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	caadria2021_389
id	caadria2021_389
authors	del Campo, Matias
year	2021
title	Architecture,Language and AI - Language,Attentional Generative Adversarial Networks (AttnGAN) and Architecture Design
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 211-220
doi	https://doi.org/10.52842/conf.caadria.2021.1.211
summary	The motivation to explore Attentional Generative Adversarial Networks (AttnGAN) as a design technique in architecture can be found in the desire to interrogate an alternative design methodology that does not rely on images as starting point for architecture design, but language. Traditionally architecture design relies on visual language to initiate a design process, wither this be a napkin sketch or a quick doodle in a 3D modeling environment. AttnGAN explores the information space present in programmatic needs, expressed in written form, and transforms them into a visual output. The key results of this research are shown in this paper with a proof-of-concept project: the competition entry for the 24 Highschool in Shenzhen, China. This award-winning project demonstrated the ability of GraphCNN to serve as a successful design methodology for a complex architecture program. In the area of Neural Architecture, this technique allows to interrogate shape through language. An alternative design method that creates its own unique sensibility.
keywords	Artificial Intelligence; Machine Learning; Artificial Neural Networks; Semiotics; Design Methodology
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	acadia21_76
id	acadia21_76
authors	Smith, Rebecca
year	2021
title	Passive Listening and Evidence Collection
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 76-81.
doi	https://doi.org/10.52842/conf.acadia.2021.076
summary	In this paper, I present the commercial, urban-scale gunshot detection system ShotSpotter in contrast with a range of ecological sensing examples which monitor animal vocalizations. Gunshot detection sensors are used to alert law enforcement that a gunshot has occurred and to collect evidence. They are intertwined with processes of criminalization, in which the individual, rather than the collective, is targeted for punishment. Ecological sensors are used as a “passive” practice of information gathering which seeks to understand the health of a given ecosystem through monitoring population demographics, and to document the collective harms of anthropogenic change (Stowell and Sueur 2020). In both examples, the ability of sensing infrastructures to “join up and speed up” (Gabrys 2019, 1) is increasing with the use of machine learning to identify patterns and objects: a new form of expertise through which the differential agendas of these systems are implemented and made visible. I trace the differential agendas of these systems as they manifest through varied components: the spatial distribution of hardware in the existing urban environment and / or landscape; the software and other informational processes that organize and translate the data; the visualization of acoustical sensing data; the commercial factors surrounding the production of material components; and the apps, platforms, and other forms of media through which information is made available to different stakeholders. I take an interpretive and qualitative approach to the analysis of these systems as cultural artifacts (Winner 1980), to demonstrate how the political and social stakes of the technology are embedded throughout them.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2021_006
id	caadria2021_006
authors	Agirachman, Fauzan Alfi and Shinozaki, Michihiko
year	2021
title	VRDR - An Attempt to Evaluate BIM-based Design Studio Outcome Through Virtual Reality
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 223-232
doi	https://doi.org/10.52842/conf.caadria.2021.2.223
summary	During the COVID-19 pandemic situation, educational institutions were forced to conduct all academic activities in distance learning formats, including the architecture program. This act barred interaction between students and supervisors only through their computers screen. Therefore, in this study, we explored an opportunity to utilize virtual reality (VR) technology to help students understand and evaluate design outcomes from an architectural design studio course in a virtual environment setting. The design evaluation process is focused on building affordance and user accessibility aspect based on the design objectives that students must achieve. As a result, we developed a game-engine based VR system called VRDR for evaluating design studio outcomes modeled as Building Information Modeling (BIM) models.
keywords	virtual reality; building information modeling; building affordance; user accessibility; architectural education
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	ascaad2021_021
id	ascaad2021_021
authors	Albassel, Mohamed; Mustafa Waly
year	2021
title	Applying Machine Learning to Enhance the Implementation of Egyptian Fire and Life Safety Code in Mega Projects
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. 7-22
summary	Machine Learning has become a significant research area in architecture; it can be used to retrieve valuable information for available data used to predict future instances. the purpose of this research was to develop an automated workflow to enhance the implementation of The Egyptian fire & life safety (FLS) code in mega projects and reduce the time wasted on the traditional process of rooms’ uses, occupant load, and egress capacity calculations to increase productivity by applying Supervised Machine Learning based on classification techniques through data mining and building datasets from previous projects, and explore the methods of preparation and analyzing data (text cleanup- tokenization- filtering- stemming-labeling). Then, provide an algorithm for classification rules using C# and python in integration with BIM tools such as Revit-Dynamo to calculate cumulative occupant load based on factors which are mentioned in the Egyptian FLS code, determine classification and uses of rooms to validate all data related to FLS. Moreover, calculating the egress capacity of means of egress for not only exit doors but also exit stairs. In addition, the research is to identify a clear understanding about ML and BIM through project case studies and how to build a model with the needed accuracy.
series	ASCAAD
email
last changed	2021/08/09 13:11

_id	acadia21_238
id	acadia21_238
authors	Anifowose, Hassan; Yan, Wei; Dixit, Manish
year	2021
title	BIM LOD + Virtual Reality
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 238-245.
doi	https://doi.org/10.52842/conf.acadia.2021.238
summary	Architectural Education faces limitations due to its tactile approach to learning in classrooms with only 2-D and 3-D tools. At a higher level, virtual reality provides a potential for delivering more information to individuals undergoing design learning. This paper investigates a hypothesis establishing grounds towards a new research in Building Information Modeling (BIM) and Virtual Reality (VR). The hypothesis is projected to determine best practices for content creation and tactile object virtual interaction, which potentially can improve learning in architectural & construction education with a less costly approach and ease of access to well-known buildings. We explored this hypothesis in a step-by-step game design demonstration in VR, by showcasing the exploration of the Farnsworth House and reproducing assemblage of the same with different game levels of difficulty which correspond with varying BIM levels of development (LODs). The game design prototype equally provides an entry way and learning style for users with or without a formal architectural or construction education seeking to understand design tectonics within diverse or cross-disciplinary study cases. This paper shows that developing geometric abstract concepts of design pedagogy, using varying LODs for game content and levels, while utilizing newly developed features such as snap-to-grid, snap-to-position and snap-to-angle to improve user engagement during assemblage may provide deeper learning objectives for architectural precedent study.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2021_203
id	ecaade2021_203
authors	Arora, Hardik, Bielski, Jessica, Eisenstadt, Viktor, Langenhan, Christoph, Ziegler, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2021
title	Consistency Checker - An automatic constraint-based evaluator for housing spatial configurations
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 2, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 351-358
doi	https://doi.org/10.52842/conf.ecaade.2021.2.351
summary	The gradual rise of artificial intelligence (AI) and its increasing visibility among many research disciplines affected Computer-Aided Architectural Design (CAAD). Architectural deep learning (DL) approaches are being developed and published on a regular basis, such as retrieval (Sharma et al. 2017) or design style manipulation (Newton 2019; Silvestre et al. 2016). However, there seems to be no method to evaluate highly constrained spatial configurations for specific architectural domains (such as housing or office buildings) based on basic architectural principles and everyday practices. This paper introduces an automatic constraint-based consistency checker to evaluate the coherency of semantic spatial configurations of housing construction using a small set of design principles to evaluate our DL approaches. The consistency checker informs about the overall performance of a spatial configuration followed by whether it is open/closed and the constraints it didn't satisfy. This paper deals with the relation of spaces processed as mathematically formalized graphs contrary to existing model checking software like Solibri.
keywords	model checking, building information modeling, deep learning, data quality
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ascaad2021_058
id	ascaad2021_058
authors	ElGewely, Maha; Wafaa Nadim, Mostafa Talaat, Ahmad El Kassed,Mohamed Yehia, Slim Abdennadher
year	2021
title	Immersive VR Environment for Construction Detailing Education: BIM Approach
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. 114-128
summary	According to literature in education, adults learn best when learning is active, self-directed, problem-based, and relevant to their needs. In Building Construction Education, construction site visits provide students with real-life practical experience which are considered an extension for classroom. Nevertheless, it is challenging to integrate construction site visits regularly during the academic semester with respect to the class specific needs. Virtual Reality as an interactive immersive technology may facilitate virtual construction site that meets the learning needs where students can explore and build in a real scale environment. The proposed VR environment is an HMD VR platform for construction detailing that provides experiential learning in a zero-risk environment. It builds on integrating VR technology as a medium and Building Information Modeling (BIM) as a repository of information. This work discusses the proposed environment curricular unit prototype design, implementation, and validation. System usability and immersion are assessed both qualitatively and quantitatively. After considering the feedback, The VR environment prototype is then validated on the level of learning outcomes, providing the evidence that it would enhance students’ engagement, motivation and achievement accordingly.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	caadria2023_446
id	caadria2023_446
authors	Guida, George
year	2023
title	Multimodal Architecture: Applications of Language in a Machine Learning Aided Design Process
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 561–570
doi	https://doi.org/10.52842/conf.caadria.2023.2.561
summary	Recent advances in Natural Language Processing (NLP) and Diffusion Models (DMs) are leading to a significant change in the way architecture is conceived. With capabilities that surpass those of current generative models, it is now possible to produce an unlimited number of high-quality images (Dhariwal and Nichol 2021). This opens up new opportunities for using synthetic images and marks a new phase in the creation of multimodal 3D forms, central to architectural concept design stages. Presented here are three methodologies of generation of meaningful 2D and 3D designs, merging text-to-image diffusion models Stable Diffusion, and DALL-E 2 with computational methods. These allow designers to intuitively navigate through a multimodal feedback loop of information originating from language and aided by artificial intelligence tools. This paper contributes to our understanding of machine-augmented design processes and the importance of intuitive user interfaces (UI) in enabling new dialogues between humans and machines. Through the creation of a prototype of an accessible UI, this exchange of information can empower designers, build trust in these tools, and increase control over the design process.
keywords	Machine Learning, Diffusion Models, Concept Design, Semantics, User Interface, Design Agency
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	caadria2021_043
id	caadria2021_043
authors	Ng, Provides
year	2021
title	21E8: Coupling Generative Adversarial Neural Networks (GANS) with Blockchain Applications in Building Information Modelling (BIM) Systems
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 111-120
doi	https://doi.org/10.52842/conf.caadria.2021.2.111
summary	The ability of GANs to synthesize large sets of data is ideal for coupling with BIM to formulate a multi-access system that enables users to search and browse through a spectrum of articulated options, all personalised to design specificity - an 'Architecture Machine'. Nonetheless, due to challenges in proprietary incompatibility, BIM systems currently lack a secured yet transparent way of freely integrating with crowdsourced efforts. This research proposes to employ blockchain as a means to couple GANs and BIM, with e8 networking topology to facilitate communication and distribution. It consists of a literature review and a design research that proposes a tech stack design and UML (unified modeling language) use cases, and presents preliminary design results obtained using GANs and e8.
keywords	21e8; GANs; Blockchain; BIM; Architecture Machine
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	ecaade2021_290
id	ecaade2021_290
authors	Nicholas, Paul, Chen, Yu, Borpujari, Nihit, Bartov, Nitsan and Refsgaard, Andreas
year	2021
title	A Chained Machine Learning Approach to Motivate Retro-Cladding of Residential Buildings
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 55-64
doi	https://doi.org/10.52842/conf.ecaade.2021.1.055
summary	This paper investigates how a novel approach to visualisation could help address the challenge of motivating residential retrofitting. Emerging retrofitting research and practice emphasises retro-cladding - the upgrading of the exterior facade of a building - using a modular approach. We present a machine-learning based approach aimed to motivate residential retrofitting through the generation of images and cost/benefit information describing climatically specific additions of external insulation and green roof panels to the façade of a Danish type house. Our approach chains a series of different models together, and implements a method for the controlled navigation of the principle generative styleGAN model. The approach is at a prototypical stage that implements a full workflow but does not include numerical evaluation of model predictions. Our paper details our processes and considerations for the generation of new datasets, the specification and chaining of models, and the linking of climatic data to travel through the latent space of a styleGAN model to visualise and provide a simple cost benefit report for retro-cladding specific to the local climates of five different Danish cities.
keywords	Retrofitting; Machine Learning; Generative Adversarial Networks; Synthetic Datasets
series	eCAADe
type	normal paper
email
last changed	2022/06/07 07:58

_id	ecaade2021_022
id	ecaade2021_022
authors	Ozkan, Dilan, Dade-Robertson, Martyn, Morrow, Ruth and Zhang, Meng
year	2021
title	Designing a Living Material Through Bio-Digital-Fabrication - Guiding the growth of fungi through a robotic system
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 77-84
doi	https://doi.org/10.52842/conf.ecaade.2021.1.077
summary	Designing with living materials require designers to look for new methods of fabrication since living cells exhibit their own agency, and are able to sense and respond to environmental stimuli. Therefore, there is an urgent demand to design a framework for fabricating living materials. This paper investigates the digital-fabrication of fungi as a new way of designing and crafting living materials without genetic manipulation. In this research, fungi act as a bio-material probe to generate and test new design strategies that enable a dialogue between digital and biological systems. Conceptual experiments, that use fungi to investigate the proposed bio-digital-fabrication scenarios, are central in this study. The research attempts to generate new information for the design process of an organism in the field of architecture. The project will expand on the latest thinking on the bio-material fabrication by allowing the living material to be engaged in the fabrication process.
keywords	Bio-digital-fabrication; Biological interactions; Self-organizing material systems; Robotic growth chamber
series	eCAADe
email
last changed	2022/06/07 08:00

_id	caadria2021_053
id	caadria2021_053
authors	Rhee, Jinmo and Veloso, Pedro
year	2021
title	Generative Design of Urban Fabrics Using Deep Learning
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 31-40
doi	https://doi.org/10.52842/conf.caadria.2021.1.031
summary	This paper describes the Urban Structure Synthesizer (USS), a research prototype based on deep learning that generates diagrams of morphologically consistent urban fabrics from context-rich urban datasets. This work is part of a larger research on computational analysis of the relationship between urban context and morphology. USS relies on a data collection method that extracts GIS data and converts it to diagrams with context information (Rhee et al., 2019). The resulting dataset with context-rich diagrams is used to train a Wasserstein GAN (WGAN) model, which learns how to synthesize novel urban fabric diagrams with the morphological and contextual qualities present in the dataset. The model is also trained with a random vector in the input, which is later used to enable parametric control and variation for the urban fabric diagram. Finally, the resulting diagrams are translated to 3D geometric entities using computer vision techniques and geometric modeling. The diagrams generated by USS suggest that a learning-based method can be an alternative to methods that rely on experts to build rule sets or parametric models to grasp the morphological qualities of the urban fabric.
keywords	Deep Learning; Urban Fabric; Generative Design; Artificial Intelligence; Urban Morphology
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	sigradi2021_31
id	sigradi2021_31
authors	Sampaio, Hugo Guimaraes, Lima, Mariana Monteiro Xavier de and Cardoso, Daniel Ribeiro
year	2021
title	Parametric Modeling as Record of Memory of Vernacular Boats
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. 657–668
summary	This work seeks to continue the research started in 2017 about the triangle boat type, based on the traditional models of the municipality of Icapuí in Ceará / Brazil. The intention is to expand and refine the research, analyzing the construction process and the typology of the boats. For this, it is proposed to define a typology and a shape grammar of the boats and implement, in a digitally-based system, a formalized language. The methodology adopted in this work starts from a knowledge construction process following the Design Science Research strategy. The main result is an in-depth analysis of the description and representation of a vernacular design of the triangle boats. The documentation approach using parametric modeling offers great contributions to the memory and cultural heritage preservation, since an algorithmic description is able to retain information pertaining to both the material and immaterial part of the artifact.
keywords	Modelagem paramétrica. Patrimônio digital. Patrimônio cultural. Design vernacular. Embarcaçao
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	acadia23_v3_71
id	acadia23_v3_71
authors	Vassigh, Shahin; Bogosian, Biayna
year	2023
title	Envisioning an Open Knowledge Network (OKN) for AEC Roboticists
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 3: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-1-0]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 24-32.
summary	The construction industry faces numerous challenges related to productivity, sustainability, and meeting global demands (Hatoum and Nassereddine 2020; Carra et al. 2018; Barbosa, Woetzel, and Mischke 2017; Bock 2015; Linner 2013). In response, the automation of design and construction has emerged as a promising solution. In the past three decades, researchers and innovators in the Architecture, Engineering, and Construction (AEC) fields have made significant strides in automating various aspects of building construction, utilizing computational design and robotic fabrication processes (Dubor et al. 2019). However, synthesizing innovation in automation encounters several obstacles. First, there is a lack of an established venue for information sharing, making it difficult to build upon the knowledge of peers. First, the absence of a well-established platform for information sharing hinders the ability to effectively capitalize on the knowledge of peers. Consequently, much of the research remains isolated, impeding the rapid dissemination of knowledge within the field (Mahbub 2015). Second, the absence of a standardized and unified process for automating design and construction leads to the individual development of standards, workflows, and terminologies. This lack of standardization presents a significant obstacle to research and learning within the field. Lastly, insufficient training materials hinder the acquisition of skills necessary to effectively utilize automation. Traditional in-person robotics training is resource-intensive, expensive, and designed for specific platforms (Peterson et al. 2021; Thomas 2013).
series	ACADIA
type	field note
email
last changed	2024/04/17 13:59

_id	caadria2021_446
id	caadria2021_446
authors	Zhou, Yifan and Park, Hyoung-June
year	2021
title	Sketch with Artificial Intelligence (AI) - A Multimodal AI Approach for Conceptual Design
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 201-210
doi	https://doi.org/10.52842/conf.caadria.2021.1.201
summary	The goal of the research is to investigate an AI approach to assist architects with multimodal inputs (sketches and textual information) for conceptual design. With different textual inputs, the AI approach generates the architectural stylistic variations of a users initial sketch input as a design inspiration. A novel machine learning approach for the multimodal input system is introduced and compared to other approaches. The machine learning approach is performed through procedural training with the content curation of training data in order to control the fidelity of generated designs from the input and to manage their diversity. In this paper, the framework of the proposed AI approach is explained. Furthermore, the implementation of its prototype is demonstrated with various examples.
keywords	Artificial Intelligence; Stylistic Variations; Multimodal Input; Content Curation; Procedural Training
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	ecaade2021_167
id	ecaade2021_167
authors	Zhu, Zhelun, Coraglia, Ugo Maria, Simeone, Davide and Fioravanti, Antonio
year	2021
title	Spaces Identity Evaluation aNd Assignment - SIENA - A duck typing approach for automatic recognition and semantic enrichment of architectural spaces
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 2, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 341-350
doi	https://doi.org/10.52842/conf.ecaade.2021.2.341
summary	This paper presents the development of SIENA - Space Identity Evaluation aNd Assignment - based on duck typing for automatic recognition and semantic enrichment of the architectural spaces. This method is known in computer science as a form of abductive reasoning and leverages on the observable features of an object in order to establish its recognition. As result, the spatial identity is object-oriented and can be dynamically defined. In this research, the duck typing approach has been achieved with the support of BIM methodology and graph database. The former allows information-based modeling of an architectural project while the latter makes possible the representation of the knowledge along with their relationships. Consequently, this research may have many possible applications, especially as a valid design support tool in the very first design stages. Furthermore, an efficient spatial identity detection could contribute to the development of further human-machine interactions and therefore a possible optimization of the design process.
keywords	Semantics; Graph database; Duck typing; Space identification
series	eCAADe
email
last changed	2022/06/07 07:57

_id	acadia21_160
id	acadia21_160
authors	Cao, Shicong; Zheng, Hao
year	2021
title	A POI-Based Machine Learning Method in Predicting Health
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 160-169.
doi	https://doi.org/10.52842/conf.acadia.2021.160
summary	This research aims to explore the quantitative relationship between urban planning decisions and the health status of residents. By modeling the Point of Interest (POI) data and the geographic distribution of health-related outcomes, the research explores the critical factors in urban planning that could influence the health status of residents. It also informs decision-making regarding a healthier built environment and opens up possibilities for other data-driven methods. The data source constitutes two data sets, the POI data from OpenStreetMap, and the PLACES: Local Data for Better Health dataset from CDC. After the data is collected and joined spatially, a machine learning method is used to select the most critical urban features in predicting the health outcomes of residents. Several machine learning models are trained and compared. With the chosen model, the prediction is evaluated on the test dataset and mapped geographically. The relations between factors are explored and interpreted. Finally, to understand the implications for urban design, the impact of modified POI data on the prediction of residents' health status is calculated and compared. This research proves the possibility of predicting resident's health from urban conditions with machine learning methods. The result verifies existing healthy urban design theories from a different perspective. This approach shows vast potential that data could in future assist decision-making to achieve a healthier built environment.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ijac202119313
id	ijac202119313
authors	Saldana Ochoa, Karla; Ohlbrock, Patrick Ole; D’Acunto, Pierluigi; Moosavi, Vahid
year	2021
title	Beyond typologies, beyond optimization: Exploring novel structural forms at the interface of human and machine intelligence
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 3, 466–490
summary	This article presents a computer-aided design framework for the generation of non-standard structural forms in static equilibrium that takes advantage of the interaction between human and machine intelligence. The design framework relies on the implementation of a series of operations (generation, clustering, evaluation, selection, and regeneration) that allow to create multiple design options and to navigate in the design space according to objective and subjective criteria defined by the human designer. Through the interaction between human and machine intelligence, the machine can learn the nonlinear correlation between the design inputs and the design outputs preferred by the human designer and generate new options by itself. In addition, the machine can provide insights into the structural performance of the generated structural forms. Within the proposed framework, three main algorithms are used: Combinatorial Equilibrium Modeling for generating of structural forms in static equilibrium as design options, Self-Organizing Map for clustering the generated design options, and Gradient-Boosted Trees for classifying the design options. These algorithms are combined with the ability of human designers to evaluate non-quantifiable aspects of the design. To test the proposed framework in a real-world design scenario, the design of a stadium roof is presented as a case study.
keywords	Structural design, machine learning, topology, graphic statics, form-finding, Combinatorial Equilibrium Modeling, Self-Organizing Map, Gradient-Boosted Trees
series	journal
email
last changed	2024/04/17 14:29

_id	ecaade2021_186
id	ecaade2021_186
authors	Sebestyen, Adam, Rock, Johanna and Hirschberg, Urs Leonhard
year	2021
title	Towards Abductive Reasoning-Based Computational Design Tools - Using Machine Learning as a way to explore the combined design spaces of multiple parametric models
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 141-150
doi	https://doi.org/10.52842/conf.ecaade.2021.1.141
summary	Abductive Reasoning - reasoning based on experience - is important for design. This research tries to lay the groundwork for using Variational Autoencoders (VAE) - currently one of the most established deep learning architectures for generative modelling, a subfield of Machine Learning (ML) - as a way to support abductive reasoning in early design stages. While our research is still in its early stages, the first results look promising. In this paper we explain the current state of our research, its premises and methods, and discuss the results achieved thus far. We also explain the motivation behind our work and the potential we see in using VAE in this way and why we believe our approach could represent a paradigmatic shift in the way parametric models can be used in design.
keywords	Machine Learning; Parametric Design; Variational Autoencoders; Generative Modeling; Abductive Reasoning
series	eCAADe
email
last changed	2022/06/07 08:00

_id	cdrf2021_139
id	cdrf2021_139
authors	Shicong Cao1 and Hao Zheng
year	2021
title	A POI-Based Machine Learning Method for Predicting Residents’ Health Status
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_13
summary	Health environment is a key factor in public health. Since people’s health depends largely on their lifestyle, the built environment which supports a healthy living style is becoming more important. With the right urban planning decisions, it’s possible to encourage healthier living and save healthcare expenditures for the society. However, there is not yet a quantitative relationship established between urban planning decisions and the health status of the residents. With the abundance of data and computing resources, this research aims to explore this relationship with a machine learning method. The data source is from both the OpenStreetMap and American Center for Decease Control and Prevention (CDC). By modeling the Point of Interest data and the geographic distribution of health-related outcome, the research explores the key factors in urban planning that could influence the health status of the residents quantitatively. It informs how to create a built environment that supports health and opens up possibilities for other data-driven methods in this field.
series	cdrf
email
last changed	2022/09/29 07:53

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