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_45
id	caadria2022_45
authors	Boim, Anna, Dortheimer, Jonathan and Sprecher, Aaron
year	2022
title	A Machine-Learning Approach to Urban Design Interventions In Non-Planned Settlements
doi	https://doi.org/10.52842/conf.caadria.2022.1.223
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. 223-232
summary	This study presents generative adversarial networks (GANs), a machine-learning technique that can be used as an urban design tool capable of learning and reproducing complex patterns that express the unique spatial qualities of non-planned settlements. We report preliminary experimental results of training and testing GAN models on different datasets of urban patterns. The results reveal that machine learning models can generate development alternatives with high morphological resemblance to the original urban fabric based on the suggested training process. This study contributes a methodological framework that has the potential to generate development alternatives sensitive to the local practices, thereby promoting preservation of traditional knowledge and cultural sustainability.
keywords	Non-planned settlements, Cultural Sustainability, Machine Learning, Generative Adversarial Networks, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_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	ecaade2022_450
id	ecaade2022_450
authors	Braumann, Johannes, Gollob, Emanuel and Singline, Karl
year	2022
title	Visual Programming for Interactive Robotic Fabrication Processes - Process flow definition in robotic fabrication
doi	https://doi.org/10.52842/conf.ecaade.2022.2.427
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. 427–434
summary	Visual, flow-based programming environments in architecture and design are built to control data flow but not process flow. However, controlling the process flow is essential for interacting with robotic fabrication processes, so that they can react to input such as user interaction or sensor data. In this research, we combine two visual programming environments, utilizing Grasshopper for defining complex, robotic toolpaths, and Unity Visual Scripting for controlling the overall process flow and process interaction. Through that, we want to enable architects and designers to define more complex, interactive production processes, with accessible, bespoke user-interfaces allowing non-experts to operate these processes - a crucial step for the commercialization of innovations. This approach is evaluated in a case study that creates a mobile, urban microfactory that prototypically fabricates location-specific objects through additive manufacturing.
keywords	Visual Programming, State Machine, Industrial Robotics, Unity Visual Scripting
series	eCAADe
email
last changed	2024/04/22 07:10

_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	ijac202220203
id	ijac202220203
authors	Dzieduszyñski, Tomasz
year	2022
title	Machine learning and complex compositional principles in architecture: Application of convolutional neural networks for generation of context-dependent spatial compositions
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 196–215
summary	A substantial part of architectural and urban design involves processing of compositional interdependenciesand contexts. This article attempts to isolate the problem of spatial composition from the broader category ofsynthetic image processing. The capacity of deep convolutional neural networks for recognition and utilization of complex compositional principles has been demonstrated and evaluated under three scenariosvarying in scope and approach. The proposed method reaches 95.1%–98.5% efficiency in the generation ofcontext-fitting spatial composition. The technique can be applied for the extraction of compositionalprinciples from the architectural, urban, or artistic contexts and may facilitate the design-related decisionmaking by complementing the required expert analysis
keywords	Spatial composition, architecture, convolutional neural network, ordering principles, machine learning, image generation, design, CAAD
series	journal
last changed	2024/04/17 14:29

_id	acadia22_310
id	acadia22_310
authors	Koehler, Daniel
year	2022
title	Building Synthetic Data Sets or How to Learn from Future Architectures?
source	ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 310-317.
summary	Simulating synthetic data can induce design speculation to machine learning applications. Leaning on density studies for modernist settlements, we propose an approach that mixes ratios of sets to generate buildings quickly. A case study exemplifies how quickly one can generate and analyze a set of buildings at the resolution of BIM modeling. We conclude that synthetic data sets could become a feature of daily design workflows due to being computationally inexpensive and easy to adapt.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:00

_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

_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	caadria2022_313
id	caadria2022_313
authors	Raanan, Noam, Yoffe, Hatzav and Grobman, Jacob
year	2022
title	A Machine Learning Evaluation Method for Sustainability Evaluation: The Case of Neighbourhoods' Design
doi	https://doi.org/10.52842/conf.caadria.2022.1.283
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. 283-291
summary	This paper proposes a framework for machine learning to evaluate landscape design. In this study, we measured key performance indicators of landscape-development plans using a convolutional neural network (CNN) approach to predict the performance level of the design. The model used 3749 performance evaluations from 36 professionals, covering six sustainability criteria in 32 neighbourhoods' designs. Results show a high agreement level between experts on the performance level of the designs. The study contributes to computational sustainability by showing the potential in evaluation-automation of urban resiliency, ecological enhancement, and design for wellbeing, using expert knowledge and machine learning.
keywords	Urban Design, Landscape Architecture, Computational Sustainability, Machine Learning, Convolutional Neural Network, Llandscape Sustainability, SDG 9, SDG 11, SDG 13, SDG 15
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2022_53
id	sigradi2022_53
authors	Stuart-Smith, Robert; Danahy, Patrick
year	2022
title	3D Generative Design for Non-Experts: Multiview Perceptual Similarity with Agent-Based Reinforcement Learning
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. 115–126
summary	Advances in additive manufacturing allow architectural elements to be fabricated with increasingly complex geometrical designs, however, corresponding 3D design software requires substantial knowledge and skill to operate, limiting adoption by non-experts or people with disabilities. Established non-expert approaches typically constrain geometry, topology, or character to a pre-established configuration, rather than aligning to figural and aesthetic characteristics defined by a user. A methodology is proposed that enables a user to develop multi-manifold designs from sketches or images in several 3d camera projections. An agent-based design approach responds to computer vision analysis (CVA) and Deep Reinforcement Learning (RL) to design outcomes with perceptual similarity to user input images evaluated by Structural Similarity Indexing (SSIM). Several CVA and RL ratios were explored in training models and tested on untrained images to evaluate their effectiveness. Results demonstrate a combination of CVA and RL motion behavior can produce meshes with perceptual similarity to image content.
keywords	Generative Design, Machine Learning, Agent-Based Systems, Non-Expert Design
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	sigradi2022_49
id	sigradi2022_49
authors	Verniz, Debora; Santos, Deborah
year	2022
title	Viability study for a sustainable energy policy in informal settlements: The case of Santa Marta favela
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. 947–956
summary	Architects and urban planners are often challenged by design problems that include public policies for social development. While the use of sustainable energy has been growing, the costs with installation are still a problem to underprivileged communities such as informal settlements. In this sense, energy justice is a relatively new concept. This paper is part of a larger research that aims to propose a framework for a sustainable energy policy. In this paper we use a case study to estimate the capacity for solar energy production. Research steps include data collection, modeling, simulation, and analysis. Results show that the case study has the capacity to produce several times more energy than it consumes, with potential of selling the overproduction to generate benefits to the community. Future research includes a more detailed simulation of the case study’s potential using machine learning techniques.
keywords	Inclusive design, Favela, Informal settlements, Affordable and clean energy, Sustainable cities and communities
series	SIGraDi
email
last changed	2023/05/16 16:57

_id	caadria2022_264
id	caadria2022_264
authors	Zhang, Garry Hangge, Meng, Leo Lin, Gardner, Nicole, Yu, Daniel and Haeusler, Matthias Hank
year	2022
title	Transit Oriented Development Assistive Interface (TODAI): A Machine Learning Powered Computational Urban Design Tool for TOD
doi	https://doi.org/10.52842/conf.caadria.2022.1.253
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. 253-262
summary	Transit-oriented Development(TOD) is widely regarded as a sustainable development paradigm for its sensible space planning and promotion of public transit access. Research in providing decision support tools of TOD may contribute to the Sustainable Development Goals, especially towards sustainable cities and communities (SDG goal 11).While the existing Geographic Information System(GIS) approach may well inform TOD planning, computational design, simulation, and visualisation techniques can further enhance this process. The research aims to provide a data-driven, computational-aided planning support system (PSS) to enhance the TOD decision-making process. The research adopts an action research methodology, which iteratively designs experiments and inquires through situating the research question in real-world practice. A work-in-progress prototype is provided - Transit-Oriented Development Assistive Interface (TODAI), along with an experiment in a newly proposed metro station in Sydney, Australia. TODAI provides real-time visualisation of urban forms and analytical data indicators reflecting key considerations relevant to TOD performance. A regressive machine learning model (XGBoost) is used to make predictions of analytical indicators, promptly producing outcomes that may otherwise require a costly computational operation.
keywords	TransUrban Planning, Transit-Oriented Development, Planning Support System, Machine Learning, SDG 11it-Oriented Development, Urban Planning, Machine Learning, Computational Design, SDG11, Sustainable Cities and Communities
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	cdrf2022_223
id	cdrf2022_223
authors	Zhiyi Dou, Waishan Qiu, Wenjing Li, Dan Luo
year	2022
title	Evaluation Process of Urban Spatial Quality and Utility Trade-Off for Post-COVID Working Preferences
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_19
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	The formation of cities, and the relocation of workers to densely populated areas reflect a spatial equilibrium, in which the higher real consumption levels of urban areas are offset by lower non-monetary amenities [1]. However, as the society progress toward a post-COVID stage, the prevailing decentralized delivery systems and location-based services, the growing trend of working from home, with citizens’ shifting preference of de-appreciating densities and gathering, have not only changed the possible spatial distribution of opportunities, resources, consumption and amenities, but also transformed people’s preference regarding desirable urban spatial qualities, value of amenities, and working opportunities [2, 3]. This research presents a systematic method to evaluate the perceived trade-off between urban spatial qualities and urban utilities such as amenities, transportation, and monetary opportunities by urban residence in the post-COVID society. The outcome of the research will become a valid tool to drive and evaluate urban design strategies based on the potential self-organization of work-life patterns and social profiles in the designated neighbourhood. To evaluate the subjective perception of the urban residence, the study started with a comparative survey by asking residence to compare two randomly selected urban contexts in a data base of 398 contexts sampled across Hong Kong and state their living preference under the presumption of following scenarios: 1. working from home; 2. working in city centre offices. Core information influencing the spatial equilibrium are provided in the comparable urban context such as street views, housing price, housing space, travel time to city centre, adjacency to public transport and amenities, etc. Each context is given a preference score calculated with Microsoft TrueSkill Bayesian ranking algorithm [4] based on the comparison survey of two scenarios. The 398 contexts are further analysed via GIS and image processing, to be deconstructed into numerical values describing main features for each of the context that influence urban design strategies such as composition of spatial features, amenity allocation, adjacency to city centre and public transportations. Machine learning models are trained with the numerical values of urban features as input and two preference scores for the two working scenarios as the output. The correlation heat maps are used to identify main urban features and its p-value that influence residence’s preference under two working scenarios in post–COVID era. The same model could also be applied to inform the direction of urban design strategies to construct a sustainable community for each type of working population and validate the design strategies via predicting its competitiveness in attracting residence and developing target industries.
series	cdrf
email
last changed	2024/05/29 14:02

_id	ecaade2022_202
id	ecaade2022_202
authors	Acican, Oyku and Luyten, Laurens
year	2022
title	Experiential Learning of Structural Systems - Comparison of design-based and experiment-based pedagogies
doi	https://doi.org/10.52842/conf.ecaade.2022.2.535
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. 535–544
summary	This research aims to compare two experiential learning methods’ effectiveness for (1) a deeper understanding of structural behaviour, and (2) skills to design architectural forms that are structurally informed. A course was planned to investigate the effect of the type and order of the two teaching units: (1) guided experiments on a parametric design model, and (2) parametric design of a tower and custom experiments using Grasshopper and Karamba. Results indicate that the group that started with the experiments learned to ask the relevant questions by experimenting with the appropriate parameters that helped them to find the structural principles and apply them during their design phase. The group that started with the design were lost in the structural concepts and in identifying the meaningful parameters to test for. However, after the experiment was completed, this group could make a knowledge transfer. Acquisition of structures knowledge may require the experience of multiple situations while the application of this knowledge may involve selecting the relevant structural experience with the architectural form-finding process. In the future, a proposed experiential learning method will be compared with an instructive learning approach of structural systems for architecture students.
keywords	Structures Education, Experiential Learning, Parametric Structural Analysis, Comparative Pedagogy
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_170
id	ecaade2022_170
authors	Colonneau, Téva, Chenafi, Sabrina and Mastrorilli, Antonella
year	2022
title	Digital Intervention Methodologies and Robotic Manufacturing for the Conservation and the Restoration of 20th-Century Concrete Architecture Damaged by Material Loss
doi	https://doi.org/10.52842/conf.ecaade.2022.2.197
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. 197–206
summary	This article deals with the characterisation of robotic manufacturing systems and digital interventions adapted for the conservation and the restoration of 20th-century concrete buildings. By exploiting the potential for analysis and implementation of robotic manufacturing technologies used in the field of heritage science, two associated non- invasive, non-destructive and integrated intervention solutions are presented here, using two research approaches. Through the use of digital recording tools, digital modelling / simulation and additive manufacturing techniques, the first approach develops a direct repair process by adding material with the help of aerial robots. The second focuses on printing recyclable plastic mouldings in order to reproduce partially degraded or completely destroyed architectural details. The results of these two diverse and complementary researches, as well as their experimental approaches applied to conservation and restoration practices, aim to test the proposed robotic manufacturing- based method, regarding the criteria of transferability and methodological feasibility.
keywords	20th-Century Concrete Built Heritage, Conservation and Restoration Practices, Digital Modelling, Robotic Manufacturing, Democratisation
series	eCAADe
email
last changed	2024/04/22 07:10

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

_id	caadria2022_152
id	caadria2022_152
authors	Deshpande, Rutvik, Nisztuk, Maciej, Cheng, Cesar, Subramanian, Ramanathan, Chavan, Tejas, Weijenberg, Camiel and Patel, Sayjel Vijay
year	2022
title	Synthetic Machine Learning for Real-time Architectural Daylighting Prediction
doi	https://doi.org/10.52842/conf.caadria.2022.1.313
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 313-322
summary	"Synthetic Machine Learning‚ offers a revolutionary leap in real-time environmental analysis for conceptual architectural design. By integrating automatic synthetic data generation, artificial neural network (ANN) training and online deployment, Synthetic Machine Learning offers two main advantages over conventional simulation; First, it reduces the analysis time for a reference simulation from minutes to seconds; Second, it is possible to deploy ANN as a web service in an online design environment, which therein increases accessibility, significantly reducing simulation costs and setup time. The application of Synthetic Machine Learning to perform Daylight Autonomy (DA) and Spatial Daylight Autonomy (sDA) studies to maximise building daylighting for a given use, window to wall ratio, and floorplan arrangement is showcased through a preliminary demonstration work. Comparatively the use of algorithmically generated synthetic data versus real-world data is becoming ubiquitous in other disciplines, the advantages of this approach to the building design process are further discussed.
keywords	Daylight Autonomy, machine learning, building energy performance, synthetic data-sets, SDG 7, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_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_367
id	ecaade2022_367
authors	Doumpioti, Christina and Huang, Jeffrey
year	2022
title	Field Condition - Environmental sensibility of spatial configurations with the use of machine intelligence
doi	https://doi.org/10.52842/conf.ecaade.2022.2.067
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 67–74
summary	Within computational environmental design (CED), different Machine Learning (ML) models are gaining ground. They aim for time efficiency by automating simulation and speeding up environmental performance feedback. This study suggests an approach that enhances not the optimization but the generative aspect of environmentally driven ML processes in architectural design. We follow Stan Allen's (2009) idea of 'field conditions' as a bottom-up phenomenon according to which form and space emerge from local invisible and dynamic connections. By employing parametric modeling, environmental analysis data, and conditional Generative Adversarial Networks [cGAN] we introduce a generative approach in design that reverses the typical design process of going from formal interpretation to analysis and encourages the emergence of spatial configurations with embedded environmental intelligence. We call it Intensive-driven Environmental Design Computation [IEDC], and we employ it in a case study on a residential building typology encountered in the Mediterranean. The paper describes the process, emphasizing dataset preparation as the stage where the logic of field conditions is established. The proposed research differentiates from cGAN models that offer automatic environmental performance predictions to one that spatial predictions stem from dynamic fields.
keywords	Field Architecture, Environmental Design, Generative Design, Machine Learning, Residential Typologies
series	eCAADe
email
last changed	2024/04/22 07:10

_id	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

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