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_336
id	caadria2022_336
authors	Araujo, Goncalo, Santos, Luis, Leitao, Antonioand Gomes, Ricardo
year	2022
title	AD-Based Surrogate Models for Simulation and Optimization of Large Urban Areas
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. 689-698
doi	https://doi.org/10.52842/conf.caadria.2022.2.689
summary	Urban Building Energy Model (UBEM) approaches help analyze the energy performance of urban areas and predict the impact of different retrofit strategies. However, UBEM approaches require a high level of expertise and entail time-consuming simulations. These limitations hinder their successful application in designing and planning urban areas and supporting the city policy-making sector. Hence, it is necessary to investigate alternatives that are easy-to-use, automated, and fast. Surrogate models have been recently used to address UBEM limitations; however, they are case-specific and only work properly within specific parameter boundaries. We propose a new surrogate modeling approach to predict the energy performance of urban areas by integrating Algorithmic Design, UBEM, and Machine Learning. Our approach can automatically model and simulate thousands of building archetypes and create a broad surrogate model capable of quickly predicting annual energy profiles of large urban areas. We evaluated our approach by applying it to a case study located in Lisbon, Portugal, where we compare its use in model-based optimization routines against conventional UBEM approaches. Results show that our approach delivers predictions with acceptable accuracy at a much faster rate.
keywords	urban building energy modelling, algorithmic design, machine learning in Architecture, optimization of urban areas, SDG 7, SDG 12, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_507
id	caadria2022_507
authors	Bolojan, Daniel, Vermisso, Emmanouil and Yousif, Shermeen
year	2022
title	Is Language All We Need? A Query Into Architectural Semantics Using a Multimodal Generative Workflow
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. 353-362
doi	https://doi.org/10.52842/conf.caadria.2022.1.353
summary	This project examines how interconnected artificial intelligence (AI)-assisted workflows can address the limitations of current language-based models and streamline machine-vision related tasks for architectural design. A precise relationship between text and visual feature representation is problematic and can lead to "ambiguity‚ in the interpretation of the morphological/tectonic complexity of a building. Textual representation of a design concept only addresses spatial complexity in a reductionist way, since the outcome of the design process is co-dependent on multiple interrelated systems, according to systems theory (Alexander 1968). We propose herewith a process of feature disentanglement (using low level features, i.e., composition) within an interconnected generative adversarial networks (GANs) workflow. The insertion of natural language models within the proposed workflow can help mitigate the semantic distance between different domains and guide the encoding of semantic information throughout a domain transfer process.
keywords	Neural Language Models, GAN, Domain Transfer, Design Agency, Semantic Encoding, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_211
id	ecaade2022_211
authors	Bonafede, Andrea and Erioli, Alessio
year	2022
title	Versus Habitat - Multi agent spatial negotiation for topology-aware, large scale architectural assemblages
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. 113–122
doi	https://doi.org/10.52842/conf.ecaade.2022.2.113
summary	With the burst of automation in the AEC industry, modular design for collective living is having a reissue; as for industrial construction in the post WW2 era, the economies of a construction system trigger urban models, but an exploration of non-standard spatial models based on computational methods is still lacking. This research proposes a competition-based process for the design of large scale (urban) collective habitats as topology-aware architectural assemblages of spatial (as in including constructive elements + void) components. Two competing multi-agent systems negotiate spatial occupancy, leveraging the morphological computation capabilities of individual and combined components at increasing scales. Localized information stored in the environment by the agents is converted in architectural components, resulting in a multi- level spatial organization that transcends typical typological classification. Space syntax techniques are used to map the assemblage properties and support design inferences on spatial occupation such as potentially implementable functional programmes.
keywords	Multi-agent System, Automation, Assemblages, Stigmergy, Space Syntax
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ascaad2022_065
id	ascaad2022_065
authors	David, Joao; Leitao, Antonio
year	2022
title	Getting a Handle on Floor Plan Analysis: Door Classification in Floor Plans and a Survey on Existing Datasets
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 221-236
summary	Floor plan interpretation and reconstruction is crucial to enable the transformation of drawings to 3D models or different digital formats. It has recently taken advantage of neural-based architectures, especially in the semantic segmentation field. These techniques perform better than traditional methods, but the results depend mainly on the data used to train the networks, which is often crafted for the specific task being performed, making it hard to reuse for different purposes. In this paper, we conduct a literature survey on the existing datasets for floor plan analysis, and we explore how information regarding door placement and orientation can be recovered without having to change the initial data or model. We propose a two-step recognition method based on image segmentation followed by classification of cropped zones to allow data augmentation during training. In the process, we generate a dataset consisting of 35000 annotated door images extracted from an existing dataset.
series	ASCAAD
email
last changed	2024/02/16 13:29

_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?
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
doi	https://doi.org/10.52842/conf.caadria.2022.1.425
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	acadia22pr_94
id	acadia22pr_94
authors	Fereos, Pavlos; Efthimiou, Eftychios-Nicolaos; Bauer, Kilian; Edelmann, Julian
year	2022
title	Additive Hyper-Ornamental Prototypes - Surface Articulation as Structural Leverage in 3D Printing
source	ACADIA 2022: Hybrids and Haecceities [Projects Catalog of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-7-4]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 94-99.
summary	This project presents two experimental prototypes built using a 6-axis Cobot (Universal Robots UR10e collaborative robot) and PETG (polyethylene terephthalate glycol) filament processed by a plastic extruder (Herz Robot 0.8). The aim was to incorporate intricate design elements into 3D models to test or even increase the material’s structural abilities and to 3D print large and highly articulated architectural mock-up models on a 1:1 scale.
series	ACADIA
type	project
email
last changed	2024/02/06 14:06

_id	cdrf2022_199
id	cdrf2022_199
authors	Jingming Li
year	2022
title	Using Text Understanding to Create Formatted Semantic Web from BIM
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_17
summary	The application of BIM in the building life cycle needs to be continuous. The information collected and accumulated in the early stages should flow to the subsequent phases. However, BIM applications currently focus on collision inspection, compliance inspection, and engineering calculation, few models can be successively used in the following stages. Remodeling is required in the operation and maintenance period, resulting in waste. Meanwhile, some of the information accumulated by BIM might be frequently used in the operation and maintenance stage, while some data are relatively rarely used. The semantic web can help manage building information at all stages. But the generation of a semantic web is mostly manually completed. It is necessary to standardize the repeated semantic description in the model and convert BIM into a standard semantic model for information indexing, reducing the resource consumption of model loading and optimizing the efficiency of the operation and maintenance system. When the existing research transforms from BIM to the semantic web, there will be a lack of information and descriptions of the ownership relationship between entities due to the limitation of formats. To realize the standard transformation from BIM to the semantic web, this work proposes a method of using Natural Language Processing (NLP) to understand the text and infer the relationship between entities according to the knowledge map. First, the entities are extracted from BIM, such as air conditioning unit, electric lamp, fan, etc., if the name of the extracted entity is irregular, the names are translated with the help of NLP and Ontology (such as brick or haystack) to obtain the standard definition. By comparing the complete knowledge graph (such as the knowledge graph of the air conditioning system), the relationships can be deduced, and then a standardized semantic model can be generated.
series	cdrf
email
last changed	2024/05/29 14:02

_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	caadria2022_238
id	caadria2022_238
authors	Liu, Nuozhi and Koh, Immanuel
year	2022
title	Machine-Reading Places & Spaces: Generative Probabilistic Modelling of Urban Thematic Zones & Contexts
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. 465-474
doi	https://doi.org/10.52842/conf.caadria.2022.1.465
summary	In this paper, a "place" is conceptualised as a composition of dynamic socioeconomic activities and collective perceptions. We apply generative probabilistic modelling to explore urban contextual semantics. By analogy to sorting documents into different topics, this research retrieves data embedding for each urban regions and classify them with thematic zones. Using Singapore as a case study, topic modelling is applied to retrieve perceptual and functional thematic zones from Instagram and TripAdvisor respectively. A subsequent analysis shows strong correlations among certain regions with functional and perceptual consistency. In addition, with our proposed uniqueness and diversity indices, a strong negative correlation at 0.82 is found, suggesting that a region could be more unique if the functions tend to be dominated by certain types of functional and perceptual thematic zones.
keywords	Machine Learning, Natural Language Processing, Generative Probabilistic Models, Urban Data Modelling, Thematic Zones, Topic Modelling, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2022_179
id	sigradi2022_179
authors	Pappa, Androniki; Paio, Alexandra; Duering, Serjoscha; Chronis, Angelos
year	2022
title	Understanding participation though a data-driven approach
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 77–88
summary	Participatory models in urban regeneration are increasingly integrated in local agendas. Yet there is still a need for evaluation methodologies of those models and their impact. This paper presents a data-driven and computational methodology to measure the impact of the BIP/ZIP program in Lisbon. Using qualitative coding, data integration, unsupervised machine learning models for data clustering and interactive visualization dashboards the study aims to explore the large and complex dataset of the projects of BIP/ZIP and identify correlation patterns between their data and especially the areas of implementation, the networks of partners and the identified activities. Departing from the pilot-case of BIP/ZIP, the proposed methodology is a first step towards the development of a generalizable evaluation framework for participatory models in urban regeneration, that considers them as urban practices and hence evaluates them based on appropriate urban tools.
keywords	Participatory Strategies, Participation Evaluation, Data-Driven Evaluation, Unsupervised Learning, Data Visualization
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	ecaade2022_105
id	ecaade2022_105
authors	Trento, Armando, Fioravanti, Antonio, Kieferle, Joachim and Woessner, Uwe
year	2022
title	Bridging Cultural Heritage Ontologies in VR Environment - A framework for querying and reasoning on the Temple of Venus and Rome restoration and documentation
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. 177–186
doi	https://doi.org/10.52842/conf.ecaade.2022.2.177
summary	VR applied to Architectural and Archaeological Heritage has a long history: Digital models in this field are evolving from an aesthetic simulation of reality, or, rather, a representation of the visual perception, to a more complex model: an information aggregation core. The investigation presents a research panel oriented to enhance the digital survey products - point clouds, meshes, 3D models -to be used as an intelligent visual archive assigning structured knowledge contents to artefacts’ geometry. The implemented case regards the Temple of Venus and Rome. Research, in progress, has been developed by the following steps: 1) Subdividing the artefact geometry into sub- regions; 2) Developing the consolidation ontology for a few restoration classes; 3) Assigning (manually) to each artefact subcomponent, namely a mesh sub-region, a “smart label” including a link to its consolidation ontology instance. The aim is to combine the potential of VR visualization with ontology reasoning systems.
keywords	VR, Archaeological Heritage, Knowledge-based Design Systems, Restoration Ontologies
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_172
id	ecaade2022_172
authors	Vugreshek, Zvonko
year	2022
title	Discrete Differences between Aggregate Systems for Generative Urban and Architectural Design
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. 29–38
doi	https://doi.org/10.52842/conf.ecaade.2022.2.029
summary	The activation of aggregate systems, procedural generation, and other models of discrete computation result in different organizations and formal outcomes. Some differences seem blurry but are relevant to understand in order to govern the computational design process in the specific domain. They are developing around empiric principles, are based on discrete automation rule sets, and are intertwined in various ways. The paper presents and describes some differences and communalities between each system. Its goal is to support the computational designer, architect or urban planner in the decision-making process and choice of which system could work best in a given context and to solve a specific problem. An introduction into aggregation or automation will serve as a foundation for the research. The discrete systems Cellular Automata, Wave Function Collapse, Graph-Grammar Aggregation will be described. In this paper, the latter is specified as selection-based-aggregation. Diffusion-Limited Aggregation (DLA), which is regarded as an early translation of natural behaviour into scripted nature will serve as a framework. In a next step potential and utilization of these discrete systems in expanding the language of architectural and urban morphology will be experimentally demonstrated and compared. The paper concludes by suggesting a current state of development and potential adaptation of the methods for broader use within the architectural and urban design paradigm of developing methods for the creation of new computational typologies.
keywords	Discrete Aggregation, Cellular Automata, Procedural Generation, Urban Morphology Generation, Wave Function Collapse
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ascaad2022_024
id	ascaad2022_024
authors	Yonder, Veli
year	2022
title	Using Artificial Neural Networks and Space Syntax Techniques to Understand Mass Housing Design Parameters
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 283-299
summary	The design of mass housing is a complex process that involves the use of a large number of components and parameters. The field of design has unavoidably been changed by the impact of digitalization, which has resulted in the proliferation of computational design models, data structures, artificial intelligence, and an algorithmic way of thinking. Artificial neural networks, space syntax methodologies, predefined rules will help shape the steps of the schematic design process and establish certain limitations. Within the confines of this research, predefined guidelines were used to bring about geometric variances in the design of mass houses. Both traditional and digital instruments were utilized in the process. Methodologies based on artificial neural network models and space syntax techniques were utilized to investigate case studies and develop prototypes. The artificial neural network model is designed to understand the factors affecting mass housing design parameters. The importance percentages of the parameters were determined according to the outputs of this model. Besides, methodologies based on space syntax have had a significant impact, both on decision-making processes and on feedback-based design. In this study, several digital tools were used to analyze such as visibility graph analyzes, node-based techniques, and isovist analysis. In the section devoted to the conclusion, the comparison of the various prototypes that were obtained, the findings of the space syntax analysis, and the various stages of model development are discussed.
series	ASCAAD
email
last changed	2024/02/16 13:24

_id	cdrf2022_223
id	cdrf2022_223
authors	Zhiyi Dou, Waishan Qiu, Wenjing Li, Dan Luo
year	2022
title	Evaluation Process of Urban Spatial Quality and Utility Trade-Off for Post-COVID Working Preferences
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_19
summary	The formation of cities, and the relocation of workers to densely populated areas reflect a spatial equilibrium, in which the higher real consumption levels of urban areas are offset by lower non-monetary amenities [1]. However, as the society progress toward a post-COVID stage, the prevailing decentralized delivery systems and location-based services, the growing trend of working from home, with citizens’ shifting preference of de-appreciating densities and gathering, have not only changed the possible spatial distribution of opportunities, resources, consumption and amenities, but also transformed people’s preference regarding desirable urban spatial qualities, value of amenities, and working opportunities [2, 3]. This research presents a systematic method to evaluate the perceived trade-off between urban spatial qualities and urban utilities such as amenities, transportation, and monetary opportunities by urban residence in the post-COVID society. The outcome of the research will become a valid tool to drive and evaluate urban design strategies based on the potential self-organization of work-life patterns and social profiles in the designated neighbourhood. To evaluate the subjective perception of the urban residence, the study started with a comparative survey by asking residence to compare two randomly selected urban contexts in a data base of 398 contexts sampled across Hong Kong and state their living preference under the presumption of following scenarios: 1. working from home; 2. working in city centre offices. Core information influencing the spatial equilibrium are provided in the comparable urban context such as street views, housing price, housing space, travel time to city centre, adjacency to public transport and amenities, etc. Each context is given a preference score calculated with Microsoft TrueSkill Bayesian ranking algorithm [4] based on the comparison survey of two scenarios. The 398 contexts are further analysed via GIS and image processing, to be deconstructed into numerical values describing main features for each of the context that influence urban design strategies such as composition of spatial features, amenity allocation, adjacency to city centre and public transportations. Machine learning models are trained with the numerical values of urban features as input and two preference scores for the two working scenarios as the output. The correlation heat maps are used to identify main urban features and its p-value that influence residence’s preference under two working scenarios in post–COVID era. The same model could also be applied to inform the direction of urban design strategies to construct a sustainable community for each type of working population and validate the design strategies via predicting its competitiveness in attracting residence and developing target industries.
series	cdrf
email
last changed	2024/05/29 14:02

_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
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
doi	https://doi.org/10.52842/conf.ecaade.2022.1.215
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	acadia22pr_124
id	acadia22pr_124
authors	Ago, Viola; Tursack, Hans
year	2022
title	Understorey - A Pavilion in Parts
source	ACADIA 2022: Hybrids and Haecceities [Projects Catalog of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-7-4]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 124-129.
summary	In the summer of 2018, our collaboration was awarded a University Design Fellowship from the Exhibit Columbus organization to design, fabricate, and build a large pavilion in Columbus, Indiana as part of a biannual contemporary architecture exhibition. Our proposal for the competition was a pavilion that would double as an ecological education center. Our inspiration for this program was triggered in part by our reading of Jane Bennett’s materialist philosophy outlined in her book Vibrant Matter (2009). Through Bennett’s lens, our design rendered our site’s context as an animate field, replete with pre-existing material composites that we wanted to celebrate through a series of displays, information boards, and artificial lighting. In this, the installation would feature samples of local plants, minerals, and rocks, indigenous to Southern Indiana.
series	ACADIA
type	project
email
last changed	2024/02/06 14:06

_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
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
doi	https://doi.org/10.52842/conf.caadria.2022.1.393
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_000
id	acadia22_000
authors	Akbarzadeh, Masoud; Aviv, Dorit; Jamelle, Hina; Stuart-Smith, Robert
year	2022
title	ACADIA 2022: Hybrids and Haecceities [Proceedings]
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. 839p.
summary	Hybrids & Haecceities seeks novel approaches to design and research that dissolve binary conditions and inherent hierarchies in order to embrace new modes of practice. Haecceities describe the qualities or properties of objects that define them as unique. Concurrently, Hybrids are entities with characteristics enhanced by the process of combining two or more elements with different properties. In concert, these terms offer a provocation toward more inclusive and specific forms of computational design. Hybrids & Haecceities aligns with a fundamental shift away from abstract generalized models of production toward greater degrees of customization at unprecedented scales, made possible by the Fourth Industrial Revolution. With greater reliance on cyber-physical systems, this shift supports more diverse and considered forms of embodiment and participation in the built environment. Conversely, the design and construction industries have profound global effects with significant political, economic, and environmental impacts. The urgent need to decarbonize buildings, and at the same time, provide equitable infrastructure to communities at risk, places responsibility on the design disciplines to form new collaborations in the effort to address today’s social and ecological crises.
series	ACADIA
type	proceedings
email
last changed	2024/02/06 14:00

_id	acadia22_001
id	acadia22_001
authors	Akbarzadeh, Masoud; Aviv, Dorit; Jamelle, Hina; Stuart-Smith, Robert
year	2022
title	ACADIA 2022: Hybrids and Haecceities [Projects Catalog]
source	ACADIA 2022: Hybrids and Haecceities [Projects Catalog of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-7-4]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 240p.
summary	Hybrids & Haecceities seeks novel approaches to design and research that dissolve binary conditions and inherent hierarchies in order to embrace new modes of practice. Haecceities describe the qualities or properties of objects that define them as unique. Concurrently, Hybrids are entities with characteristics enhanced by the process of combining two or more elements with different properties. In concert, these terms offer a provocation toward more inclusive and specific forms of computational design. Hybrids & Haecceities aligns with a fundamental shift away from abstract generalized models of production toward greater degrees of customization at unprecedented scales, made possible by the Fourth Industrial Revolution. With greater reliance on cyber-physical systems, this shift supports more diverse and considered forms of embodiment and participation in the built environment. Conversely, the design and construction industries have profound global effects with significant political, economic, and environmental impacts. The urgent need to decarbonize buildings, and at the same time, provide equitable infrastructure to communities at risk, places responsibility on the design disciplines to form new collaborations in the effort to address today’s social and ecological crises.
series	ACADIA
type	projects catalog
email
last changed	2024/02/06 14:00

_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
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
doi	https://doi.org/10.52842/conf.caadria.2022.1.525
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

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