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_210
id	caadria2022_210
authors	Tabi, Salma, Sakai, Yasushi, Tung, Nguyen, Taima, Masahiro, Cheddadi, Aqil and Ikeda, Yasushi
year	2022
title	A Framework for a Gameful Collective Urbanism Based on Tokenized Location Data and Liquid Democracy: Early Prototyping of a Case Study Using E-bikes
doi	https://doi.org/10.52842/conf.caadria.2022.1.585
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 585-594
summary	The participation of citizens in designing their social and built environments is vital for the creation of sustainable cities and communities. However, in practice, collective decision-making remains challenging. Several researchers have proposed innovative models of governance to achieve a more democratic participation. This paper attempts to contribute to this topic from the viewpoint of urban planning. The objectives are twofold. First, to introduce a conceptual framework of a gameful collective process of urbanism based on location data. Second, to present an early stage of prototyping a case study using e-bikes. Research questions are elaborated as follows: How can collective processes of urban planning engage the collective intelligence and the local knowledge of the community? How to utilize technological tools to support new forms of participatory urban governance? The main contribution of this work lies in the combination of the concepts of temporal ownership of public space, tokenization of location data, and liquid democracy, to design a dynamic and gameful decision-making process that promotes collective intelligence.
keywords	Collective urbanism, Liquid democracy, Temporal ownership, Tokenization, Location data, Data dignity, Gameful design, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_507
id	caadria2022_507
authors	Bolojan, Daniel, Vermisso, Emmanouil and Yousif, Shermeen
year	2022
title	Is Language All We Need? A Query Into Architectural Semantics Using a Multimodal Generative Workflow
doi	https://doi.org/10.52842/conf.caadria.2022.1.353
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. 353-362
summary	This project examines how interconnected artificial intelligence (AI)-assisted workflows can address the limitations of current language-based models and streamline machine-vision related tasks for architectural design. A precise relationship between text and visual feature representation is problematic and can lead to "ambiguity‚ in the interpretation of the morphological/tectonic complexity of a building. Textual representation of a design concept only addresses spatial complexity in a reductionist way, since the outcome of the design process is co-dependent on multiple interrelated systems, according to systems theory (Alexander 1968). We propose herewith a process of feature disentanglement (using low level features, i.e., composition) within an interconnected generative adversarial networks (GANs) workflow. The insertion of natural language models within the proposed workflow can help mitigate the semantic distance between different domains and guide the encoding of semantic information throughout a domain transfer process.
keywords	Neural Language Models, GAN, Domain Transfer, Design Agency, Semantic Encoding, SDG 9
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	ascaad2022_004
id	ascaad2022_004
authors	Falih, Zahraa; Mahdavinejad, Mohammadjavad; Tarawneh, Deyala; Al-Mamaniori, Hamza
year	2022
title	Solar Energy Control Strategy using Interactive Modules
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. 117-138
summary	The concept of interactive canopy emerged as a notable manifestation of smart buildings in architectural endeavors, using artificial intelligence applications in computational architecture, interactive canopies came as a potential response for living organisms to combat external environmental changes as well as reduce energy consumption in buildings. This research aims to explore architecture with higher efficiency through the impact of environmentally technological factors on the design form by introducing solar energy into the design process through the implementation of interactive curtains that interact with the sun in the form of an umbrella. The main objective of the umbrellas is to protect the users from the sun's harmful rays. After designing an interactive cell using Grasshopper, the methodology follows an analytical and experimental approach, the analytical section is summarized by conducting a case study of multiple models and analyzing the techniques used in these models to discover the significant advantages and disadvantages of the design. While the experimental section demonstrates the mechanism for implementing the interactive modules. The research suggests that by designing an interactive canopy that responds to external changes and senses solar radiation in ways that when the intensity of solar radiation increases and the sun is perpendicular to the dynamic units, will lead to maintaining a more balanced level of illumination. The work efficiency is studied by simulating it by Climate Studio.
series	ASCAAD
email
last changed	2024/02/16 13:24

_id	caadria2022_317
id	caadria2022_317
authors	Grugni, Francesco, Voltolina, Marco and Cattaneo, Tiziano
year	2022
title	Use of Object Recognition AI in Community and Heritage Mapping for the Drafting of Sustainable Development Strategies Suitable for Individual Communities, With Case Studies in China, Albania and Italy
doi	https://doi.org/10.52842/conf.caadria.2022.1.717
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. 717-726
summary	In order to plan effective strategies for the sustainable development of individual communities, as prescribed by the United Nations‚ Sustainable Development Goal 11, it is necessary for designers and policy makers to gain a deep awareness of the bond that connects people to their territory. AI-driven technologies, and specifically Object Recognition algorithms, are powerful tools that can be used to this end, as they make it possible to analyse huge amounts of pictures shared on social media by residents and visitors of a specific area. A model of the emotional, subjective point of view of the members of the community is thus generated, giving new insights that can support traditional techniques such as surveys and interviews. For the purposes of this research, three case studies have been considered: the neighbourhood around Siping Road in Shanghai, China; the village of Moscopole in southeastern Albania; the rural area of Oltrep Pavese in northern Italy. The results demonstrate that a conscious use of AI-driven technologies does not necessarily imply homogenisation and flattening of individual differences: on the contrary, in all three cases diversities tend to emerge, making it possible to recognise and enhance the individuality of each community and the genius loci of each place.
keywords	sustainable communities, artificial intelligence, object recognition, social media, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2022_15
id	sigradi2022_15
authors	Jiang, Wanzhu; Wang, Jiaqi
year	2022
title	Autonomous Collective Housing Platform: Digitization, Fluidization and Materialization of Ownership
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. 15–26
summary	New social phenomena like digital nomads urge an upgrade in housing ownership. This research proposes an autonomous housing platform that shapes residential communities into adaptive and reconfigurable systems, framing a cycle of digitalization, fluidization and materialization of housing ownership. Specifically, the interactive interface carries the flexible ownership model that uses virtual space voxels as digital currency; the artificial intelligence algorithm drives the multilateral ownership negotiation and circulation, and modular robots complete the mapping from ownership status to real spaces. Taking project TESSERACT as a case study, we verified the feasibility of this method and presented expected co-living scenarios: the spaces and ownership are constantly adjusted according to demands and are always in the closest interaction with users. By exploring the ownership evolution, this research guides an integrated and inclusive housing system paradigm, triggering critical evaluation of traditional models and providing new ideas for solving housing problems in the post-digital era.
keywords	Agent-Based Systems, Digital Platform, Housing Ownership, Space Planning Algorithm, Discrete Material System
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	caadria2022_231
id	caadria2022_231
authors	Kim, Frederick Chando and Huang, Jeffrey
year	2022
title	Deep Architectural Archiving (DAA), Towards a Machine Understanding of Architectural Form
doi	https://doi.org/10.52842/conf.caadria.2022.1.727
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. 727-736
summary	With the ‚digital turn‚, machines now have the intrinsic capacity to learn from big data in order to understand the intricacies of architectural form. This paper explores the research question: how can architectural form become machine computable? The research objective is to develop "Deep Architectural Archiving‚ (DAA), a new method devised to address this question. DAA consists of the combination of four distinct steps: (1) Data mining, (2) 3D Point cloud extraction, (3) Deep form learning, as well as (4) Form mapping and clustering. The paper discusses the DAA method using an extensive dataset of architecture competitions in Switzerland (with over 360+ architectural projects) as a case study resource. Machines learn the particularities of forms using 'architectural' point clouds as an opportune machine-learnable format. The result of this procedure is a multidimensional, spatialized, and machine-enabled clustering of forms that allows for the visualization of comparative relationships among form-correlated datasets that exceeds what the human eye can generally perceive. Such work is necessary to create a dedicated digital archive for enhancing the formal knowledge of architecture and enabling a better understanding of innovation, both of which provide architects a basis for developing effective architectural form in a post-carbon world.
keywords	artificial intelligence, deep learning, architectural form, architectural competitions, architectural archive, 3D dataset, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2022_168
id	sigradi2022_168
authors	Koh, Immanuel
year	2022
title	Palette2Interior Architecture: From Syntactic and Semantic Colour Palettes to Generative Interiors with Deep 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. 187–198
summary	Colour palettes have long played a significant role in not only capturing design ambience (e.g., as mood boards), but more significantly, in translating an abstract intuition into an explicit ordering mechanism for design representation and synthesis, whether it is in the discipline of graphic design, interior design or architectural design. Might this difficult process of design synthesis from a low-dimensional colour input domain to a high-dimensional spatial design output domain be computationally mapped? Using today’s generative adversarial networks (GANs), the paper aims to investigate this plausibility, and in doing so, hoping to envision an AI-augmented design workflow and tooling. Newly-created datasets are made procedurally and used to train three different types of deep learning models in the specific context of generating living room interior layouts. The results suggest that a combination of syntactic and semantic generative processes is necessary for a critical appropriation of such AI models
keywords	Machine Learning, Artificial Intelligence, Deep Neural Networks, Colour Palette, Interior Design
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	ecaade2022_176
id	ecaade2022_176
authors	Kotov, Anatolii, Starke, Rolf and Vukorep, Ilija
year	2022
title	Spatial Agent-based Architecture Design Simulation Systems
doi	https://doi.org/10.52842/conf.ecaade.2022.2.105
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. 105–112
summary	This paper presents case studies and analysis of agent-based reinforcement learning (RL) systems towards practical applications for specific architecture/engineering tasks using Unity 3D-based simulation methods. Finding and implementing sufficient abstraction for architecture and engineering problems to be solved by agent-based systems requires broad architectural knowledge and the ability to break down complex problems. Modern artificial intelligence (AI) and machine learning (ML) systems based on artificial neural networks can solve complex problems in different domains such as computer vision, language processing, and predictive maintenance. The paper will give a theoretical overview, such as more theoretical abstractions like zero-sum games, and a comparison of presented games. The application section describes a possible categorization of practical usages. From more general applications to more narrowed ones, we explore current possibilities of RL application in the field of relatable problems. We use the Unity 3D engine as the basis of a robust simulation environment.
keywords	AI Aided Architecture, Reinforcement Learning, Agent Simulation
series	eCAADe
email
last changed	2024/04/22 07:10

_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	acadia23_v1_242
id	acadia23_v1_242
authors	Noel, Vernelle A.
year	2023
title	Carnival + AI: Heritage, Immersive virtual spaces, and Machine Learning
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 1: Projects Catalog of the 43rd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 242-245.
summary	Built on a Situated Computations framework, this project explores preservation, reconfiguration, and presentation of heritage through immersive virtual experiences, and machine learning for new understandings and possibilities (Noel 2020; 2017; Leach and Campo 2022; Leach 2021). Using the Trinidad and Tobago Carnival - hereinafter referred to as Carnival - as a case study, Carnival + AI is a series of immersive experiences in design, culture, and artificial intelligence (AI). These virtual spaces create new digital modes of engaging with cultural heritage and reimagined designs of traditional sculptures in the Carnival (Noel 2021). The project includes three virtual events that draw on real events in the Carnival: (1) the Virtual Gallery, which builds on dancing sculptures in the Carnival and showcases AI-generated designs; (2) Virtual J’ouvert built on J’ouvert in Carnival with AI-generated J’ouvert characters specific; and (3) Virtual Mas which builds on the masquerade.
series	ACADIA
type	project
email
last changed	2024/04/17 13:58

_id	ijac202220308
id	ijac202220308
authors	Rodrigues, Ricardo C; Rovenir B Duarte
year	2022
title	Generating floor plans with deep learning: A cross-validation assessment over different dataset sizes
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 3, pp. 630–644
summary	The advent of deep learning has enabled a series of opportunities; one of them is the ability to tackle subjective factors on the floor plan design and make predictions though spatial semantic maps. Nonetheless, the amount available of data grows exponentially on a daily basis, in this sense, this research seeks to investigate deep generative methods of floor plan design and its relationship between data volume, with training time, quality and diversity in the outputs; in other words, what is the amount of data required to rapidly train models that return optimal results. In our research, we used a variation of the Conditional Generative Adversarial Network algorithm, that is, Pix2pix, and a dataset of approximately 80 thousand images to train 10 models and evaluate their performance through a series of computational metrics. The results show that the potential of this data-driven method depends not only on the diversity of the training set but also on the linearity of the distribution; therefore, high-dimensional datasets did not achieve good results. It is also concluded that models trained on small sets of data (800 images) may return excellent results if given the correct training instructions (Hyperparameters), but the best baseline to this generative task is in the mid-term, using around 20 to 30 thousand images with a linear distribution. Finally, it is presented standard guidelines for dataset design, and the impact of data curation along the entire process
keywords	Dataset Reduction, Pix2pix, Artificial Intelligence, Deep Generative Models, GANs
series	journal
last changed	2024/04/17 14:30

_id	ascaad2022_060
id	ascaad2022_060
authors	Senem, Mehmet; Koc, Mustafa; Tuncay, Hayriye; As, Imdat
year	2022
title	Using Deep Learning to Generate Front and Backyards in Landscape Architecture
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 2-16
summary	The use of artificial intelligence (AI) engines in the design disciplines is a nascent field of research, which became very popular over the last decade. In particular, deep learning (DL) and related generative adversarial networks (GANs) proved to be very promising. While there are many research projects exploring AI in architecture and urban planning, e.g., in order to generate optimal floor layouts, massing models, evaluate image quality, etc., there are not many research projects in the area of landscape architecture - in particular the design of two-dimensional garden layouts. In this paper, we present our work using GANs to generate optimal front- and backyard layouts. We are exploring various GAN engines, e.g., DCGAN, that have been successfully used in other design disciplines. We used supervised and unsupervised learning utilizing a massive dataset of about 100,000 images of front- and backyard layouts, with qualitative and quantitative attributes, e.g., idea and beauty scores, as well as functional and structural evaluation scores. We present the results of our work, i.e., the generation of garden layouts, and their evaluation, and speculate on how this approach may help landscape architects in developing their designs. The outcome of the study may also be relevant to other design disciplines.
series	ASCAAD
email
last changed	2024/02/16 13:29

_id	architectural_intelligence2022_4
id	architectural_intelligence2022_4
authors	Yihui Li, Wen Gao & Borong Lin
year	2022
title	From type to network: a review of knowledge representation methods in architecture intelligence design
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00006-9
source	Architectural Intelligence Journal
summary	With the rise of the next generation of artificial intelligence driven by knowledge and data, the research on knowledge representation in architecture is also receiving widespread attention from the academia. This paper sorts out the evolution of architectural knowledge representation methods in the history of architecture, and summarizes three progressive representation frameworks of their development with type, pattern and network. By searching these three keywords in the Web of Science Core Collection among 4867 publications from 1990 to 2021, the number of publications in the past 5 years raised more than 50%, which show significant research interest in architecture industry in recent years. Among them, the first two are static declarative knowledge representation methods, while the network-based knowledge representation method also includes procedural knowledge representation methods and provides a way for knowledge association. This means the network representation has more advantage in terms of the logical completeness of knowledge representation, and accounts for 67% of the current research on knowledge representation in architecture. In the context of the rapid development of artificial intelligence, this method can realize the construction of architectural knowledge system and greatly improve the work efficiency of the building industry. On the other hand, in the face of carbon-neutral sustainable development scenarios, using knowledge representation, building performance knowledge and design knowledge could be expressed in a unified manner, and a personalized and efficient workflow for performance-oriented scheme design and optimization would be achieved.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	ascaad2022_024
id	ascaad2022_024
authors	Yonder, Veli
year	2022
title	Using Artificial Neural Networks and Space Syntax Techniques to Understand Mass Housing Design Parameters
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. 283-299
summary	The design of mass housing is a complex process that involves the use of a large number of components and parameters. The field of design has unavoidably been changed by the impact of digitalization, which has resulted in the proliferation of computational design models, data structures, artificial intelligence, and an algorithmic way of thinking. Artificial neural networks, space syntax methodologies, predefined rules will help shape the steps of the schematic design process and establish certain limitations. Within the confines of this research, predefined guidelines were used to bring about geometric variances in the design of mass houses. Both traditional and digital instruments were utilized in the process. Methodologies based on artificial neural network models and space syntax techniques were utilized to investigate case studies and develop prototypes. The artificial neural network model is designed to understand the factors affecting mass housing design parameters. The importance percentages of the parameters were determined according to the outputs of this model. Besides, methodologies based on space syntax have had a significant impact, both on decision-making processes and on feedback-based design. In this study, several digital tools were used to analyze such as visibility graph analyzes, node-based techniques, and isovist analysis. In the section devoted to the conclusion, the comparison of the various prototypes that were obtained, the findings of the space syntax analysis, and the various stages of model development are discussed.
series	ASCAAD
email
last changed	2024/02/16 13:24

_id	caadria2024_238
id	caadria2024_238
authors	Barashkov, Julia
year	2024
title	Re-commoning Urban Spaces From the Bottom-Up: Empowering Urban Communities: A Digital Toolbox for Bottom-Up Intervention in Kottbusser Tor, Berlin
doi	https://doi.org/10.52842/conf.caadria.2024.2.241
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. 241–250
summary	Urban planners and bodies of urban governance maintain an inherent divergence in the internal structures governing the integration of local communities and the fulfilment of their needs. Theoretical frameworks and contemporary digital tools often reinforce those inherent power imbalances, further exacerbating the disconnection between communities and their urban environments. While public administration holds the authority to access and utilise extensive datasets derived from digital urban statistics, they frequently lack the grassroots, bottom-up intelligence that local communities possess. On the other hand, local communities find themselves denied access to their urban data and face limitations in both capability and authority to generate meaningful changes in the urban fabric. This paper explores the empowerment of local communities with the tools, knowledge, and skillsets necessary to act upon their inherent bottom-up local intelligence to enable community-generated interventions and solutions to urban challenges. Through the context of Kottbusser Tor, Berlin, Germany, this study develops a toolbox designed to equip communities with the means to facilitate self-organised actions.
keywords	participatory urbanism, digital tools, self-organisation, community empowerment, ethical smart urbanism.
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	sigradi2021_234
id	sigradi2021_234
authors	Al Nouri, Mhd Ziwar, Baghdadi, Bilal and Khateeb, Nairooz
year	2021
title	Re-coding Post-War Syria: The Role of Data Collection & Objective Investigations in PostWar Smart City
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 127–145
summary	Re-coding post-war Syria is an ongoing research and data platform, focused on innovation and collecting comprehensive, infrastructural and socioeconomic analytics, synchronization data, by using AI driven to give a more transparent image of innovating a new methodology to regenerate the future of post-war smart cities into advanced and sustainable urban environments in a smarter way (Fig. 1). The pressure to achieve a rapid Post-war smart city without clear strategy and comprehensive analysis of all aspects will cause a particularly catastrophic collapse in the interconnected social structure, services, education and health care system, leaving a long-term impact on the society. This paper presents the current status of the Research & Documentation methodology in the Data Collection phase by the objective investigations conducted through a series of local and international workshops species developed in this research called “Re-Coding“, offering consequent direct ground surveys, statistics and documentation study of the targeted areas, merging professionalism and youth power with local community to detect an open source data used as a tool to re-generate a precarious area towards a new methodology.
keywords	Post-War Smart cities, Collecting Data, Local community, Objective Investigations, Artificial intelligence
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	ecaade2023_125
id	ecaade2023_125
authors	Baºarir, Lale, Çiçek, Selen and Koç, Mustafa
year	2023
title	Demystifying the patterns of local knowledge: The implicit relation of local music and vernacular architecture
doi	https://doi.org/10.52842/conf.ecaade.2023.2.791
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 1, Graz, 20-22 September 2023, pp. 791–800
summary	As the zeitgeist suggests, the development of novel design output using Artificial Neural Networks (ANNs) is becoming an important milestone in the architectural design discourse. With the recent encounter of the computational design realm with the diffusion models, it becomes even easier to generate 2D and 3D design outputs. Yet, the utilization of machine learning tools within design computing domains is confined to generating or classifying visual and encoded data. However, it is critical to evaluate the untapped potentials of machine learning technologies in terms of illuminating the implicit correlations and links underlying distinct concepts and themes across a wide range of technical domains. With the ongoing research project named “Local Intelligence", we hypothesized that the local knowledge of a certain location might be conceptualized as a distributed network to connect different forms of local knowledge. As the first case of the project, we tried to reinstate a commonality between the local music and vernacular architecture, for which we trained generative adversarial network (GAN) models with the visual spectrograms translated from the audio data of the local songs and images of vernacular architectural instances from a defined geography. The two multi-modal GAN models differ in terms of the inherent convolutional layers and data pairing process. The outcomes demonstrated that both GAN models can learn how to depict vernacular architectural features from the rhythmic pattern of the songs in various patterns. Consequently, the implicit relations between music and architecture in the initial findings come one step closer to being demystified. Thus, the process and generative outcomes of the two models are compared and discussed in terms of the legibility of the architectural features, by taking the original vernacular architectural image dataset as the ground truth.
keywords	Local Intelligence, Machine Learning, Generative Adversarial Network (GAN), Local Music, Vernacular Architecture
series	eCAADe
email
last changed	2023/12/10 10:49

_id	ddss2004_ra-33
id	ddss2004_ra-33
authors	Diappi, L., P. Bolchim, and M. Buscema
year	2004
title	Improved Understanding of Urban Sprawl Using Neural Networks
source	Van Leeuwen, J.P. and H.J.P. Timmermans (eds.) Recent Advances in Design & Decision Support Systems in Architecture and Urban Planning, Dordrecht: Kluwer Academic Publishers, ISBN: 14020-2408-8, p. 33-49
summary	It is widely accepted that the spatial pattern of settlements is a crucial factor affecting quality of life and environmental sustainability, but few recent studies have attempted to examine the phenomenon of sprawl by modelling the process rather than adopting a descriptive approach. The issue was partly addressed by models of land use and transportation which were mainly developed in the UK and US in the 1970s and 1980s, but the major advances were made in the area of modelling transportation, while very little was achieved in the area of spatial and temporal land use. Models of land use and transportation are well-established tools, based on explicit, exogenouslyformulated rules within a theoretical framework. The new approaches of artificial intelligence, and in particular, systems involving parallel processing, (Neural Networks, Cellular Automata and Multi-Agent Systems) defined by the expression “Neurocomputing”, allow problems to be approached in the reverse, bottom-up, direction by discovering rules, relationships and scenarios from a database. In this article we examine the hypothesis that territorial micro-transformations occur according to a local logic, i.e. according to use, accessibility, the presence of services and conditions of centrality, periphericity or isolation of each territorial “cell” relative to its surroundings. The prediction capabilities of different architectures of supervised Neural networks are implemented to the south Metropolitan area of Milan at two different temporal thresholds and discussed. Starting from data on land use in 1980 and 1994 and by subdividing the area into square cells on an orthogonal grid, the model produces a spatial and functional map of urbanisation in 2008. An implementation of the SOM (Self Organizing Map) processing to the Data Base allows the typologies of transformation to be identified, i.e. the classes of area which are transformed in the same way and which give rise to territorial morphologies; this is an interesting by-product of the approach.
keywords	Neural Networks, Self-Organizing Maps, Land-Use Dynamics, Supervised Networks
series	DDSS
last changed	2004/07/03 22:13

_id	architectural_intelligence2023_5
id	architectural_intelligence2023_5
authors	Qiaoming Deng, Xiaofeng Li, Yubo Liu & Kai Hu
year	2023
title	Exploration of three-dimensional spatial learning approach based on machine learning–taking Taihu stone as an example
doi	https://doi.org/https://doi.org/10.1007/s44223-023-00023-2
source	Architectural Intelligence Journal
summary	Under the influence of globalization, the transformation of traditional architectural space is vital to the growth of local architecture. As an important spatial element of traditional gardens, Taihu stone has the image qualities of being “thin, wrinkled, leaky and transparent” The “transparency” and “ leaky” of Taihu stone reflect the connectivity and irregularity of Taihu stone’s holes, which are consistent with the contemporary architectural design concepts of fluid space and transparency. Nonetheless, relatively few theoretical studies have been conducted on the spatial analysis and design transformation of Taihu stone. Using machine learning, we attempt to extract the three-dimensional spatial variation pattern of Taihu stone in this paper. This study extracts 3D spatial features for experiments using artificial neural networks (ANN) and generative adversarial networks (GAN). In order to extract 3D spatial variation patterns, the machine learning model learns the variation patterns between adjacent sections. The trained machine learning model is capable of generating a series of spatial sections with the spatial variation pattern of the Taihu stone. The purpose of the experimental results is to compare the performance of various machine learning models for 3D space learning in order to identify a model with superior performance. This paper also presents a novel concept for machine learning to master continuous 3D spatial features.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

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