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_033
id	ascaad2022_033
authors	Rohani, Nima; Kim, Ikhwan
year	2022
title	Urban Design Analysis of New York City's Virtual Model: The Case of Tom Clancy's The Division
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. 188-201
summary	People have started spending time with digital tools and virtual worlds to escape reality's horrors. However, designed spaces are more than the players' needs, especially those digital games that their stories involve urban environments. This inefficiency causes spending futile efforts both in time and cost for the digital games' productions; The urban environments in these digital games are replicas of real-world cities. Some companies use some techniques for downgrading replicas. Therefore, this study aims to uncover the used techniques for designing Tom Clancy's The Division (2016). By using reverse engineering methodology and qualitative comparative analysis, the in-game map compared with the real-world map. Based on the results, the used techniques allowed the designers to scale down the game environment to be 2.5 times smaller than the actual city. Rather, verisimilitude is achieved by combining sufficiently accurate elements to give the impression of complete accuracy. By implementing the results of this research, designers can develop smaller replicas to be perceived as more extensive.
series	ASCAAD
email
last changed	2024/02/16 13:24

_id	caadria2022_60
id	caadria2022_60
authors	Chowdhury, Shuva and Hanegraaf, Johan
year	2022
title	Co-presence in Remote VR Co-design: Using Remote Virtual Collaborative Tool Arkio in Campus Design
doi	https://doi.org/10.52842/conf.caadria.2022.2.465
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. 465-474
summary	A participatory co-design approach is most often counted as a time-consuming method and ends without any concrete solution. Since the new evolution of virtual reality-based communication tools, researchers are trying to integrate citizens in the spatial design making process in-situ situation. However, there has been little research on how remotely co-presence in VR can integrate end-users in a co-design environment in re-envisioning their own using spaces. This study adopts a remote VR collaborative platform Arkio to involve novice designers remotely to design their known urban places. Participants are in three different virtual communication systems. Groups can actively engage in co-creating 3D artefacts relevant to a virtual urban environment and communicate through audio together in a remote setting. The platform was tested with a group of graduate students. The given design task was to re-envision the urban places of their academic institute campus. The sessions have been recorded and transcribed for analysis. The analysis of remote conversations shows that co-presence existed while they were engaged in co-design.
keywords	Affordable Tools, Remote Collaboration, Virtual Reality, Participatory Design, SDG 11, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_154
id	ecaade2022_154
authors	Ferretti, Maddalena, Di Leo, Benedetta, Quattrini, Ramona and Vasic, Iva
year	2022
title	Creativity and Digital Transition in Central Apennine - Innovative design methods and digital technologies as interactive tools to enable heritage regeneration and community engagement
doi	https://doi.org/10.52842/conf.ecaade.2022.2.187
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. 187–196
summary	This contribution proposes strategies of reactivation of the central Apennine of Marche Region in Italy through creative design methods and virtual technologies. The research activities are connected to two related PhD projects: one focusing on architectural and urban design, the other one on heritage digitalization and new technologies and to other research activities of our interdisciplinary team. Cagli, a small town of 8.000 inhabitants, is currently undergoing socio-economic transformations that need to be addressed strategically with a cultural and spatial perspective. The research explores regenerative solutions and local development strategies to enhance the city and its cultural landscape. Participatory processes aided by digital tools and innovative design methods are tested in Cagli’s living lab. The final output of the overall research is a “Reactive Map” combining a trans-scalar and multidisciplinary territorial analysis with visions to identify “potential spaces”. The map is a design tool to define a shared strategy of enhancement of the city and its heritage. With this paper we present one of the methodological steps of the research, a WEB-APP built upon a point clouds database and assessed through a preliminary user test. The highly descriptive 3D environment is able to collect analysis and to be enriched in a participatory way during planned activities of co-thinking. The 3D environment, improved with interviews, plans, historical pictures and other media contents, is also paired with a virtual tour to offer a different representation of the “potential spaces”. The fully boosting 3D digital technology thus represents a viable and effective solution to involve citizens and an innovative and interdisciplinary tool for knowledge advancement in the fields of architectural and urban design and heritage regeneration.
keywords	Tangible and Intangible Heritage, Co-Thinking, Trans-Scalar Approach, Narrative, Point Clouds Exploitation, Interactive Annotation, Virtual Reality
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_143
id	ecaade2022_143
authors	Talmor-Blaistain, Anat and Fisher-Gewirtzman, Dafna
year	2022
title	Developing an Interactive Method for Generation and Evaluation of Urban Environments
doi	https://doi.org/10.52842/conf.ecaade.2022.2.267
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. 267–276
summary	The ongoing increase in the population sizes of urban dwellers around the globe translates into dense and crowded neighborhoods that may negatively impact residents’ well-being. This research study presents a novel process for creating, evaluating, and filtering a range of suitable urban planning alternatives at the neighborhood scale, using generative tools and computerized analytical tools. This innovative model enables the proposal of a range of planning alternatives during the initial planning stages when changes can be made simply and without incurring unnecessary costs. Generative approaches that find optimal solutions tend to process that resemble the “black box”. This can Couse the designer to feel a lack of involvement in the process. Therefore, the suggested method emphasizes interactions between the designer and computerized tools, providing an applicable algorithm that supports the designer in the decision-making process.
keywords	Generative Urban Design, New Urbanism Theory, Dynamic Visibility Analysis (DVA), Daylight Analysis, Design Alternatives Filtering
series	eCAADe
email
last changed	2024/04/22 07:10

_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	ecaade2022_16
id	ecaade2022_16
authors	Bailey, Grayson, Kammler, Olaf, Weiser, Rene, Fuchkina, Ekaterina and Schneider, Sven
year	2022
title	Performing Immersive Virtual Environment User Studies with VREVAL
doi	https://doi.org/10.52842/conf.ecaade.2022.2.437
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 437–446
summary	The new construction that is projected to take place between 2020 and 2040 plays a critical role in embodied carbon emissions. The change in material selection is inversely proportional to the budget as the project progresses. Given the fact that early-stage design processes often do not include environmental performance metrics, there is an opportunity to investigate a toolset that enables early-stage design processes to integrate this type of analysis into the preferred workflow of concept designers. The value here is that early-stage environmental feedback can inform the crucial decisions that are made in the beginning, giving a greater chance for a building with better environmental performance in terms of its life cycle. This paper presents the development of a tool called LearnCarbon, as a plugin of Rhino3d, used to educate architects and engineers in the early stages about the environmental impact of their design. It facilitates two neural networks trained with the Embodied Carbon Benchmark Study by Carbon Leadership Forum, which learns the relationship between building geometry, typology, and construction type with the Global Warming potential (GWP) in tons of C02 equivalent (tCO2e). The first one, a regression model, can predict the GWP based on the massing model of a building, along with information about typology and location. The second one, a classification model, predicts the construction type given a massing model and target GWP. LearnCarbon can help improve the building life cycle impact significantly through early predictions of the structure’s material and can be used as a tool for facilitating sustainable discussions between the architect and the client.
keywords	Pre-Occupancy Evaluation, Immersive Virtual Environment, Wayfinding, User Centered Design, Architectural Study Design
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ijac202220212
id	ijac202220212
authors	Castriotto, Caio; Felipe Tavares; Gabriela Celani; Olga Popovic Larsen; Xan Browne
year	2022
title	Clamp links: A novel type of reciprocal frame connection
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 378–399
summary	Reciprocal frames (RFs) are complex structural systems based on mutual support between elements. One of the main challenges for these structures is achieving geometrical complexity with ease for assembly. This paper describes the development of a new type of connection for RF that uses a single bolt to fix a whole fan. The method used was the Research Through Design, using algorithmic modelling and virtual and physical prototyping. After the exploration of different alternatives, the connection selected was structurally evaluated with a 3D solid finite element analysis (FEM) software and a 2D bar parametric model. Finally, a fullscale pavilion was built as a proof-of-concept. A total of 47 connections were fabricated using four 3D-printed templates combined with a hand router. The construction allowed us to draw conclusions on the connection design and the assembly method, and the process as a whole can contribute to the development of new structural links and production methods.
keywords	Reciprocal frames, connections, computational design, simulations, digital fabrication
series	journal
last changed	2024/04/17 14:29

_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_336
id	caadria2022_336
authors	Araujo, Goncalo, Santos, Luis, Leitao, Antonioand Gomes, Ricardo
year	2022
title	AD-Based Surrogate Models for Simulation and Optimization of Large Urban Areas
doi	https://doi.org/10.52842/conf.caadria.2022.2.689
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. 689-698
summary	Urban Building Energy Model (UBEM) approaches help analyze the energy performance of urban areas and predict the impact of different retrofit strategies. However, UBEM approaches require a high level of expertise and entail time-consuming simulations. These limitations hinder their successful application in designing and planning urban areas and supporting the city policy-making sector. Hence, it is necessary to investigate alternatives that are easy-to-use, automated, and fast. Surrogate models have been recently used to address UBEM limitations; however, they are case-specific and only work properly within specific parameter boundaries. We propose a new surrogate modeling approach to predict the energy performance of urban areas by integrating Algorithmic Design, UBEM, and Machine Learning. Our approach can automatically model and simulate thousands of building archetypes and create a broad surrogate model capable of quickly predicting annual energy profiles of large urban areas. We evaluated our approach by applying it to a case study located in Lisbon, Portugal, where we compare its use in model-based optimization routines against conventional UBEM approaches. Results show that our approach delivers predictions with acceptable accuracy at a much faster rate.
keywords	urban building energy modelling, algorithmic design, machine learning in Architecture, optimization of urban areas, SDG 7, SDG 12, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_139
id	caadria2022_139
authors	Ataman, Cem, Tuncer, Bige and Perrault, Simon
year	2022
title	Asynchronous Digital Participation in Urban Design Processes: Qualitative Data Exploration and Analysis With Natural Language Processing
doi	https://doi.org/10.52842/conf.caadria.2022.1.383
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 383-392
summary	This paper aims to improve the usability of qualitative urban big data sources by utilizing Natural Language Processing (NLP) as a promising AI-based technique. In this research, we designed a digital participation experiment by deploying an open-source and customizable asynchronous participation tool, "Consul Project‚, with 47 participants in the campus transformation process of the Singapore University of Technology and Design (SUTD). At the end of the data collection process with several debate topics and proposals, we analysed the qualitative data in entry scale, topic scale, and module scale. We investigated the impact of sentiment scores of each entry on the overall discussion and the sentiment scores of each introduction text on the ongoing discussions to trace the interaction and engagement. Furthermore, we used Latent Dirichlet Allocation (LDA) topic modelling to visualize the abstract topics that occurred in the participation experiment. The results revealed the links between different debates and proposals, which allow designers and decision makers to identify the most interacted arguments and engaging topics throughout participation processes. Eventually, this research presented the potentials of qualitative data while highlighting the necessity of adopting new methods and techniques, e.g., NLP, sentiment analysis, LDA topic modelling, to analyse and represent the collected qualitative data in asynchronous digital participation processes.
keywords	Urban Design, Digital Participation, Qualitative Urban Data, Natural Language Processing (NLP), Sentiment Analysis, LDA Topic Modelling, SDG 10, SDG 11.
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_59
id	caadria2022_59
authors	Banihashemi, Farzan, Reitberger, Roland and Lang, Werner
year	2022
title	Investigating Urban Heat Island and Vegetation Effects Under the Influence of Climate Change in Early Design Stages
doi	https://doi.org/10.52842/conf.caadria.2022.2.679
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. 679-688
summary	Different criteria need to be considered for optimal strategies in the early design stages of urban developments. Under the influence of climate change, the urban heat island effect (UHI) is a phenomenon that gains importance in the early design stages. Here, different parameters, for instance, vegetation ratio in the city district and building density, play a significant role in the UHI effect. These parameters need to be quantified through different simulation tools for optimal climate adaptation and mitigation measures on the urban district scale. However, not all parameters and their influence are clear to the decision-makers and actors in the early design stages. Hence, we propose a Monte Carlo based sensitivity analysis (SA) and uncertainty analysis (UA) to show the significance of different parameters and quantify them. The SA aims to identify the major influencing parameters, whereas the UA quantifies the effect on the energy performance and indoor thermal comfort of occupants. The workflow is integrated into a collaborative design platform and applied in a case study to support decision-makers in the early design stages for new developments, densification, or refurbishment scenarios.
keywords	Monte Carlo Simulation, Sensitivity Analysis, Uncertainty Analysis, Building Energy Simulation, SDG 13, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ijac202220307
id	ijac202220307
authors	Cicek, Selen; Gozde Damla Turhan
year	2022
title	Computational generation of a spatial layout through syntactical evaluation and multi-objective evolutionary optimization
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 3, pp. 610–629
summary	The space layout problem encompasses challenges that rely on a diverse range of contexts regarding urban planning and architectural design, during the traditional design phases which require immense effort and time for the evaluation of the spatial elements’ characteristic needs. In order to eliminate the burden of considering all multidimensional design aspects at the same time, this research presents a three-bodied computational method for locating the spaces of the given architectural design program in a project site, according to the defined list of design objectives and criteria. Besides the determination of the layout according to the requirements of the spatial elements, this research proposes an integration of the space syntax theory’s analytical compounds in terms of Justified Graph Analysis and Integration Values as the fitness criteria for the multi-objective evolutionary optimization in the computational model. To satisfy the integrity levels of each various characterized element within site organization, that are implied inherently by the architectural design program and generate a sustainable space network layout for the project site
keywords	computational space layout, space syntax, spatial organization, spatial network, evolutionary algorithms
series	journal
last changed	2024/04/17 14:30

_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	ecaade2022_175
id	ecaade2022_175
authors	Di Carlo, Raffaele, Mittal, Divyae and Vesely, Ondrej
year	2022
title	Generating 3D Building Volumes for a Given Urban Context using Pix2Pix GAN
doi	https://doi.org/10.52842/conf.ecaade.2022.2.287
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 287–295
summary	Our ability to delegate the most intellectually demanding tasks to machines improves with each passing day. Even in the fields of architecture and design, which were previously thought to be exclusive domain of human creativity and flare, we are moving the first steps towards developing models that can capture the patterns, invisible to the naked eye, embedded in the creative process. These patterns reflect ideas and traditions, imprinted in the collective mind over the course of history, that can be improved upon or serve as a cautionary tale for the new generation of designers in their work of designing an equitable, more inclusive future. Generative Adversarial Networks (GANs) give us the opportunity to turn style and design into learnable features that can be used to automatically generate blueprints and layouts. In this study, we attempt to apply this technology to urban design and to the task of generating a building footprint and volume that fits within the surrounding built environment. We do so by developing a Pix2Pix model composed of a ResNet-6 generator and a Patch discriminator, applying it to satellite views of neighborhoods from across the Netherlands, and then turning the resulting 2D generated building footprint into a reusable 3D model. The model is trained using the national cadastral data and TU Delft 3D BAG dataset. The results show that it is possible to predict a building shape compatible in style and height with the surroundings. Although the model can be used for different applications, we use it as an evaluation tool to compare the design alternatives fitting the desired contextual patterns.
keywords	Generative Adversarial Networks, Urban Design, Pix2Pix, Raster Vectorization, 3D Rendering
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_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_145
id	caadria2022_145
authors	Duering, Serjoscha, Fink, Theresa, Chronis, Angelos and Konig, Reinhard
year	2022
title	Environmental Performance Assessment - The Optimisation of High-Rises in Vienna
doi	https://doi.org/10.52842/conf.caadria.2022.1.545
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. 545-554
summary	Our cities are facing different kinds of challenges - in parallel to the urban transformation and densification, climate targets and objectives of decision-makers are on the daily agenda of planning. Therefore, the planning of new neighbourhoods and buildings in high-density areas is complex in many ways. It requires intelligent processes that automate specific aspects of planning and thus enable impact-oriented planning in the early phases. The impacts on environment, economy and society have to be considered for a sustainable planning result in order to make responsible decisions. The objective of this paper is to explore pathways towards a framework for the environmental performance assessment and the optimisation of high-rise buildings with a particular focus on processing large amounts of data in order to derive actionable insights. A development area in the urban centre of Vienna serves as case study to exemplify the potential of automated model generation and applying ML algorithm to accelerate simulation time and extend the design space of possible solutions. As a result, the generated designs are screened on the basis of their performance using a Design Space Exploration approach. The potential for optimisation is evaluated in terms of their environmental impact on the immediate environment.
keywords	simulation, prediction and evaluation, machine learning, computational modelling, digital design, high-rises, SGD 11, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_456
id	caadria2022_456
authors	Gong, Pixin, Huang, Xiaoran, Huang, Chenyu and White, Marcus
year	2022
title	Quantifing the Imbalance of Spatial Distribution of Elderly Service with Muti-source Data
doi	https://doi.org/10.52842/conf.caadria.2022.1.455
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. 455-464
summary	With the growing challenge of aging populations around the world, the study of the elderly service is an essential initiative to accommodate the particular needs of the disadvantaged communities and promote social equity. Previous research frameworks are very case-specific with limited evaluation indicators that cannot be extended to other scenarios and fields. Based on multi-source data and Geographic Information System (GIS), this paper quantifies and visualises the imbalance in the spatial distribution of elderly services in 218 neighbourhoods in Shijingshan District, Beijing, China. Mortality data were obtained, and the most contributing indicators to mortality were investigated by correlation analysis. Finally, mapping between other facility indicators to mortality rates was constructed using machine learning to further investigate the factors influencing the quality of elderly services at the community level. The conclusion shows that the functional density of transportation facilities, medical facilities, living services facilities, and the accessibility of elderly care facilities are most negatively correlated with mortality. The correlation conclusion is combined with a machine learning prediction model to provide future recommendations for the construction of unbalanced elderly neighbourhoods. This research offers a novel systematic method to study urban access to elderly services as well as a new perspective on improving social fairness.
keywords	elderly service facilities, multi-source data, machine learning, SDG 3, SDG 10, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_278
id	ecaade2022_278
authors	Gopalakrishnan, Srilalitha, Srikanth, Anjanaa, Hablani, Chirag and Schroepfer, Thomas
year	2022
title	Measuring Impacts of Vertically Integrated Pedestrian Network Configurations on Urban Space Use in Dense Built Environments
doi	https://doi.org/10.52842/conf.ecaade.2022.2.307
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. 307–316
summary	Integrated mixed-use developments are increasingly taking the form of vertical extensions of urban spaces on the ground. The spatial networks within the evolving vertical neighbourhoods, their relationships with the larger urban fabric, and the user interactions within these complex multi-layered urban built environments are numerous and varied. This paper presents an analytical framework to map and analyse the pedestrian connectivity within the vertically integrated urban open space network and its interactions with the ground level urban fabric using a Network Science-based approach. The research uses Kampung Admiralty, a first-of-its-kind building site scale 'vertical city' prototype in Singapore, as a case study. A 3d pedestrian network link model mapping the pedestrian connectivity within the development is generated and analysed to understand the flows and accessibility to the vertically distributed urban open spaces. This 3d pedestrian link model is further combined with the 2d urban walking network at the ground level to generate an integrated neighbourhood-level walkability analysis. Analysing the two-dimensional connectivity at the ground level and comparing the influence of linking the three-dimensional vertical connectivity to the ground network generates valuable design insights into the spatial performance of vertically integrated developments in their immediate urban context.
keywords	Network Science, sDNA, Urban Pedestrian Network, Vertical Urban Environments, Vertical Connectivity
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	caadria2024_87
id	caadria2024_87
authors	Li, Jiongye and Stouffs, Rudi
year	2024
title	Distribution of Carbon Storage and Potential Strategies to Enhance Carbon Sequestration Capacity in Singapore: A Study Based on Machine Learning Simulation and Geospatial Analysis
doi	https://doi.org/10.52842/conf.caadria.2024.2.089
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 89–98
summary	The expansion of urbanization leads to significant changes in land use, consequently affecting carbon storage. This research aims to investigate the carbon loss due to land use alterations and proposes strategies for mitigation. Utilizing existing land use data from 2017 and 2022, along with simulated data for 2025 generated by an ANN model and Cellular Automata, we identified changes in land use. These changes were then correlated with variations in carbon storage, both gains and losses. Our findings reveal a significant loss of 36,859 metric tons of carbon storage from 2017 to 2022. The projection for 2025 estimates a further reduction, reaching a total loss of 83,409 metric tons. By employing the LISA method, we identified that low-carbon storage zones are concentrated in the southeast region of the research site. By overlaying these zones with areas of carbon storage loss, we pinpointed regions severely affected by carbon depletion. Consequently, we propose that mitigation strategies should be imperatively implemented in these identified areas to counteract the trend of carbon storage loss. This approach offers urban planners a solution to identify areas experiencing carbon storage decline. Moreover, our research methodology provides a novel framework for scholars studying similar carbon issues.
keywords	land use and land cover (LULC) changes, simulated LULC, machine learning model, carbon storage changes, GIS
series	CAADRIA
email
last changed	2024/11/17 22:05

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