Cumincad : CUMINCAD Papers : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id	caadria2022_33
id	caadria2022_33
authors	Alva, Pradeep, Mosteiro-Romero, Martin, Miller, Clayton and Stouffs, Rudi
year	2022
title	Digital Twin-Based Resilience Evaluation of District-Scale Archetypes
doi	https://doi.org/10.52842/conf.caadria.2022.1.525
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 525-534
summary	District-scale energy demand models can be powerful tools for understanding interactions in complex urban areas and optimising energy systems in new developments. The process of coupling characteristics of urban environments with simulation software to achieve accurate results is nascent. We developed a digital twin through a web map application for a 170ha district-scale university campus as a pilot. The impact on the built environment is simulated with pandemic (COVID-19) and climate change scenarios. The former can be observed through varying occupancy rates and average cooling loads in the buildings during the lockdown period. The digital twin dashboard was built with visualisations of the 3D campus, real-time data from sensors, energy demand simulation results from the City Energy Analyst (CEA) tool, and occupancy rates from WiFi data. The ongoing work focuses on formulating a resilience assessment metric to measure the robustness of buildings to these disruptions. This district-scale digital twin demonstration can help in facilities management and planning applications. The results show that the digital twin approach can support decarbonising initiatives for cities.
keywords	Digital twin, City Information Modelling, Planning Support System, energy demand model, SGD 11, SGD 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_42
id	caadria2022_42
authors	Chen, Jielin and Stouffs, Rudi
year	2022
title	Robust Attributed Adjacency Graph Extraction Using Floor Plan Images
doi	https://doi.org/10.52842/conf.caadria.2022.2.385
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 385-394
summary	Architectural design solutions are intrinsically structured information with a broad range of interdependent scopes. Compared to conventional 2D Euclidean data such as orthographic drawings and perspectives, non-Euclidean data (e.g., attributed adjacency graphs) can be more effective and accurate for representing 3D architectural design information, which can be useful for numerous design tasks such as spatial analysis and reasoning, and practical applications such as floor plan parsing and generation. Thus, getting access to a matching attributed adjacency graph dataset of architectural design becomes a necessity. However, the task of conveniently acquiring attributed adjacency graphs from existing architectural design solutions still remains an open challenge. To this end, this project leverages state-of-the-art image segmentation techniques using an ensemble learning scheme and proposes an end-to-end framework to efficiently extract attributed adjacency graphs from floor plan images with diverse styles and varied levels of complexity, aiming at addressing generalization issues of existing approaches. The proposed graph extraction framework can be used as an innovative tool for advancing design research infrastructure, with which we construct a large-scale attributed adjacency graph dataset of architectural design using floor plan images retrieved in bulk. We have open sourced our code and dataset.
keywords	attributed adjacency graph, floor plan segmentation, ensemble learning, architectural dataset, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_325
id	caadria2022_325
authors	Cui, Qinyu, Zhang, Shuyu and Huang, Yiting
year	2022
title	Retail Commercial Space Clustering Based on Post-carbon Era Context: A Case Study of Shanghai
doi	https://doi.org/10.52842/conf.caadria.2022.1.515
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 515-524
summary	In the post-carbon era, it has become a development and research trend on adjusting commercial locations to help achieve resource conservation by using big data. This paper uses multi-source urban data and machine learning to make reasonable evaluations and adjustments to commercial district planning. Many relevant factors are affecting urban commercial agglomeration, but how to select the appropriate ones among the many factors is a problem to be considered and studied, while there may be spatial differences in the strength of each influencing factor on commercial agglomeration. Therefore, this paper takes Shanghai, a city with a high economic and commercial development level in China, as an example and identifies the influencing factors through a literature review. Next, this paper uses the machine learning BORUTA algorithm of features selection to screen the influencing factors. It then uses multi-scale geographically weighted regression model (MGWR) to analyse the spatial heterogeneity of factors affecting retail spatial agglomeration. Finally, based on the background of the changing transportation modes and the unchanged social activities in the post-carbon era, the future spatial planning pattern of retail commercial space is discussed to provide particular suggestions for the future location adjustment of urban commerce.
keywords	Business District Hierarchy, Agglomeration Effect, Spatial Variability, Multi-scale Geographically Weighted Regression Model, Machine Learning, Big Data Analysis, SDG 8, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_175
id	ecaade2022_175
authors	Di Carlo, Raffaele, Mittal, Divyae and Vesely, Ondrej
year	2022
title	Generating 3D Building Volumes for a Given Urban Context using Pix2Pix GAN
doi	https://doi.org/10.52842/conf.ecaade.2022.2.287
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. 287–295
summary	Our ability to delegate the most intellectually demanding tasks to machines improves with each passing day. Even in the fields of architecture and design, which were previously thought to be exclusive domain of human creativity and flare, we are moving the first steps towards developing models that can capture the patterns, invisible to the naked eye, embedded in the creative process. These patterns reflect ideas and traditions, imprinted in the collective mind over the course of history, that can be improved upon or serve as a cautionary tale for the new generation of designers in their work of designing an equitable, more inclusive future. Generative Adversarial Networks (GANs) give us the opportunity to turn style and design into learnable features that can be used to automatically generate blueprints and layouts. In this study, we attempt to apply this technology to urban design and to the task of generating a building footprint and volume that fits within the surrounding built environment. We do so by developing a Pix2Pix model composed of a ResNet-6 generator and a Patch discriminator, applying it to satellite views of neighborhoods from across the Netherlands, and then turning the resulting 2D generated building footprint into a reusable 3D model. The model is trained using the national cadastral data and TU Delft 3D BAG dataset. The results show that it is possible to predict a building shape compatible in style and height with the surroundings. Although the model can be used for different applications, we use it as an evaluation tool to compare the design alternatives fitting the desired contextual patterns.
keywords	Generative Adversarial Networks, Urban Design, Pix2Pix, Raster Vectorization, 3D Rendering
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_89
id	ecaade2022_89
authors	Di Mascio, Danilo
year	2022
title	An Untold Story of a Creative Community of Level Designers - Designing and sharing imaginary navigable virtual environments with game technologies
doi	https://doi.org/10.52842/conf.ecaade.2022.1.481
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 1, Ghent, 13-16 September 2022, pp. 481–490
summary	The following paper describes and critically reflects on the remarkable production of a creative community of level designers who designed and published 3D game levels (3D real-time virtual navigable environments) during the end of the 1990s and the first decade of the 2000s. During those years, many level designers from several countries created an impressive number and variety of custom levels (user-created content), characterised by imaginary architectures and places informed by narrative elements. This international community was supported by various websites that are no longer available. However, an open-source website, Unreal Archive, constitutes “an initiative to preserve and maintain availability of the rich and vast history of user-created content for the Unreal and Unreal Tournament series of games” (Unreal Archive, 2022). The number of levels available on Unreal Archive exceeds 34,000. For the first time in the architectural research community, this paper aims to shed light on the creative production of that period, and to identify and critically reflect on aspects that could have cultural, creative and educational value for architecture and architectural education. The author directly experienced the achievements of that historical period, and created and published a number of virtual environments using early versions of the Unreal Editor/Engine and 3D modelling software. This research is part of a larger project that investigates transdisciplinary expressions of spaces and architectures, as well as concepts, methodologies and tools in the video games field that can inspire or be transferred to the architecture field.
keywords	Virtual Environments, Imaginary Architectures and Places, Narrative, 3D Navigable Environments, Digital Heritage, User-Created Content, Unreal Editor, Unreal Series, Video Games, Level Design, Environmnetal Storytelling
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_145
id	caadria2022_145
authors	Duering, Serjoscha, Fink, Theresa, Chronis, Angelos and Konig, Reinhard
year	2022
title	Environmental Performance Assessment - The Optimisation of High-Rises in Vienna
doi	https://doi.org/10.52842/conf.caadria.2022.1.545
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 545-554
summary	Our cities are facing different kinds of challenges - in parallel to the urban transformation and densification, climate targets and objectives of decision-makers are on the daily agenda of planning. Therefore, the planning of new neighbourhoods and buildings in high-density areas is complex in many ways. It requires intelligent processes that automate specific aspects of planning and thus enable impact-oriented planning in the early phases. The impacts on environment, economy and society have to be considered for a sustainable planning result in order to make responsible decisions. The objective of this paper is to explore pathways towards a framework for the environmental performance assessment and the optimisation of high-rise buildings with a particular focus on processing large amounts of data in order to derive actionable insights. A development area in the urban centre of Vienna serves as case study to exemplify the potential of automated model generation and applying ML algorithm to accelerate simulation time and extend the design space of possible solutions. As a result, the generated designs are screened on the basis of their performance using a Design Space Exploration approach. The potential for optimisation is evaluated in terms of their environmental impact on the immediate environment.
keywords	simulation, prediction and evaluation, machine learning, computational modelling, digital design, high-rises, SGD 11, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_93
id	caadria2022_93
authors	Feng, Jiajia, Liang, Yuebing, Hao, Qi, Xu, Ke and Qiu, Waishan
year	2022
title	POI Data Versus Land Use Data, Which Are Most Effective in Modelling Theft Crimes?
doi	https://doi.org/10.52842/conf.caadria.2022.1.425
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 425-434
summary	Alleviating crime and improving urban safety is important for sustainable development of society. Prior studies have used either land use data or point-of-interests (POI) data to represent urban functions and investigate their associations with urban crime. However, inconsistent and even contrary results were yielded between land use and POI data. There is no agreement on which is more effective. To fill this gap, we systematically compare land use and POI data regarding their strength as well as the divergence and coherence in profiling urban functions for crime studies. Three categories of urban function features, namely the density, fraction, and diversity, are extracted from POI and land use data, respectively. Their global and local strength are compared using ordinary least square (OLS) regression and geographically weighted regression (GWR), with a case study of Beijing, China. The OLS results indicate that POI data generally outperforms land use data. The GWR models reveal that POI Density is superior to other indicators, especially in areas with concentrated commercial or public service facilities. Additionally, Land Use Fraction performs better for large-scale functional areas like green space and transportation hubs. This study provides important reference for city planners in selecting urban function indicators and modelling crimes.
keywords	POI, Land Use, Urban Functions, Theft crime, Predictive Power, SDG 16
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_102
id	caadria2022_102
authors	Gardner, Nicole, Haeusler, Matthias Hank, Yu, Daniel, Barton, Jack, Dunn, Kate and Huang, Tracy
year	2022
title	Revisiting Shoei Yoh: Developing a Workflow for a Browser-Based 3D Model Environment to Create an Immersive Digital Archive
doi	https://doi.org/10.52842/conf.caadria.2022.1.687
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 687-696
summary	The digitisation of architecturally significant buildings and sites creates opportunities to innovate methods of analysis, interpretation, representation, and audience engagement. To illustrate this potential, but also examine the attendant challenges, this paper outlines a research project that has digitised archival assets and living buildings designed by the Japanese architect Shoei Yoh to create an immersive 3D Spatial Archive. It focuses particularly on the creation of a browser-based 3D environment using WebGL technology that connects to and displays a repository of digitised archival assets. This includes the use of 3D scan data of Yoh's Naiju Community Centre project to accurately model the 3D immersive environment and a Grasshopper / Rhino into the glTF. File format (graphics library Transmission Format) workflow to render Naiju‚s complex geometry and detailed outdoor scenery. The paper demonstrates how using the .glTF File, which is an open format specifically for transmitting processed and pre-calculated 3D models, can improve the processing efficiency of web-browser based 3D environments. Improving the stability and processing speed of 3D browser-based environments is significant to enhancing how audiences can connect with and experience culturally significant sites remotely. The digital recreation and repurposing of Naiju (which is currently unoccupied and in a state of disrepair) as an immersive archival exhibition space operates to simultaneously protect the real building from over visitation, but also raise awareness of its cultural significance to support preservation efforts. In so doing, the paper makes a further contribution to the developing field of digital cultural heritage.
keywords	Digital Cultural Heritage, Browser-based Modelling, glTF File, Architectural Visualisation, Shoei Yoh, SDG 9, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ijac202220216
id	ijac202220216
authors	Keyvanfar, Ali; Arezou Shafaghat; Muhamad SF Rosley
year	2022
title	Performance comparison analysis of 3D reconstruction modeling software in construction site visualization and mapping
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 453–475
summary	Unmanned aerial vehicle (UAV) technology has overcome the limitations of conventional construction management methods using advanced and automated visualization and 3D reconstruction modeling techniques. Although the mapping techniques and reconstruction modeling software can generate real-time and high-resolution descriptive textural, physical, and spatial data, they may fail to develop an accurate and complete 3D model of the construction site. To generate a quality 3D reconstruction model, the construction manager must optimize the trade-offs among three major software-selection factors: functionalities, technical capabilities, and the system hardware specifications. These factors directly affect the robust 3D reconstruction model of the construction site and objects. Accordingly, the purpose of this research was to apply nine well-established 3D reconstruction modeling software tools (DroneDeploy, COLMAP, 3DF+Zephyr, Autodesk Recap, LiMapper, PhotoModeler, 3D Survey, AgiSoft Photoscan, and Pix4D Mapper) and compare their performances and reliabilities in generating complete 3D models. The research was conducted in an eco-home building at the University of Technology, Malaysia. A series of regression analyses were conducted to compare the performances of the selected 3D reconstruction modeling software in alignment and registration, distance computing, geometric measurement, and plugin execution. Regression analysis determined that among the software programs, LiMapper had the strongest positive linear correlation with the ground truth model. Furthermore, the correlation analysis showed a statistically significant p-value for all software, except for 3D Survey. In addition, the research found that Autodesk Recap generated the most-robust and highest-quality dense point clouds. DroneDeploy can create an accurate point cloud and triangulation without using many points as required by COLMAP and LiMapper. It was concluded that most of the software is robustly, positively, and linearly correlated with the corresponding ground truth model. In the future, other factors involving software selection should be studied, such as vendor-related, user-related, and automation factors.
keywords	Construction site visualization, unmanned aerial vehicle, photogrammetry, 3D reconstruction modeling, multi-view-stereopsis, structure-from-motion, ANOVA and regression analysis
series	journal
last changed	2024/04/17 14:29

_id	acadia22_182
id	acadia22_182
authors	Tessmer, Lavender; Goldstein, Ganit; Herrera-Arcos, Guillermo; Korolovych, Volodymyr; Bellisle, Rachel; Paige, Cody; Shallal, Christopher; Sahasrabudhe, Atharva; Herr, Hugh
year	2022
title	3D Knit Spacesuit Sleeve
source	ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 182-195.
summary	This paper presents a novel approach to spacesuit fabrication and functionality using CNC knitting to enable precise material control throughout the three-dimensional structure, creating higher functionality in a seamless and minimal textile architecture. We have developed a 3D textile framework consisting of a computational design workflow, multifunctional fiber integration, and a highly customizable 3D layering method that can be adapted to the personalized dimensions of the body. This method includes designating regions for mobility, tunable compression, integrated sensing, and quick donning and doffing within a single sleeve prototype as a first step toward a novel approach for spacesuit fabrication. While this work has focused on the spacesuit application, we imagine future applications in other textile architectures and next-generation apparel with integrated monitoring for increased performance, environmental regulation, and improved comfort.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:00

_id	caadria2022_169
id	caadria2022_169
authors	Xu, Hang and Wang, Tsung-Hsien
year	2022
title	An Integrated Parametric Generation and Computational Workflow to Support Sustainable City Planning
doi	https://doi.org/10.52842/conf.caadria.2022.1.535
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 535-544
summary	To examine how efforts in the built environment can contribute to global climate change mitigation at the urban scale, urban building energy modelling (UBEM) is one of the research areas gaining increasing interest in recent years. However, limited studies systematically illustrate a comprehensive UBEM workflow for most architects and urban planners considering available public datasets, particularly at the early conceptual design phase. In current UBEM studies, major challenges arise from the lack of fine-grained measured urban data and incompatibility between software. To address these challenges and support future sustainable cities and communities, this paper proposed a streamlined computational workflow of UBEM to facilitate sustainable urban design development. Through a case study of Sheffield in the UK, this paper demonstrated an automated and standardised computational workflow that can test the decarbonisation potential in built environments by evaluating energy demand and supply scenarios at an urban scale. This workflow is envisaged to be applicable at various scales of an urban region given an appropriate geographic information system (GIS) dataset.
keywords	Parametric Design Generation, Urban Sustainability, Urban Building Energy Modelling, Building Performance Simulation, Renewable Energy, Decarbonisation, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_290
id	ecaade2022_290
authors	Yalçinkaya, Sezgi and Çolakoglu, Meryem Birgül
year	2022
title	A Visibility Algorithm Based on Voxel Modelling Method Developed for Architectural Geometries
doi	https://doi.org/10.52842/conf.ecaade.2022.2.057
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. 57–66
summary	A visibility algorithm has been developed and practiced on the iconic towers of Istanbul from various locations with multiple observation points. This research accepts visibility as a binary subject and limited subjective perspectives. Using visibility as a design strategy is a prevalent technique for designers. Applying visibility aspects to the design is an outcome of psychological research in the 60s. Benedikt was the first person who added visibility to his architectural studies. Visibility refers to being able to see or be seen from a distance. This research accepts and practices this definition on two iconic towers of the city of Istanbul with the model MoV (Model of Visibility) developed. The implantation of the developed model is represented with the voxel modeling method. To be able to provide volumetric data, voxel-based representation was explicitly applied to the digital models of iconic towers. This representation model aimed to illustrate and quantify the visible and non-visible areas according to the location of the observation point’s visibility range.
keywords	Visibility, Isovist, Algorithym, Iconic Structures, Visibility Values, Developed Algorithm
series	eCAADe
email
last changed	2024/04/22 07:10

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

_id	ecaade2022_168
id	ecaade2022_168
authors	Abdulmawla, Abdulmalik, Schneider, Sven, Koenig, Reinhard, Bielik, Martin and Fuchkina, Ekaterina
year	2022
title	Parametric Urban Data Structuring and Spatial Query - Advanced data mapping and selection methods for parametric modelling environments
doi	https://doi.org/10.52842/conf.ecaade.2022.2.277
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. 277–286
summary	This paper presents a method for organising urban data inside the CAD environment into a hierarchical structure, which promotes the ease of transferring information between all available urban elements, from streets to buildings passing by the plots and blocks. This is done using parametric methods that map the urban data using the available CAD and GIS records. Finally, the paper presents a couple of example scenarios where such methods are most needed and how much they could facilitate more detailed and complex data to be accessed, compared, and analysed.
keywords	Urban Query, Urban Geometry, Spatial Mapping
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_234
id	ecaade2022_234
authors	Afsar, Secil, Estévez, Alberto T., Abdallah, Yomna K., Turhan, Gozde Damla, Ozel, Berfin and Doyuran, Aslihan
year	2022
title	Activating Co-Creation Methodologies of 3D Printing with Biocomposites Developed from Local Organic Wastes
doi	https://doi.org/10.52842/conf.ecaade.2022.1.215
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 1, Ghent, 13-16 September 2022, pp. 215–224
summary	Compared to the take-make-waste-oriented linear economy model, the circular model has been studied since the 1980s. Due to consumption-oriented lifestyles along with having a tendency of considering waste materials as trash, studies on sustainable materials management (SMM) have remained at a theoretical level or created temporary and limited impacts. To ensure SMM supports The European Green Deal, there is a necessity of developing top-down and bottom-up strategies simultaneously, which can be metaphorized as digging a tunnel from two different directions to meet in the middle of a mountain. In parallel with the New European Bauhaus concept, this research aims to create a case study for boosting bottom-up and data-driven methodologies to produce short-loop products made of bio-based biocomposite materials from local food & organic wastes. The Architecture departments of two universities from different countries collaborated to practice these design democratization methodologies using data transfer paths. The 3D printable models, firmware code, and detailed explanation of working with a customized 3D printer paste extruder were shared using online tools. Accordingly, the bio-based biocomposite recipe from eggshell, xanthan gum, and citric acid, which can be provided from local shops, food & organic wastes, was investigated concurrently to enhance its printability feature for generating interior design elements such as a vase or vertical gardening unit. While sharing each step from open-source platforms with adding snapshots and videos allows further development between two universities, it also makes room for other researchers/makers/designers to replicate the process/product. By combining modern manufacturing and traditional crafting methods with materials produced with DIY techniques from local resources, and using global data transfer platforms to transfer data instead of products themselves, this research seeks to unlock the value of co-creative design practices for SMM.
keywords	Sustainable Materials Management, Co-Creation, Food Waste, 3D Printing, New European Bauhaus
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_277
id	caadria2022_277
authors	Akbar, Zuardin, Wood, Dylan, Kiesewetter, Laura, Menges, Achim and Wortmann, Thomas
year	2022
title	A Data-Driven Workflow for Modelling Self-Shaping Wood Bilayer, Utilizing Natural Material Variations with Machine Vision and Machine Learning
doi	https://doi.org/10.52842/conf.caadria.2022.1.393
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 393-402
summary	This paper develops a workflow to train machine learning (ML) models with a small dataset from physical samples to predict the curvatures of self-shaping wood bilayers based on local variations in the grain. In contrast to state-of-the-art predictive models, specifically 1.) a 2D Timoshenko model and 2.) a 3D numerical model with a rheological model, our method accounts for natural and unavoidable material variations. In this paper, we only focus on local grain variations as the main driver for curvatures in small-scale material samples. We extracted a feature matrix from grain images of active and passive layers as a Grey Level Co-Occurrence Matrix and used it as the input for our ML models. We also analysed the impact of grain variations on the feature matrix. We trained and tested several tree-based regression models with different features. The models achieved very accurate predictions for curvatures in each sample (R;0.9) and extend the range of parameters that is incalculable by a Timoshenko model. This research contributes to the material-efficient design of weather-responsive shape-changing wood structures by further leveraging the use of natural material features and explainable data-driven modelling and extends the topic in ML for material behaviour-driven design among the CAADRIA community.
keywords	data-driven model, machine learning, material programming, smart material, timber structure, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	acadia22_68
id	acadia22_68
authors	Al Othman, Sulaiman; Bechthold, Martin
year	2022
title	Non-Linear Fabrication
source	ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 68-75.
summary	This paper describes an improved data collection methodology in the context of clay 3D printing that integrates structured light scanning tech- nology. The ultimate goal is to use this data for toolpath calibration during the next step of the research. The integrated process measures and then addresses the deflections caused by the successive build-up of clay layers that cause changes in stiffness across the lower printed layers, distortions and shifting of clay beads caused by extrusion pressure and nozzle maneuvering, and air gaps in the clay mix that affect the material flow rate.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:00

_id	cdrf2022_293
id	cdrf2022_293
authors	Amal Algamdey, Aleksander Mastalski, Angelos Chronis, Amar Gurung, Felipe Romero Vargas, German Bodenbender, and Lea Khairallah
year	2022
title	AI Urban Voids: A Data-Driven Approach to Urban Activation
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_26
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	With the development of digital technologies, big urban data is now readily available online. This opens the opportunity to utilize new data and create new relationships within multiple urban features for cities. Moreover, new computational design techniques open a new portal for architects and designers to reinterpret this urban data and provide much better-informed design decisions. The “AI Urban Voids'' project is defined as a data-driven approach to analyze and predict the strategic location for urban uses in the addition of amenities within the city. The location of these urban amenities is evaluated based on predictions and scores followed by a series of urban analyses and simulations using K-Means clustering. Furthermore, these results are then visualized in a web-based platform; likewise, the aim is to create a tool that will work on a feedback loop system that constantly updates the information. This paper explains the use of different datasets from Five cities including Melbourne, Sydney, Berlin, Warsaw, and Sao Paulo. Python, Osmx libraries and K-means clustering open the way to manipulate large data sets by introducing a collection of computational processes that can override traditional urban analysis.
series	cdrf
email
last changed	2024/05/29 14:02

_id	caadria2022_167
id	caadria2022_167
authors	Aman, Jayedi, Matisziw, Timothy C, Kim, Jong Bum and Luo, Dan
year	2022
title	Sensing the City: Leveraging Geotagged Social Media Posts and Street View Imagery to Model Urban Streetscapes Using Deep Neural Networks
doi	https://doi.org/10.52842/conf.caadria.2022.1.595
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 595-604
summary	Understanding the relationships between individuals and the urban streetscape is an essential component of sustainable city planning. However, analysis of these relationships involves accounting for a complex mix of human behaviour, perception, as well as geospatial context. In this context, a comprehensive framework for predicting preferred streetscape characteristics utilizing deep learning and geospatial techniques is proposed. Geotagged social media posts and street view imagery are employed to account for individual sentiment and geospatial context. Natural Language Processing (NLP) and computer vision (CV) are then used to infer sentiment and model the visual environment within which individuals make posts to social media. An application of the developed framework is provided using Instagram posts and Google Street View imagery of the urban environment. A spatial analysis is conducted to assess the extent to which urban attributes correlate with the sentiment of social media postings. The results shed light on sustainable streetscape planning by focusing on the relationship between users and the built environment in a complex urban setting. Finally, limitations of the developed methodology as well as future directions are discussed.
keywords	Urban sustainability, data mining, pedestrian sentiments, transportation behavior, street level imagery, transformers, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_357
id	caadria2022_357
authors	Bedarf, Patrick, Szabo, Anna, Zanini, Michele, Heusi, Alex and Dillenburger, Benjamin
year	2022
title	Robotic 3D Printing of Mineral Foam for a Lightweight Composite Concrete Slab
doi	https://doi.org/10.52842/conf.caadria.2022.2.061
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 61-70
summary	This paper presents the design and fabrication of a lightweight composite concrete slab prototype using 3D printing (3DP) of mineral foams. Conventionally, concrete slabs are standardized monolithic elements that are responsible for a large share of used materials and dead weight in concrete framed buildings. Optimized slab designs require less material at the expense of increasing the formwork complexity, required labour, and costs. To address these challenges, foam 3D printing (F3DP) can be used in construction as demonstrated in previous studies for lightweight facade elements. The work in this paper expands this research and uses F3DP to fabricate the freeform stay-in-place formwork components for a material-efficient lightweight ribbed concrete slab with a footprint of 2 x 1.3 m. For this advancement in scale, the robotic fabrication and material processing setup is refined and computational design strategies for the generation of advanced toolpaths developed. The presented composite of hardened mineral foam and fibre-reinforced ultra-high-performance concrete shows how custom geometries can be efficiently fabricated for geometrically complex formwork. The prototype demonstrates that optimized slabs could save up to 72% of total concrete volume and 70% weight. The discussion of results and challenges in this study provides a valuable outlook on the viability of this novel fabrication technique to foster a sustainable and resourceful future construction culture.
keywords	robotic 3d-printing, mineral foam, stay-in-place formwork, concrete composite, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

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