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	cdrf2019_169
id	cdrf2019_169
authors	Yubo Liu, Yihua Luo, Qiaoming Deng, and Xuanxing Zhou
year	2020
title	Exploration of Campus Layout Based on Generative Adversarial Network Discussing the Significance of Small Amount Sample Learning for Architecture
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_16
summary	This paper aims to explore the idea and method of using deep learning with a small amount sample to realize campus layout generation. From the perspective of the architect, we construct two small amount sample campus layout data sets through artificial screening with the preference of the specific architects. These data sets are used to train the ability of Pix2Pix model to automatically generate the campus layout under the condition of the given campus boundary and surrounding roads. Through the analysis of the experimental results, this paper finds that under the premise of effective screening of the collected samples, even using a small amount sample data set for deep learning can achieve a good result.
series	cdrf
email
last changed	2022/09/29 07:51

_id	sigradi2020_484
id	sigradi2020_484
authors	Pavan, Luís Henrique; Oliveira, Lucas Fernandes de; Rosa, Gabriel Machado da; Kos, José Ripper
year	2020
title	The Privacy of the Academic Community in Mapping Usage Patterns over Wi-Fi Connections
source	SIGraDi 2020 [Proceedings of the 24th Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Online Conference 18 - 20 November 2020, pp. 484-489
summary	Data sourced by mobile devices has gained importance in urban research techniques. We have conducted two different studies on the usage pattern of a university campus using Wi-Fi connections. We have evaluated this data using anonymization methods more restricted than the Brazilian Data Protection Law. One of our studies is an ongoing work on the campus reopening after the coronavirus crisis. We sought to obtain data that may constitute resources to resilient urban projects on campus. Our results highlight the liability of the more restricted anonymization method and the quality maintenance of the data representation simultaneously.
keywords	Wi-Fi connections, Data protection, Data visualization, University campus, Social infrastructure
series	SIGraDi
email
last changed	2021/07/16 11:49

_id	acadia20_94
id	acadia20_94
authors	Yoo, Wonjae; Kim, Hyoungsub; Shin, Minjae; J.Clayton, Mark
year	2020
title	BIM-Based Automatic Contact Tracing System Using Wi-Fi
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 94-101.
doi	https://doi.org/10.52842/conf.acadia.2020.1.094
summary	This study presents a BIM-based automatic contact tracing method using a stations-oriented indoor localization (SOIL) system. The SOIL system integrates BIM models and existing network infrastructure (i.e., Wi-Fi), using a clustering method to generate roomlevel occupancy schedules. In this study, we improve the accuracy of the SOIL system by including more detailed Wi-Fi signal travel sources, such as reflection, refraction, and diffraction. The results of field measurements in an educational building show that the SOIL system was able to produce room-level occupant location information with a 95.6% level of accuracy. This outcome is 2.6% more accurate than what was found in a previous study. We also describe an implementation of the SOIL system for conducting contact tracing in large buildings. When an individual is confirmed to have COVID-19, public health professionals can use this system to quickly generate information regarding possible contacts. The greatest strength of this SOIL implementation is that it has wide applicability in largescale buildings, without the need for additional sensing devices. Additional tests using buildings with multiple floors are required to further explore the robustness of the system.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_089
id	ecaade2020_089
authors	Ardic, Sabiha Irem, Kirdar, Gulce and Lima, Angela Barros
year	2020
title	An Exploratory Urban Analysis via Big Data Approach: Eindhoven Case - Measuring popularity based on POIs, accessibility and perceptual quality parameters
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 309-318
doi	https://doi.org/10.52842/conf.ecaade.2020.2.309
summary	The cities are equipped with the data as a result of the individuals' sharings and application usage. This significant amount of data has the potential to reveal relations and support user-centric decision making. The focus of the research is to examine the relational factors of the neighborhoods' popularity by implementing a big data approach to contribute to the problem of urban areas' degradation. This paper presents an exploratory urban analysis for Eindhoven at the neighborhood level by considering variables of popularity: density and diversity of points of interest (POI), accessibility, and perceptual qualities. The multi-sourced data are composed of geotagged photos, the location and types of POIs, travel time data, and survey data. These different datasets are evaluated using BBN (Bayesian Belief Network) to understand the relationships between the parameters. The results showed a positive and relatively high connection between popularity - population change, accessibility by walk - density of POIs, and the feeling of safety - social cohesion. For further studies, this approach can contribute to the decision-making process in urban development, specifically in real estate and tourism development decisions to evaluate the land prices or the hot-spot touristic places.
keywords	big data approach; neighborhood analysis; popularity; point of interest (POI); accessibility; perceptual quality
series	eCAADe
email
last changed	2022/06/07 07:54

_id	caadria2020_118
id	caadria2020_118
authors	Chow, Ka Lok and van Ameijde, Jeroen
year	2020
title	Generative Housing Communities - Design of Participatory Spaces in Public Housing Using Network Configurational Theories
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 283-292
doi	https://doi.org/10.52842/conf.caadria.2020.2.283
summary	This research-by-design project explores how public housing estates can accommodate social diversity and the appropriation of shared spaces, using qualitative and quantitative analysis of circulation networks. A case study housing estate in Hong Kong was analysed through field observations of movements and activities and as a site for the speculative re-design of shared spaces. Generative design processes were developed based on several parameters, including shortest paths, visibility integration and connectivity integration (Hillier & Hanson, 1984). Additional tools were developed to combine these techniques with optimisation of sunlight access, maximisation of views for residential towers and the provision of permeability of ground level building volumes. The project demonstrates how flexibility of use and social engagement can constitute a platform for self-organisation, similar to Jane Jacobs' notion of vibrant streets leading to active and progressive communities. It shows how computational design and configurational theories can promote a bottom-up approach for generating new types of residential environments that support participatory and diverse communities, rather than a conventional top-down approach that is perceived to embody mechanisms of social regimentation.
keywords	Urban Planning and Design; Network Configuration; Community Space and Social Interaction; Hong Kong Public Housing
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	sigradi2020_392
id	sigradi2020_392
authors	Fialho, Beatriz Campos; Codinhoto, Ricardo; Fabricio, Márcio Minto
year	2020
title	BIM and IoT for the AEC Industry: A systematic literature mapping
source	SIGraDi 2020 [Proceedings of the 24th Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Online Conference 18 - 20 November 2020, pp. 392-399
summary	The AEC industry has been facing a digital transformation for improving services involved in buildings lifecycle, fostered by two disruptive technologies: Building Information Modelling (BIM) and Internet of Things (IoT). However, the literature lacks discussions regarding applications and challenges of BIM and IoT systems in the AEC. This Systematic Literature Mapping addresses this gap through search, analysis, and classification of 75 journal article abstracts published between 2015 and 2019. An increase of articles over the period is observed, predominantly with technical and processual solutions for Construction and Operation and Maintenance. The interoperability of data is a key challenge to organizations.
keywords	Building Information Modelling, Internet of Things, Integration, Network, Smart Cities
series	SIGraDi
email
last changed	2021/07/16 11:49

_id	artificial_intellicence2019_207
id	artificial_intellicence2019_207
authors	Hao Zheng
year	2020
title	Form Finding and Evaluating Through Machine Learning: The Prediction of Personal Design Preference in Polyhedral Structures
source	Architectural Intelligence Selected Papers from the 1st International Conference on Computational Design and Robotic Fabrication (CDRF 2025)
doi	https://doi.org/https://doi.org/10.1007/978-981-15-6568-7_13
summary	3D Graphic Statics (3DGS) is a geometry-based structural design and analysis method, helping designers to generate 3D polyhedral forms by manipulating force diagrams with given boundary conditions. By subdividing 3D force diagrams with different rules, a variety of forms can be generated, resulting in more members with shorter lengths and richer overall complexity in forms. However, it is hard to evaluate the preference toward different forms from the aspect of aesthetics, especially for a specific architect with his own scene of beauty and taste of forms. Therefore, this article proposes a method to quantify the design preference of forms using machine learning and find the form with the highest score based on the result of the preference test from the architect. A dataset of forms was firstly generated, then the architect was asked to keep picking a favorite form from a set of forms several times in order to record the preference. After being trained with the test result, the neural network can evaluate a new inputted form with a score from 0 to 1, indicating the predicted preference of the architect, showing the possibility of using machine learning to quantitatively evaluate personal design taste.
series	Architectural Intelligence
email
last changed	2022/09/29 07:28

_id	acadia20_170
id	acadia20_170
authors	Li, Peiwen; Zhu, Wenbo
year	2020
title	Clustering and Morphological Analysis of Campus Context
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 170-177.
doi	https://doi.org/10.52842/conf.acadia.2020.2.170
summary	“Figure-ground” is an indispensable and significant part of urban design and urban morphological research, especially for the study of the university, which exists as a unique product of the city development and also develops with the city. In the past few decades, methods adapted by scholars of analyzing the figure-ground relationship of university campuses have gradually turned from qualitative to quantitative. And with the widespread application of AI technology in various disciplines, emerging research tools such as machine learning/deep learning have also been used in the study of urban morphology. On this basis, this paper reports on a potential application of deep clustering and big-data methods for campus morphological analysis. It documents a new framework for compressing the customized diagrammatic images containing a campus and its surrounding city context into integrated feature vectors via a convolutional autoencoder model, and using the compressed feature vectors for clustering and quantitative analysis of campus morphology.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	sigradi2020_930
id	sigradi2020_930
authors	Montás Laracuente, Nelson; Barinas Uribe, Marcos
year	2020
title	In-Situ & Computational Façade Performance Analysis: The B1- Campus A University Building Case in Sto. Dgo., Dom. Rep.
source	SIGraDi 2020 [Proceedings of the 24th Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Online Conference 18 - 20 November 2020, pp. 930-938
summary	This paper presents experimental and simulated façade thermal and humidity performance assessments concerning three (3) types of widely used façade systems in the Dominican construction market: 8” block wall, ventilated façade & curtain wall. Using indoor and outdoor temperature (/1T) and humidity differences (/1H) as indicators in order to compare said performances between the systems and, in turn, with environmental simulations approximating them, we try to diagnose weaknesses and foresee improvement avenues for sustainable façade systems in the Dominican context. The data was obtained by on-site measurements using eight (8) temperature and relative humidity sensors in a twelve (12) storey building in Santo Domingo, Dominican Republic.
keywords	Façade performance, Temperature, Relative humidity, Environmental simulation, Sensors
series	SIGraDi
email
last changed	2021/07/16 11:53

_id	ecaade2020_167
id	ecaade2020_167
authors	Newton, David, Piatkowski, Dan, Marshall, Wesley and Tendle, Atharva
year	2020
title	Deep Learning Methods for Urban Analysis and Health Estimation of Obesity
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 297-304
doi	https://doi.org/10.52842/conf.ecaade.2020.1.297
summary	In the 20th and 21st centuries, urban populations have increased dramatically with a whole host of impacts to human health that remain unknown. Research has shown significant correlations between design features in the built environment and human health, but this research has remained limited. A better understanding of this relationship could allow urban planners and architects to design healthier cities and buildings for an increasingly urbanized population. This research addresses this problem by using discriminative deep learning in combination with satellite imagery of census tracts to estimate rates of obesity. Data from the California Health Interview Survey is used to train a Convolutional Neural Network that uses satellite imagery of selected census tracts to estimate rates of obesity. This research contributes knowledge on methods for applying deep learning to urban health estimation, as well as, methods for identifying correlations between urban morphology and human health.
keywords	Deep Learning; Artificial Intelligence; Urban Planning; Health; Remote Sensing
series	eCAADe
email
last changed	2022/06/07 07:58

_id	ijac202220109
id	ijac202220109
authors	Ortner, F. Peter; Jing Zhi Tay
year	2022
title	Resilient by design: Informing pandemic-safe building redesign with computational models of resident congestion
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 1, pp. 129–144
summary	This paper describes a computational design-support tool created in response to safe-distancing measures enforced during the COVID-19 pandemic. The tool was developed for a specific use case: understanding congestion in crowded migrant worker dormitories that experienced high rates of COVID-19 transmission in 2020. Building from agent-based and network-based computational simulations, the tool presents a hybrid method for simulating building resident movements based on known or pre-determined schedules and likely itineraries. This hybrid method affords the design tool a novel approach to simultaneous exploration of spatial and temporal design scenarios. The paper demonstrates the use of the tool on an anonymised case study of a high-density migrant worker dormitory, comparing results from a baseline configuration against design variations that modify dormitory physical configuration and schedule. Comparisons between the design scenarios provide evidence for reflections on pandemic-resilient design and operation strategies for dor- mitories. A conclusions section considers the extent to which the model and case study results are applicable to other dense institutional buildings and describes the paper’s contributions to general understanding of configurational and operational aspects of resilience in the built environment.
keywords	Design for resilience, evidence-based design, design support, agent-based model, schedule-based model, network analysis
series	journal
last changed	2024/04/17 14:29

_id	caadria2020_384
id	caadria2020_384
authors	Patt, Trevor Ryan
year	2020
title	Spectral Clustering for Urban Networks
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 91-100
doi	https://doi.org/10.52842/conf.caadria.2020.2.091
summary	As planetary urbanization accelerates, the significance of developing better methods for analyzing and making sense of complex urban networks also increases. The complexity and heterogeneity of contemporary urban space poses a challenge to conventional descriptive tools. In recent years, the emergence of urban network analysis and the widespread availability of GIS data has brought network analysis methods into the discussion of urban form. This paper describes a method for computationally identifying clusters within urban and other spatial networks using spectral analysis techniques. While spectral clustering has been employed in some limited urban studies, on large spatialized datasets (particularly in identifying land use from orthoimages), it has not yet been thoroughly studied in relation to the space of the urban network itself. We present the construction of a weighted graph Laplacian matrix representation of the network and the processing of the network by eigen decomposition and subsequent clustering of eigenvalues in 4d-space.In this implementation, the algorithm computes a cross-comparison for different numbers of clusters and recommends the best option based on either the 'elbow method,' or by "eigen gap" criteria. The results of the clustering operation are immediately visualized on the original map and can also be validated numerically according to a selection of cluster metrics. Cohesion and separation values are calculated simultaneously for all nodes. After presenting these, the paper also expands on the 'silhouette' value, which is a composite measure that seems especially suited to urban network clustering.This research is undertaken with the aim of informing the design process and so the visualization of results within the active 3d model is essential. Within the paper, we illustrate the process as applied to formal grids and also historic, vernacular urban fabric; first on small, extract urban fragments and then over an entire city networks to indicate the scalability.
keywords	Urban morphology; network analysis; spectral clustering; computation
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	acadia20_218
id	acadia20_218
authors	Rossi, Gabriella; Nicholas, Paul
year	2020
title	Encoded Images
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 218-227.
doi	https://doi.org/10.52842/conf.acadia.2020.1.218
summary	In this paper, we explore conditional generative adversarial networks (cGANs) as a new way of bridging the gap between design and analysis in contemporary architectural practice. By substituting analytical finite element analysis (FEA) modeling with cGAN predictions during the iterative design phase, we develop novel workflows that support iterative computational design and digital fabrication processes in new ways. This paper reports two case studies of increasing complexity that utilize cGANs for structural analysis. Central to both experiments is the representation of information within the data set the cGAN is trained on. We contribute a prototypical representational technique to encode multiple layers of geometric and performative description into false color images, which we then use to train a Pix2Pix neural network architecture on entirely digital generated data sets as a proxy for the performance of physically fabricated elements. The paper describes the representational workflow and reports the process and results of training and their integration into the design experiments. Last, we identify potentials and limits of this approach within the design processes.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	cdrf2019_103
id	cdrf2019_103
authors	Runjia Tian
year	2020
title	Suggestive Site Planning with Conditional GAN and Urban GIS Data
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_10
summary	In architecture, landscape architecture, and urban design, site planning refers to the organizational process of site layout. A fundamental step for site planning is the design of building layout across the site. This process is hard to automate due to its multi-modal nature: it takes multiple constraints such as street block shape, orientation, program, density, and plantation. The paper proposes a prototypical and extensive framework to generate building footprints as masterplan references for architects, landscape architects, and urban designers by learning from the existing built environment with Artificial Neural Networks. Pix2PixHD Conditional Generative Adversarial Neural Network is used to learn the mapping from a site boundary geometry represented with a pixelized image to that of an image containing building footprint color-coded to various programs. A dataset containing necessary information is collected from open source GIS (Geographic Information System) portals from the city of Boston, wrangled with geospatial analysis libraries in python, trained with the TensorFlow framework. The result is visualized in Rhinoceros and Grasshopper, for generating site plans interactively.
series	cdrf
email
last changed	2022/09/29 07:51

_id	caadria2020_054
id	caadria2020_054
authors	Shen, Jiaqi, Liu, Chuan, Ren, Yue and Zheng, Hao
year	2020
title	Machine Learning Assisted Urban Filling
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 679-688
doi	https://doi.org/10.52842/conf.caadria.2020.2.679
summary	When drawing urban scale plans, designers should always define the position and the shape of each building. This process usually costs much time in the early design stage when the condition of a city has not been finally determined. Thus the designers spend a lot of time working forward and backward drawing sketches for different characteristics of cities. Meanwhile, machine learning, as a decision-making tool, has been widely used in many fields. Generative Adversarial Network (GAN) is a model frame in machine learning, specially designed to learn and generate image data. Therefore, this research aims to apply GAN in creating urban design plans, helping designers automatically generate the predicted details of buildings configuration with a given condition of cities. Through the machine learning of image pairs, the result shows the relationship between the site conditions (roads, green lands, and rivers) and the configuration of buildings. This automatic design tool can help release the heavy load of urban designers in the early design stage, quickly providing a preview of design solutions for urban design tasks. The analysis of different machine learning models trained by the data from different cities inspires urban designers with design strategies and features in distinct conditions.
keywords	Artificial Intelligence; Urban Design; Generative Adversarial Networks; Machine Learning
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	sigradi2020_128
id	sigradi2020_128
authors	Sousa, Megg; Mônaco, Denise; Martínez, Andressa; Souza, Douglas
year	2020
title	The operationalization of "A Pattern Language" by using network analysis tools
source	SIGraDi 2020 [Proceedings of the 24th Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Online Conference 18 - 20 November 2020, pp. 128-136
summary	One of the most significant public space studies, "A Pattern Language", published in 1977, is until today's background for some contemporary investigations. The aim of this paper is to propose an operationalization of the patterns' network of the book into a network analysis tool. The methodology is based on a new classification of patterns, in addition to what is initially presented in the book: "context patterns" (evidencing pre-existing conditions and potentialities) and "design patterns" (considering possibilities limited by the stakeholder at that location). The digital operationalization can enhance the analytical and predictive character of the work.
keywords	Pattern language, Network analysis tool, Christopher Alexander, Public spaces
series	SIGraDi
email
last changed	2021/07/16 11:48

_id	ascaad2021_041
id	ascaad2021_041
authors	Taºdelen, Sümeyye; Leman Gül
year	2021
title	Social Network Analysis of Digital Design Actors: Exploratory Study Covering the Journal Architectural Design
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 280-292
summary	This research asks the question of how the design knowledge production mechanism is processed differentiates digital design actors from each other in the social media/professional and academic fields of architecture. Due to the broad nature of the research question, the study focuses on academia and academia-related media through prominent architect-authors and subject titles in the literature. Bourdieu’s concept of capital is introduced, in which cultural and symbolic capital are considered part of the production values of digital design actors. Digital design actors use image-based social media tools such as Instagram effectively. The paper uses two methods: the first is a bibliographical analysis of author-texts, and the second is a social network analysis. By employing the keyword-based search from the Web of Science database, this study has managed to extract papers with full records (citations, keywords, and abstracts), with the journal Architectural Design having most publications. Considering that both academicians and professionals contribute to publications in Architectural Design, we selected all its publications between 2010-2020 for bibliometric analysis. These analysis techniques include the bibliometric network analyses and social network analysis with the focus on visualizing the algorithms and statistical calculations of well-established metrics. The research reveals the most critical nodes of the bibliometric network by calculating the appropriate central metrics. The network formed by the selected Instagram accounts of digital design actors are shown to be a small-scale network group, while the hashtags of digital design concepts are more numerous than the digital design actors.
series	ASCAAD
email
last changed	2021/08/09 13:11

_id	cdrf2019_144
id	cdrf2019_144
authors	Xuexin Duan
year	2020
title	The Development of ‘Agent-Based Parametric Semiology’ as Design Research Program
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_14
summary	A new framework, agenda and practice is called for to address the challenges and opportunities architecture must confront in the age of our computationally empowered Post-Fordist network society. This paper introduces the research agenda of ‘agent-based parametric semiology’, and explains the necessity of introducing a new tool, agent-based life-process modelling, as part of the design process, in order to cope with the new complexity and dynamism of architecture’s social functionality. The paper reviews the development of this design research program over the last 10 years. Finally, the paper describes current efforts to move from the illustrative use of life-process modelling to a scientifically grounded quantitative analysis and generative design optimization.
series	cdrf
email
last changed	2022/09/29 07:51

_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
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_18
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	caadria2020_045
id	caadria2020_045
authors	Zheng, Hao and Ren, Yue
year	2020
title	Machine Learning Neural Networks Construction and Analysis in Vectorized Design Drawings
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 707-716
doi	https://doi.org/10.52842/conf.caadria.2020.2.707
summary	Machine Learning, a recently prevalent research domain in data prediction and analysis, has been widely used in a variety of fields. In the design field, especially for architectural design, a machine learning method to learn and generate design data as pixelized images has been developed in previous researches. However, proceeding pixelized image data will cause the problems of precision loss and calculation waste, since the geometric architectural design data is efficiently stored and presented as vectorized CAD files. Thus, in this article, the author developed a specific machine learning neural network to learn and predict design drawings as vectorized data, speeding up the learning and predicting process, while improving the accuracy. First, two necessary geometric tests have been successfully done, which shows the central concept of neural network construct. Then, a design rule prediction model was built to demonstrate the methods to optimize the neural network and data structure. Lastly, a generation model based on human-made design data was constructed, which can be used to predict and generate the bedroom furniture positions by inputting the boundary data of the room, door, and window.
keywords	Machine Learning; Artificial Intelligence; Generative Design; Geometric Design
series	CAADRIA
email
last changed	2022/06/07 07:57

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