/04/17 14:29

_id	caadria2022_153
id	caadria2022_153
authors	Cheng, Cesar, Li, Yuke, Deshpande, Rutvik, Antonio, Rishan, Chavan, Tejas, Nisztuk, Maciej, Subramanian, Ramanathan, Weijenberg, Camiel and Patel, Sayjel Vijay
year	2022
title	Realtime Urban Insights for Bottom-up 15-minute City Design
doi	https://doi.org/10.52842/conf.caadria.2022.1.435
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. 435-444
summary	This paper introduces a real-time neighbour scoring system, using data collected from various web-based APIs, to facilitate "15-minute city‚ designs. The system extends on the current state of the art in three ways; first, it incorporates a multi-source urban API, to automate the extraction of location-based information from online sources; second, it provides a quantitative method to calculate and index "15-minute city‚ performance; and third, it provides a web-based application, to allow real-time feedback of neighbourhood design performance complementing the design refinements at a building and tenancy level. In addition to discussing its theoretical basis, and technical implementation, this paper provides a case study to demonstrate how the neighbourhood scoring system is incorporated into the design of a hypothetical mixed-use urban development.
keywords	Industry Innovation and Infrastructure, Sustainable Cities and Communities, Urban Walkability, Urban Accessibility, 15-minute City, Spatial Analysis, SDG 9, SDG 11
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_233
id	caadria2022_233
authors	Dai, Sida, Kleiss, Michael, Alani, Mostafa and Pebryani, Nyoman
year	2022
title	Reinforcement Learning-Based Generative Design Methodology for Kinetic Facade
doi	https://doi.org/10.52842/conf.caadria.2022.1.151
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. 151-160
summary	This paper presents a reinforcement learning (RL) based design method for kinetic facades to optimize the movement direction of shading panels. Included with this research is a case study on the Westin Peachtree Plaza in Atlanta, USA to examine the effectiveness of the proposed design method in a real-life context. Optimization of building performance has been given increased attention due to the significant impact buildings have on energy consumption and carbon emissions. Further, building performance is closely related to the "Sustainable Cities and Communities‚ mentioned in SDG11. Results show that the novel design method improved the building performance by reducing solar radiation and glare and illustrate the potential of RL in tackling complex design problems in the architectural field.
keywords	reinforcement learning, kinetic facade, generative design, design methodology, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_89
id	ecaade2022_89
authors	Di Mascio, Danilo
year	2022
title	An Untold Story of a Creative Community of Level Designers - Designing and sharing imaginary navigable virtual environments with game technologies
doi	https://doi.org/10.52842/conf.ecaade.2022.1.481
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. 481–490
summary	The following paper describes and critically reflects on the remarkable production of a creative community of level designers who designed and published 3D game levels (3D real-time virtual navigable environments) during the end of the 1990s and the first decade of the 2000s. During those years, many level designers from several countries created an impressive number and variety of custom levels (user-created content), characterised by imaginary architectures and places informed by narrative elements. This international community was supported by various websites that are no longer available. However, an open-source website, Unreal Archive, constitutes “an initiative to preserve and maintain availability of the rich and vast history of user-created content for the Unreal and Unreal Tournament series of games” (Unreal Archive, 2022). The number of levels available on Unreal Archive exceeds 34,000. For the first time in the architectural research community, this paper aims to shed light on the creative production of that period, and to identify and critically reflect on aspects that could have cultural, creative and educational value for architecture and architectural education. The author directly experienced the achievements of that historical period, and created and published a number of virtual environments using early versions of the Unreal Editor/Engine and 3D modelling software. This research is part of a larger project that investigates transdisciplinary expressions of spaces and architectures, as well as concepts, methodologies and tools in the video games field that can inspire or be transferred to the architecture field.
keywords	Virtual Environments, Imaginary Architectures and Places, Narrative, 3D Navigable Environments, Digital Heritage, User-Created Content, Unreal Editor, Unreal Series, Video Games, Level Design, Environmnetal Storytelling
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_114
id	caadria2022_114
authors	Dong, Zhiyong, Lin, Jinru, Wang, Siqi, Xu, Yijia, Xu, Jiaqi and Liu, Xiao
year	2022
title	Where Will Romance Occur, A New Prediction Method of Urban Love Map through Deep Learning
doi	https://doi.org/10.52842/conf.caadria.2022.1.213
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 213-222
summary	Romance awakens fond memories of the city. Finding out the relationship between romantic scene and urban morphology, and providing a prediction, can potentially facilitate the better urban design and urban life. Taking the Yangtze River Delta region of China as an example, this study aims to predict the distribution of romantic locations using deep learning based on multi-source data. Specifically, we use web crawlers to extract romance-related messages and geographic locations from social media platforms, and visualize them as romance heatmap. The urban environment and building features associated with romantic information are identified by Pearson correlation analysis and annotated in the city map. Then, both city labelled maps and romance heatmaps are fed into a Generative Adversarial Networks (GAN) as the training dataset to achieve final romance distribution predictions across regions for other cities. The ideal prediction results highlight the ability of deep learning techniques to quantify experience-based decision-making strategies that can be used in further research on urban design.
keywords	Romance Heatmap, Generative Adversarial Networks, Deep Learning, Big Data Analysis, Correlation Analysis, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	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

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