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	ecaade2020_156
id	ecaade2020_156
authors	Hemmerling, Marco and Maris, Simon
year	2020
title	INTERCOM - A platform for collaborative design processes
doi	https://doi.org/10.52842/conf.ecaade.2020.2.173
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. 173-180
summary	The INTERCOM project propounds a cloud-based collaboration platform for digital planning processes in architecture. The concept is based on an openBIM approach and ensures open access for all partners involved. At its core it provides IFC-based and model-related online tools for planning, communication and collaboration. The interaction with the model and the exchange with other project partners takes place in real-time via a model-related chat and BCF exports. In addition, the integration of e-learning modules (e.g. video tutorials, wikis, project documents) encourages problem solving through further education. Especially the integration of communication and collaboration tools is supposed to enhance the decision making throughout the design process and become a key factor for a successful and coordinated BIM process. Primarily INTERCOM has been developed as a prototype for teaching BIM in interdisciplinary teams. Subsequently, the application can also be adopted for professional practice. The paper evaluates previous experiences from BIM cloud teaching and discusses the conception and development of the proposed collaborative platform.
keywords	architecture curriculum; didactics; building information modeling (BIM); collaborative design process; common data environment (CDE)
series	eCAADe
email
last changed	2022/06/07 07:49

_id	ijac202018203
id	ijac202018203
authors	Beattie , Hamish; Daniel Brown and Sara Kindon
year	2020
title	Solidarity through difference: Speculative participatory serious urban gaming (SPS-UG)
source	International Journal of Architectural Computing vol. 18 - no. 2, 141-154
summary	This article discusses the methodology and results of the Maslow’s Palace workshops project, which engages with current debates surrounding the democratisation of digital urban design technology and stakeholder decision making, through the implementation of a speculative oriented approach to serious gaming. The research explores how serious games might be used to help marginalised communities consider past, future and present community experiences, reconcile dissimilar assumptions, generate social capital building and design responses and prime participants for further long-term design engagement processes through a new approach called Speculative Participatory Serious Urban Gaming. Empirical material for this research was gathered from a range of case study workshops prepared with three landfill-based communities and external partners throughout 2017. Results show the approach helped participants develop shared norms, values and collective understandings of sensitive topics and develop ideas for future action through ‘collective tinkering.
keywords	Participatory design, urban design, social capital, serious games
series	journal
email
last changed	2020/11/02 13:34

_id	sigradi2020_464
id	sigradi2020_464
authors	Builes Vélez, Ana Elena; Celani, Pierfrancesco
year	2020
title	Application of the Sustainable Urban Environments model based on the Smart Outdoor approach in the city of Medellín
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. 464-469
summary	The quality of an urban space significantly influences the habitability of a city. In an era where buildings are becoming more and more "intelligent", outdoor space needs to evolve to make it more welcoming and to allow it to be shared and appropriate, capable of expanding opportunities and functionality for the inhabitant who lives in it. In this context the COGITO project, is exploring ways to extend the cognitive logic typical of intelligent buildings to the urban space. We propose to appropriate the model developed in COGITO and apply it in a case study of the city of Medellin.
keywords	Smart Cities, Urban Space, Sustainability, Smart Outdoor
series	SIGraDi
email
last changed	2021/07/16 11:49

_id	caadria2020_071
id	caadria2020_071
authors	Carroll, Stan
year	2020
title	Managing Risk in a Research-Based Practice as Projects Scale To Construction:A Case Study
doi	https://doi.org/10.52842/conf.caadria.2020.1.065
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. 65-74
summary	Research-based architectural practices often experiment along the bleeding edge of the new frontier of design and include developing methodologies unfamiliar to the construction industry. Successfully implementing the resulting research methodologies to an architectural scale requires careful consideration of risk management within a Design-Bid-Build construction project. How a firm manages the risk when scaling a research conclusion to an architectural scale is an essential aspect of assuring the success of the project. These considerations are particularly acute within firms whose research involves convoluted geometry. In the field of doubly-curved geometric material systems, the level of precision required to manage professional risk is commensurate with the level of geometric complexity. Adopting the mindset of a Medieval master mason's process within the context of twenty-first-century materials and processes can be a method toward a successful project. By performing well thought-out transfer procedures of digital data, resolving the fundamental challenges of fabrication, and including structural analysis as a part of the early design phases, experimental architectural expressions can be realized without extra financial risk to the designer.
keywords	Risk Management; Research-Based Practice; Complex Geometry; Digital Fabrication; Computational Design
series	CAADRIA
email
last changed	2022/06/07 07:55

_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
doi	https://doi.org/10.52842/conf.caadria.2020.2.283
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
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_334
id	sigradi2020_334
authors	Correa, Natália de Andrade; Alves, Gilfranco Medeiros
year	2020
title	From Parametric Design to Contour Crafting Technics: A Lab for Algo+Ritmo, a Brazilian Research Group
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. 334-342
summary	This article presents a discussion on digital design processes. More precisely about the use of Contour Crafting (CC) as a material and technique solution for the construction which will carry less impact for the environment. It explores the connection between parametric process and the file-to-factory concept analyzing the consequences of those strategies. The paper describes and analyzes a case study starting from the demand for a headquarters project for a university research group. It presents possibilities and discusses futures developments based on the methodology used in the process.
keywords	Digital Fabrication, Design Process, File-to-factory, Contour Crafting, Algorithm
series	SIGraDi
email
last changed	2021/07/16 11:49

_id	caadria2020_426
id	caadria2020_426
authors	Goepel, Garvin and Crolla, Kristof
year	2020
title	Augmented Reality-based Collaboration - ARgan, a bamboo art installation case study
doi	https://doi.org/10.52842/conf.caadria.2020.2.313
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. 313-322
summary	ARgan is a geometrically complex bamboo sculpture that relied on Mixed Reality (MR) for its joint creation by multiple sculptors and used latest Augmented Reality (AR) technology to guide manual fabrication actions. It was built at the Chinese University of Hong Kong in the fall of 2019 by thirty participants of a design-and-build workshop on the integration of AR in construction. As part of its construction workflow, holographic setups were created on multiple devices, including a series of Microsoft HoloLenses and several handheld Smartphones, all linked simultaneously to a single digital base model to interactively guide the manufacturing process. This paper critically evaluates the experience of extending recent AR and MR tool developments towards applications that centre on creative collaborative production. Using ARgan as a demonstrator project, its developed workflow is assessed on its ability to transform a geometrically complex digitally drafted design to its final physically built form, highlighting the necessary strategic integration of variability as an opportunity to relax notions on design precision and exact control. The paper concludes with a plea for digital technology's ability to stimulate dialogue and collaboration in creative production and augment craftsmanship, thus providing greater agency and more diverse design output.
keywords	Augmented-Reality; Mixed-Reality; Post-digital; High-tech vs low-tech; Bamboo
series	CAADRIA
email
last changed	2022/06/07 07:51

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

_id	caadria2020_106
id	caadria2020_106
authors	Tian, Jieren and Yu, Chuanfei
year	2020
title	Dynamic Translation of Real-world Environment Factors and Urban Design Operation in a Game Engine - A Case Study of Central District in Tiebei New Town, Nanjing
doi	https://doi.org/10.52842/conf.caadria.2020.2.011
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. 11-20
summary	The building and its urban environment are complex and dynamic data systems. Designers, who make design decisions, need the design tools to simulate the built environment, to estimate the feasibility of the design. However, the static modeling software, widely used nowadays, restricts the linkage relationship between the actual data environment and the simulation model, which lacks the dynamic constraint relationship and the construction of the loop order. Different from traditional modeling and analysis tools, simulation games, with dynamic constraint rules and real-time feedback operations, provide a new way of thinking and a perspective to observe the urban, which makes the simulation game be seen as a simplified analog system, to some extent. Therefore, this paper plan to builds a city model, based on an urban design project of an urban district of Nanjing as an example, by using the Cities: Skylines, a city simulation game with priority of traffic and zoning concept. Based on this dynamic model, the next step will evaluate the original project and carry out further optimization operations in real-time.
keywords	real-time interaction; dynamic process simulation; urban environment; city simulation system; simulated game
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	acadia20_228
id	acadia20_228
authors	Alawadhi, Mohammad; Yan, Wei
year	2020
title	BIM Hyperreality
doi	https://doi.org/10.52842/conf.acadia.2020.1.228
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. 228-236.
summary	Deep learning is expected to offer new opportunities and a new paradigm for the field of architecture. One such opportunity is teaching neural networks to visually understand architectural elements from the built environment. However, the availability of large training datasets is one of the biggest limitations of neural networks. Also, the vast majority of training data for visual recognition tasks is annotated by humans. In order to resolve this bottleneck, we present a concept of a hybrid system—using both building information modeling (BIM) and hyperrealistic (photorealistic) rendering—to synthesize datasets for training a neural network for building object recognition in photos. For generating our training dataset, BIMrAI, we used an existing BIM model and a corresponding photorealistically rendered model of the same building. We created methods for using renderings to train a deep learning model, trained a generative adversarial network (GAN) model using these methods, and tested the output model on real-world photos. For the specific case study presented in this paper, our results show that a neural network trained with synthetic data (i.e., photorealistic renderings and BIM-based semantic labels) can be used to identify building objects from photos without using photos in the training data. Future work can enhance the presented methods using available BIM models and renderings for more generalized mapping and description of photographed built environments.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_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	caadria2020_444
id	caadria2020_444
authors	Higgs, Baptiste and Doherty, Ben
year	2020
title	Sanitary Sanity: Evaluating Privacy Preserving Machine Learning Methods for Post-occupancy Evaluation
doi	https://doi.org/10.52842/conf.caadria.2020.2.697
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. 697-706
summary	Traditional post-occupancy evaluation (POE) of building performance has typically privileged physical building attributes over human behavioural data. This is due to a lack of capability and is especially the case for private spaces such as Sanitary Facilities (SFs). A privacy-preserving sensor-based system using Machine Learning (ML) was previously developed, however it was limited to basic body position classification. Yet, SF usage behaviour can be significantly more complex. This research accordingly builds on the aforementioned work to expand behavioural classifications using a sensor-based ML system. Specifically, the case study uses a GridEYE thermal sensor array, which is trained on a cubicle location within a workplace SF. A variety of ML algorithms are then evaluated on their behaviour-classifying ability. A detailed analysis of behaviour-classification performance is then provided. A system with greater fidelity is thus demonstrated, albeit hampered by imprecise behaviour definitions. Regardless, this contributes to the capability of the broader field of research that is investigating Evidence Based Design (EBD) by extending the ability to examine human behaviour, especially in private spaces. This further contributes to the growing body of work surrounding SF provision.
keywords	EBD; Data; Internet of Things; Machine Learning; Post Occupancy Evaluation
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	acadia20_382
id	acadia20_382
authors	Hosmer, Tyson; Tigas, Panagiotis; Reeves, David; He, Ziming
year	2020
title	Spatial Assembly with Self-Play Reinforcement Learning
doi	https://doi.org/10.52842/conf.acadia.2020.1.382
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. 382-393.
summary	We present a framework to generate intelligent spatial assemblies from sets of digitally encoded spatial parts designed by the architect with embedded principles of prefabrication, assembly awareness, and reconfigurability. The methodology includes a bespoke constraint-solving algorithm for autonomously assembling 3D geometries into larger spatial compositions for the built environment. A series of graph-based analysis methods are applied to each assembly to extract performance metrics related to architectural space-making goals, including structural stability, material density, spatial segmentation, connectivity, and spatial distribution. Together with the constraint-based assembly algorithm and analysis methods, we have integrated a novel application of deep reinforcement (RL) learning for training the models to improve at matching the multiperformance goals established by the user through self-play. RL is applied to improve the selection and sequencing of parts while considering local and global objectives. The user’s design intent is embedded through the design of partial units of 3D space with embedded fabrication principles and their relational constraints over how they connect to each other and the quantifiable goals to drive the distribution of effective features. The methodology has been developed over three years through three case study projects called ArchiGo (2017–2018), NoMAS (2018–2019), and IRSILA (2019-2020). Each demonstrates the potential for buildings with reconfigurable and adaptive life cycles.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_161
id	caadria2020_161
authors	Kido, Daiki, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2020
title	Mobile Mixed Reality for Environmental Design Using Real-Time Semantic Segmentation and Video Communication - Dynamic Occlusion Handling and Green View Index Estimation
doi	https://doi.org/10.52842/conf.caadria.2020.1.681
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. 681-690
summary	Mixed reality (MR), that blends the real and virtual worlds, attracted attention for consensus-building among stakeholders in environmental design with the visualization of planned landscape onsite. One of the technical challenges in MR is the occlusion problem which occurs when virtual objects hide physical objects that should be rendered in front of virtual objects. This problem may cause inappropriate simulation. And the visual environmental assessment of present and proposed landscape with MR can be effective for the evidence-based design, such as urban greenery. Thus, this study aims to develop a MR-based environmental assessment system with dynamic occlusion handling and green view index estimation using semantic segmentation based on deep learning. This system was designed for the use on a mobile device with video communication over the Internet to implement a real-time semantic segmentation whose computational cost is high. The applicability of the developed system is shown through case studies.
keywords	Mixed Reality (MR); Environmental Design; Dynamic Occlusion Handling; Semantic Segmentation; Green View Index
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	acadia20_178
id	acadia20_178
authors	Meeran, Ahmed; Conrad Joyce, Sam
year	2020
title	Machine Learning for Comparative Urban Planning at Scale: An Aviation Case Study
doi	https://doi.org/10.52842/conf.acadia.2020.1.178
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. 178-187.
summary	Aviation is in flux, experiencing 5.4% yearly growth over the last two decades. However, with COVID-19 aviation was hard hit. This, along with its contribution to global warming, has led to louder calls to limit its use. This situation emphasizes how urban planners and technologists could contribute to understanding and responding to this change. This paper explores a novel workflow of performing image-based machine learning (ML) on satellite images of over 1,000 world airports that were algorithmically collated using European Space Agency Sentinel2 API. From these, the top 350 United States airports were analyzed with land use parameters extracted around the airport using computer vision, which were mapped against their passenger footfall numbers. The results demonstrate a scalable approach to identify how easy and beneficial it would be for certain airports to expand or contract and how this would impact the surrounding urban environment in terms of pollution and congestion. The generic nature of this workflow makes it possible to potentially extend this method to any large infrastructure and compare and analyze specific features across a large number of images while being able to understand the same feature through time. This is critical in answering key typology-based urban design challenges at a higher level and without needing to perform on-ground studies, which could be expensive and time-consuming.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_172
id	caadria2020_172
authors	Xia, Xinyu and Tong, Ziyu
year	2020
title	A Machine Learning-Based Method for Predicting Urban Land Use
doi	https://doi.org/10.52842/conf.caadria.2020.2.021
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. 21-30
summary	Land use is one of the most basic elements of urban management. In urban planning and design, land use is often determined by experience and case studies. However, the development of urbanization has led to a combinatory trend for land use, and the land use of a plot is always impacted by the surrounding environment. In such a complex situation, it is difficult to find hidden relationships among types of land use by humans alone. Within artificial intelligence, machine learning can help find correlations among data. This paper presents a new method for learning the rules relating the known land use data and predicting the land use of a target plot by constructing an artificial neural network. We take Nanjing as a specific case and study the logic of its land use. The results not only demonstrate associations between the surroundings and the target but also show the feasibility of a combinatory land use index in urban planning and design.
keywords	Land use; Urban planning and design; Machine learning; Artificial neural network
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	cdrf2019_134
id	cdrf2019_134
authors	Zhen Han, Wei Yan, and Gang Liu
year	2020
title	A Performance-Based Urban Block Generative Design Using Deep Reinforcement Learning and Computer Vision
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_13
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	In recent years, generative design methods are widely used to guide urban or architectural design. Some performance-based generative design methods also combine simulation and optimization algorithms to obtain optimal solutions. In this paper, a performance-based automatic generative design method was proposed to incorporate deep reinforcement learning (DRL) and computer vision for urban planning through a case study to generate an urban block based on its direct sunlight hours, solar heat gains as well as the aesthetics of the layout. The method was tested on the redesign of an old industrial district located in Shenyang, Liaoning Province, China. A DRL agent - deep deterministic policy gradient (DDPG) agent - was trained to guide the generation of the schemes. The agent arranges one building in the site at one time in a training episode according to the observation. Rhino/Grasshopper and a computer vision algorithm, Hough Transform, were used to evaluate the performance and aesthetics, respectively. After about 150 h of training, the proposed method generated 2179 satisfactory design solutions. Episode 1936 which had the highest reward has been chosen as the final solution after manual adjustment. The test results have proven that the method is a potentially effective way for assisting urban design.
series	cdrf
email
last changed	2022/09/29 07:51

_id	acadia20_226p
id	acadia20_226p
authors	Borhani, Alireza; Kalantar, Negar
year	2020
title	Interlocking Shell
source	ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. edited by M. Yablonina, A. Marcus, S. Doyle, M. del Campo, V. Ago, B. Slocum. 226-231
summary	With a specific focus on robotic stereotomy, two full-scale vault structures were designed to explore the potential of self-standing building structures made from interlocking components; these structures were fabricated with a track-mounted industrial-scale robot (ABB 4600). To respond to the economic affordances of robotic subtractive cutting, all uniquely shaped structural modules came from one block of material (48"" x96"" x36""). Through the discretization of curvilinear tessellated vault surfaces into a limited number of uniquely shaped modules with embedded form-fitting connectors, the project exhibited the potential for programming a robot to cut ruled surfaces to produce freeform shells of any kind. Representing nearly zero-waste construction, the developed technology can potentially be used for self-supporting emergency shelters and field medical clinics, facilitating easy shipping and speedy assembly. Without using any scaffolding, a few people can erect and dismantle an entire mortar-free structure at the construction site. The disassembled structure occupies minimal space in storage, and the structure’s pieces can be transported to the site in stacks. Robot milling is a common technique for removing material to transform a block into a sculptural shape. Unlike milling techniques that produce significant waste, we used a hotwire that sliced through a Geofoam block to create almost no waste pieces. Since the front side of every module was concurrent with the backside of the next one, such a decision allowed to operate just one cut per front side of each module. In this case, by having three cuts, two neighboring modules were fabricated. The form of the structure and its modules emerged from the constraints of the fabrication technique, aiming to establish a feedback loop between geometry, material, simulation, and tool. By cross-referencing geometric data across Grasshopper, a customized tessellation script was made to breakdown a vault into its modular ruled surface constructs.
series	ACADIA
type	project
email
last changed	2021/10/26 08:08

_id	ecaade2024_4
id	ecaade2024_4
authors	Irodotou, Louiza; Gkatzogiannis, Stefanos; Phocas, Marios C.; Tryfonos, George; Christoforou, Eftychios G.
year	2024
title	Application of a Vertical Effective Crank–Slider Approach in Reconfigurable Buildings through Computer-Aided Algorithmic Modelling
doi	https://doi.org/10.52842/conf.ecaade.2024.1.421
source	Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 421–430
summary	Elementary robotics mechanisms based on the effective crank–slider and four–bar kinematics methods have been applied in the past to develop architectural concepts of reconfigurable structures of planar rigid-bar linkages (Phocas et al., 2020; Phocas et al., 2019). The applications referred to planar structural systems interconnected in parallel to provide reconfigurable buildings with rectangular plan section. In enabling structural reconfigurability attributes within the spatial circular section buildings domain, a vertical setup of the basic crank–slider mechanism is proposed in the current paper. The kinematics mechanism is integrated on a column placed at the middle of an axisymmetric circular shaped spatial linkage structure. The definition of target case shapes of the structure is based on a series of numerical geometric analyses that consider certain architectural and construction criteria (i.e., number of structural members, length, system height, span, erectability etc.), as well as structural objectives (i.e., structural behavior improvement against predominant environmental actions) aiming to meet diverse operational requirements and lightweight construction. Computer-aided algorithmic modelling is used to analyze the system's kinematics, in order to provide a solid foundation and enable rapid adaptation for mechanisms that exhibit controlled reconfigurations. The analysis demonstrates the implementation of digital parametric design tools for the investigation of the kinematics of the system at a preliminary design stage, in avoiding thus time-demanding numerical analysis processes. The design process may further provide enhanced interdisciplinary performance-based design outcomes.
keywords	Reconfigurable Structures, Spatial Linkage Structures, Kinematics, Parametric Associative Design
series	eCAADe
email
last changed	2024/11/17 22:05

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