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	ecaade2022_161
id	ecaade2022_161
authors	Kharbanda, Kritika, Papadopoulou, Iliana, Pouliou, Panagiota, Daw, Karim, Belwadi, Anirudh and Loganathan, Hariprasath
year	2022
title	LearnCarbon - A tool for machine learning prediction of global warming potential from abstract designs
doi	https://doi.org/10.52842/conf.ecaade.2022.2.601
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. 601–610
summary	The new construction that is projected to take place between 2020 and 2040 plays a critical role in embodied carbon emissions. The change in material selection is inversely proportional to the budget, as the project progresses. Given the fact that early-stage design processes often do not include environmental performance metrics, there is an opportunity to investigate a toolset that enables early-stage design processes to integrate this type of analysis into the preferred workflow of concept designers. The value here is that early-stage environmental feedback can inform the crucial decisions that are made in the beginning, giving a greater chance for a building with better environmental performance in terms of its life cycle. This paper presents the development of a tool called LearnCarbon, as a plugin of Rhino3d, used to educate architects and engineers in the early stages about the environmental impact of their design. It facilitates two neural networks trained with the Embodied Carbon Benchmark Study by Carbon Leadership Forum, which learn the relationship between building geometry, typology, and structure with the Global Warming potential in tCO2e. The first one, a regression model, is able to predict the GWP based on the massing model of a building, along with information about typology and location. The second one, a classification model, predicts the construction type given a massing model and target GWP. LearnCarbon can help improve the building life cycle impact significantly, through early predictions of the structure’s material, and can be used as a tool for facilitating sustainable discussions between the architect and the client.
keywords	Machine Learning, Carbon Emissions, LCA, Rhino Plug-in
series	eCAADe
email
last changed	2024/04/22 07:10

_id	cdrf2019_229
id	cdrf2019_229
authors	Jingyi Li and Hong Chen
year	2020
title	Optimization and Prediction of Design Variables Driven by Building Energy Performance—A Case Study of Office Building in Wuhan
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_22
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	This research focuses on the energy performance of office building in Wuhan. The research explored and predicted the optimal solution of design variables by Multi-Island Genetic Algorithm (MIGA) and RBF Artificial neural networks (RBF-ANNs). Research analyzed the cluster centers of design variable by K-means cluster method. In the study, the RBF-ANNs model was established by 1,000 simulation cases. The RMSE (root mean square error) of the RBF-ANNs model in different energy aspects does not exceed 15%. Comparing to the reference case (the largest energy consumption case in the optimization), the 214 elite cases in RBF-ANNs model save at least 37.5% energy. By the cluster centers of the design variables in the elite cases, the study summarized the benchmark of 14 design variables and also suggested a building energy guidance for Wuhan office building design.
series	cdrf
email
last changed	2022/09/29 07:51

_id	caadria2020_259
id	caadria2020_259
authors	Rhee, Jinmo, Veloso, Pedro and Krishnamurti, Ramesh
year	2020
title	Integrating building footprint prediction and building massing - an experiment in Pittsburgh
doi	https://doi.org/10.52842/conf.caadria.2020.2.669
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. 669-678
summary	We present a novel method for generating building geometry using deep learning techniques based on contextual geometry in urban context and explore its potential to support building massing. For contextual geometry, we opted to investigate the building footprint, a main interface between urban and architectural forms. For training, we collected GIS data of building footprints and geometries of parcels from Pittsburgh and created a large dataset of Diagrammatic Image Dataset (DID). We employed a modified version of a VGG neural network to model the relationship between (c) a diagrammatic image of a building parcel and context without the footprint, and (q) a quadrilateral representing the original footprint. The option for simple geometrical output enables direct integration with custom design workflows because it obviates image processing and increases training speed. After training the neural network with a curated dataset, we explore a generative workflow for building massing that integrates contextual and programmatic data. As trained model can suggest a contextual boundary for a new site, we used Massigner (Rhee and Chung 2019) to recommend massing alternatives based on the subtraction of voids inside the contextual boundary that satisfy design constraints and programmatic requirements. This new method suggests the potential that learning-based method can be an alternative of rule-based design methods to grasp the complex relationships between design elements.
keywords	Deep Learning; Prediction; Building Footprint; Massing; Generative Design
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	cdrf2022_209
id	cdrf2022_209
authors	Yecheng Zhang, Qimin Zhang, Yuxuan Zhao, Yunjie Deng, Feiyang Liu, Hao Zheng
year	2022
title	Artificial Intelligence Prediction of Urban Spatial Risk Factors from an Epidemic Perspective
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_18
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	From the epidemiological perspective, previous research methods of COVID-19 are generally based on classical statistical analysis. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. We take the Spatio-temporal data of people infected with new coronary pneumonia before February 28 in Wuhan in 2020 as the research object. We use kriging spatial interpolation technology and core density estimation technology to establish the epidemic heat distribution on fine grid units. We further examine the distribution of nine main spatial risk factors, including agencies, hospitals, park squares, sports fields, banks, hotels, Etc., which are tested for the significant positive correlation with the heat distribution of the epidemic. The weights of the spatial risk factors are used for training Generative Adversarial Network models, which predict the heat distribution of the outbreak in a given area. According to the trained model, optimizing the relevant environment design in urban areas to control risk factors effectively prevents and manages the epidemic from dispersing. The input image of the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area.
series	cdrf
email
last changed	2024/05/29 14:02

_id	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
doi	https://doi.org/10.52842/conf.caadria.2020.2.707
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
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

_id	caadria2020_115
id	caadria2020_115
authors	Zhong, Jia Ding, Chao, Sara, Ming Chun and Tsou, Jin Yeu
year	2020
title	Establishing a Prediction Model for Better Decision Making Regarding Urban Green Planning in a High-density Urban Context
doi	https://doi.org/10.52842/conf.caadria.2020.1.517
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 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 517-526
summary	This paper presents a prototype of a prediction model. The model helps to improve decision making regarding urban green patch planning. This process is achieved by the model predicting the response of thermal comfort conditions in an urban green patch to different planning decisions. This process is demonstrated via an investigation of variations in urban density. The model features a surface temperature mapping approach, which assigns surface temperature data acquired through field-measurement to solid surfaces in CFD simulations based on the shading state. Besides, trees are simulated in a systematic way, and the model combines CFD simulations with PET values, the processes of which are also demonstrated in this paper.
keywords	Urban Green Planning; Decision Making; Thermal Comfort; CFD
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	ecaade2020_515
id	ecaade2020_515
authors	Chadha, Kunaljit, Dubor, Alexandre, Puigpinos, Laura and Rafols, Irene
year	2020
title	Space Filling Curves for Optimising Single Point Incremental Sheet Forming using Supervised Learning Algorithms
doi	https://doi.org/10.52842/conf.ecaade.2020.1.555
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. 555-562
summary	Increasing use of computational design tools have led to an increase in the demand for mass customised fabrication, rendering decades old industrial CAD-CAM protocols limiting for such fabrication processes. This bespoke demand of components has led to a unified workflow between design strategies and production techniques. Recent advances in computation have allowed us to predict and register the tolerances of fabrication before and while being fabricated. Procedural algorithms are a set of novel problem-solving methods and have been attracting considerable attention for their good performance.They follow a procedural way of iteration with an established way of behavior.In the particular case of Incremental Sheet forming (ISF), these algorithms can realize several functions such as edge detection and segmentation required for optimizing machining time and accuracy.In this context, this paper presents a methodology to optimize long-drawn-out ISF operation by using geometrical intervention informed by supervised machine learning algorithms.
keywords	Procedural Algorithms; Incremental Sheet Forming; Robotic Cold forming; Mass Customization
series	eCAADe
email
last changed	2022/06/07 07:55

_id	caadria2020_431
id	caadria2020_431
authors	Kim, Jong Bum, Balakrishnan, Bimal and Aman, Jayedi
year	2020
title	Environmental Performance-based Community Development - A parametric simulation framework for Smart Growth development in the United States
doi	https://doi.org/10.52842/conf.caadria.2020.1.873
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 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 873-882
summary	Smart Growth is an urban design movement initiated by Environmental Protection Agency (EPA) in the United States (Smart Growth America, 2019). The regulations of Smart Growth control urban morphologies such as building height, use, position, section configurations, faÃ§ade configurations, and materials, which have an explicit association with energy performances. This research aims to analyze and visualize the impact of Smart Growth developments on environmental performances. This paper presents a parametric modeling and simulation framework for Smart Growth developments that can model the potential community development scenarios, simulate the environmental footprints of each parcel, and visualize the results of modeling and simulation. We implemented and examined the proposed framework through a case study of two Smart Growth regulations: Columbia Unified Development Code (UDC) in Missouri (City of Columbia Missouri, 2017) and Overland Park Downtown Form-based Code (FBC) in Kansas City (City of Overland Park, 2017, 2019). Last, we discuss the implementation results, the limitations of the proposed framework, and the future work. We anticipate that the proposed method can improve stakeholders' understanding of how Smart Growth developments are associated with potential environmental footprints from an expeditious and thorough exploration of what-if scenarios of the multiple development schemes.
keywords	Smart Growth; Building Information Modeling (BIM); Parametric Simulation; Solar Radiation
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	ecaade2020_334
id	ecaade2020_334
authors	Ntzoufras, Sotirios, Oungrinis, Konstantinos-Alketas, Liapi, Marianthi and Papamanolis, Antonios
year	2020
title	Robotic Swarms in Architectural Design - A communication platform bridging design analysis and robotic construction
doi	https://doi.org/10.52842/conf.ecaade.2020.2.453
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. 453-462
summary	The research work fueling this paper examines ?ptimal approaches for bridging design analysis and robotic spatial construction. In this context, the paper presents the development of a unified platform for managing a swarm of robotic fabrication agents. The goal is the development of a streamlined methodology that guides the conversion of a design model into construction data code that can be assigned to the robotic swarm for fabrication.The work focuses on bridging architectural design platforms and distributed automation processes, on the one hand, and on the other, it targets the development of a functional management tool for adjusting and optimizing fabrication. A crucial parameter considered is the monitoring and assessment of all stages of the proposed process. This involves a constant exchange of information between the various actors, such as the swarm agents, the construction data and the designer - user. As a result, the construction process is treated as a constant reassessment and re-adjustment of the design parameters rather than the linear result of the original set of construction data. Therefore, the proposed system cannot be described as reactive, but acts responsively in a ``sensible'' manner.
keywords	Swarm Robotics; Adaptive Fabrication; Robotic Construction Communication Platform; Sensible System
series	eCAADe
email
last changed	2022/06/07 08:00

_id	ecaade2020_115
id	ecaade2020_115
authors	Azambuja Varela, Pedro and Sousa, José Pedro
year	2020
title	Liquid Stereotomy - the Tamandua Vault
doi	https://doi.org/10.52842/conf.ecaade.2020.2.361
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. 361-370
summary	A renewed interest in stereotomy, narrowly entwined with digital technologies, has allowed for the recovery and proposal of new techniques and expressions in this building approach. A new classification scheme for stereotomy research allows for the framing of various aspects related to this discipline, including a newly developed fabrication system specially tailored for the wedge-shaped voussoirs. This fabrication system is based in a reusable mould which may assume an infinite number of geometries, avoiding the wasteful discarding of material found in subtractive strategies. The usage of a mould also allows for more sustainable materials to be employed, catering to current challenges. The strategies subject for demonstration in this project rely on various bottom-up approaches, which involve particle physic simulations such as a hanging model to compute an optimal stereo-funicular shape, or spring mechanisms to find optimal coplanar solutions. The proposed mechanisms work in a parametric algorithmically environment, able to handle dozens of uniquely different voussoirs at the same time. Together with the automatic translation to fabrication data, the proposed shape complexity would hardly be built with classic tools. The Tamandua Vault project has the purpose of exemplifying the possibilities of an updated stereotomy, while its design demonstrates current strategies that may be employed in the resolution of complex geometrical problems and bespoke fabrication of construction components for stereotomy.
keywords	stereotomy; digital design; digital fabrication; compression; sustainability
series	eCAADe
email
last changed	2022/06/07 07:54

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

_id	caadria2020_342
id	caadria2020_342
authors	Han, Yoojin and Lee, Hyunsoo
year	2020
title	A Deep Learning Approach for Brand Store Image and Positioning - Auto-generation of Brand Positioning Maps Using Image Classification
doi	https://doi.org/10.52842/conf.caadria.2020.2.689
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. 689-696
summary	This paper presents a deep learning approach to measuring brand store image and generating positioning maps. The rise of signature brand stores can be explained in terms of brand identity. Store design and architecture have been highlighted as effective communicators of brand identity and position but, in terms of spatial environment, have been studied solely using qualitative approaches. This study adopted a deep learning-based image classification model as an alternative methodology for measuring brand image and positioning, which are conventionally considered highly subjective. The results demonstrate that a consistent, coherent, and strong brand identity can be trained and recognized using deep learning technology. A brand positioning map can also be created based on predicted scores derived by deep learning. This paper also suggests wider uses for this approach to branding and architectural design.
keywords	Deep Learning; Image Classification; Brand Identity; Brand Positioning Map; Brand Store Design
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	acadia20_658
id	acadia20_658
authors	Ho, Brian
year	2020
title	Making a New City Image
doi	https://doi.org/10.52842/conf.acadia.2020.1.658
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. 658-667.
summary	This paper explores the application of computer vision and machine learning to streetlevel imagery of cities, reevaluating past theory linking urban form to human perception. This paper further proposes a new method for design based on the resulting model, where a designer can identify areas of a city tied to certain perceptual qualities and generate speculative street scenes optimized for their predicted saliency on labels of human experience. This work extends Kevin Lynch’s Image of the City with deep learning: training an image classification model to recognize Lynch’s five elements of the city image, using Lynch’s original photographs and diagrams of Boston to construct labeled training data alongside new imagery of the same locations. This new city image revitalizes past attempts to quantify the human perception of urban form and improve urban design. A designer can search and map the data set to understand spatial opportunities and predict the quality of imagined designs through a dynamic process of collage, model inference, and adaptation. Within a larger practice of design, this work suggests that the curation of archival records, computer science techniques, and theoretical principles of urbanism might be integrated into a single craft. With a new city image, designers might “see” at the scale of the city, as well as focus on the texture, color, and details of urban life.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	cdrf2019_68
id	cdrf2019_68
authors	Pierre Cutellic
year	2020
title	Growing Shapes with a Generalised Model from Neural Correlates of Visual Discrimination
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_7
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	This paper focuses on the application of visual Event-Related Potentials (ERP) in better generalisations for design and architectural modelling. It makes use of previously built techniques and trained models on EEG signals of a singular individual and observes the robustness of advanced classification models to initiate the development of presentation and classification techniques for enriched visual environments by developing an iterative and generative design process of growing shapes. The pursued interest is to observe if visual ERP as correlates of visual discrimination can hold in structurally similar, but semantically different, experiments and support the discrimination of meaningful design solutions. Following bayesian terms, we will coin this endeavour a Design Belief and elaborate a method to explore and exploit such features decoded from human visual cognition.
series	cdrf
email
last changed	2022/09/29 07:51

_id	ecaade2020_283
id	ecaade2020_283
authors	Sebestyen, Adam and Tyc, Jakub
year	2020
title	Machine Learning Methods in Energy Simulations for Architects and Designers - The implementation of supervised machine learning in the context of the computational design process
doi	https://doi.org/10.52842/conf.ecaade.2020.1.613
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. 613-622
summary	Application of Machine Learning (ML) in the field of architecture is a worthwhile topic to discuss in the context of digital architecture. Authors propose to extend this discussion, presenting an integrated ML pipeline built with the state-of-the-art data science tools. To investigate the affordances of such pipelines, an ML model being able to predict the environmental metrics of a generalized facade system is created. This approach is valid for arbitrary facades, as long as the proposed design could be discretized in the form analogous to the data generated for the ML model training. The presented experiment evaluates the precision of the sunlight hours and radiation values predictions, aiming at the application in the early design phases. Conducted investigation builds up on the knowledge embedded in the Grasshopper and Ladybug toolsets. Potential application of Convolutional Neural Networks and categorical datasets for classifications tasks to increase the precision of the ML models have been identified. Possibility to extend the approach beyond the workspace of Rhino and Grasshopper is suggested. Further research outlook, investigating the data pattern recognition capabilities in relation to the three-dimensional forms discretized as multidimensional arrays, is stated.
keywords	Machine Learning; Environmental Analysis; Parametric Design; Supervised Learning
series	eCAADe
email
last changed	2022/06/07 08:00

_id	caadria2020_354
id	caadria2020_354
authors	Tomarchio, Ludovica, He, Peijun, Herthogs, Pieter and Tuncer, Bige
year	2020
title	Cultural-Smart City: Establishing New Data-informed Practices to Plan Culture in Cities
doi	https://doi.org/10.52842/conf.caadria.2020.2.081
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. 81-90
summary	The idea of the Creative City has encouraged planners to develop cultural policies to support creative economies, city branding, urban identity and urban quality. On the other side, the concept of Smart City introduced the possibility to create, collect and analyse data to inform decisions on cities. The two city agendas overlap in different ways, creating a Smart cultural city nexus, that propose similar goals and mixed methodologies, like the possibility to inform planning processes with big data-based technologies. In line with this direction, we introduced conceptual and methodological tools: the first tool is the definition of Hybrid Art Spaces, the second tool is the Singapore Art Maps (SAM), which uses social media data to locate art venues in cities (Tomarchio et al. 2016); the third tool is the Social Media Art Model, which establishes a relationship between social media production and art venues features. While these tools have already shown interesting analytics outcomes (Tomarchio et al. 2016), it is important to validate their utility among practitioners and to set protocols of practices. This paper presents results from semi-structured interviews and a focus group, as a first step towards assessing the usefulness of our three tools for cultural planning practice.
keywords	social media; art; cultural planning; urban planning
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	ecaade2020_432
id	ecaade2020_432
authors	Fragkia, Vasiliki and Worre Foged, Isak
year	2020
title	Methods for the Prediction and Specification of Functionally Graded Multi-Grain Responsive Timber Composites
doi	https://doi.org/10.52842/conf.ecaade.2020.2.585
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. 585-594
summary	The paper presents design-integrated methods for high-resolution specification and prediction of functionally graded wood-based thermal responsive composites, using machine learning. The objective is the development of new circular design workflow, employing robotic fabrication, in order to predict fabrication files linked to material performance and design requirements, focused on application for intrinsic responsive and adaptive architectural surfaces. Through an experimental case study, the paper explores how machine learning can form a predictive design framework where low-resolution data can solve material systems at high resolution. The experimental computational and prototyping studies show that the presented image-based machine learning method can be adopted and adapted across various stages and scales of architectural design and fabrication. This in turn allows for a design-per-requirement approach that optimizes material distribution and promotes material economy.
keywords	material specification; responsive timber composites; machine learning; robotic fabrication; building envelopes
series	eCAADe
email
last changed	2022/06/07 07:50

_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
doi	https://doi.org/https://doi.org/10.1007/978-981-15-6568-7_13
source	Architectural Intelligence Selected Papers from the 1st International Conference on Computational Design and Robotic Fabrication (CDRF 2025)
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_84
id	acadia20_84
authors	Kirova, Nikol; Markopoulou, Areti
year	2020
title	Pedestrian Flow: Monitoring and Prediction
doi	https://doi.org/10.52842/conf.acadia.2020.1.084
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. 84-93.
summary	The worldwide lockdowns during the first wave of the COVID-19 pandemic had an immense effect on the public space. The events brought up an opportunity to redesign mobility plans, streets, and sidewalks, making cities more resilient and adaptable. This paper builds on previous research of the authors that focused on the development of a graphene-based sensing material system applied to a smart pavement and utilized to obtain pedestrian spatiotemporal data. The necessary steps for gradual integration of the material system within the urban fabric are introduced as milestones toward predictive modeling and dynamic mobility reconfiguration. Based on the capacity of the smart pavement, the current research presents how data acquired through an agent-based pedestrian simulation is used to gain insight into mobility patterns. A range of maps representing pedestrian density, flow, and distancing are generated to visualize the simulated behavioral patterns. The methodology is used to identify areas with high density and, thus, high risk of transmitting airborne diseases. The insights gained are used to identify streets where additional space for pedestrians is needed to allow safe use of the public space. It is proposed that this is done by creating a dynamic mobility plan where temporal pedestrianization takes place at certain times of the day with minimal disruption of road traffic. Although this paper focuses mainly on the agent-based pedestrian simulation, the method can be used with real-time data acquired by the sensing material system for informed decision-making following otherwise-unpredictable pedestrian behavior. Finally, the simulated data is used within a predictive modeling framework to identify further steps for each agent; this is used as a proof-of-concept through which more insights can be gained with additional exploration.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ijac202018404
id	ijac202018404
authors	Paul Nicholas, Gabriella Rossi, Ella Williams, Michael Bennett and Tim Schork
year	2020
title	Integrating real-time multi-resolution scanning and machine learning for Conformal Robotic 3D Printing in Architecture
source	International Journal of Architectural Computing vol. 18 - no. 4, 371–384
summary	Robotic 3D printing applications are rapidly growing in architecture, where they enable the introduction of new materials and bespoke geometries. However, current approaches remain limited to printing on top of a flat build bed. This limits robotic 3D printing’s impact as a sustainable technology: opportunities to customize or enhance existing elements, or to utilize complex material behaviour are missed. This paper addresses the potentials of conformal 3D printing and presents a novel and robust workflow for printing onto unknown and arbitrarily shaped 3D substrates. The workflow combines dual-resolution Robotic Scanning, Neural Network prediction and printing of PETG plastic. This integrated approach offers the advantage of responding directly to unknown geometries through automated performance design customization. This paper firstly contextualizes the work within the current state of the art of conformal printing. We then describe our methodology and the design experiment we have used to test it. We lastly describe the key findings, potentials and limitations of the work, as well as the next steps in this research.
keywords	Conformal printing, robotic fabrication, 3D scanning, neural networks, industry 4.0
series	journal
email
last changed	2021/06/03 23:29

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