Cumincad : CUMINCAD Papers : Search Results

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

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_id	ecaade2022_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
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
doi	https://doi.org/10.52842/conf.ecaade.2022.2.287
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	caadria2022_490
id	caadria2022_490
authors	Li, Ce, Guo, Zhe, Cai, Chengzhi, Miao, Junyi, Cao, Xiaoyu, Li, Cong, Guo, Yefei, Cao, Qingning, Zheng, Zifei, Guo, Yuchen, Wu, Wanling, Xu, Zhiyan and Zhou, Xinyan
year	2022
title	Softness and Hardness: What Does Concrete Want? Concrete Physical Form Finding Based on Computational Combined Formwork
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. 233-242
doi	https://doi.org/10.52842/conf.caadria.2022.2.233
summary	This project proposes a physical form finding design method by generating concrete flexible formwork through digital algorithm, which aims to explore the potential formal correlation between real material as the medium of transmitting information in physical space and virtual data, so as to discuss the autonomy and intelligence of material under the support of digital design technology. The first part of this paper first discusses the current situation of the application and development of concrete materials in the field of digital construction in recent years, and then studies the adaptability of flexible formwork to the flowable characteristics of concrete materials; Then, the second part puts forward the moulding method of concrete physical shape finding through flexible and rigid composite formwork, and tries to explore the influence of formwork shape under the control of digital algorithm on this process; The third part of the paper records the process of concrete moulding experiment under this method to discuss the internal relationship between the physical form of concrete and combined formwork.
keywords	Physical Form Finding, Textile Concrete Formwork, Material Attributes, Concrete Fabrication, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ascaad2022_063
id	ascaad2022_063
authors	Ozman, Gizem; Selcuk, Semra
year	2022
title	Generating Mass Housing Plans through GANs: A case in TOKI, Turkey
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. 17-29
summary	Nowadays, Machine Learning (ML) is frequently used in almost all disciplines having an intersection with technology. Recently, architects are using existing plan data sets in architecture through Deep Learning (DL) algorithms of big data to achieve generative and non-existent plan models by using ML. Especially, Generative Adversarial Neural Networks (GANs), one of the deep learning algorithms, have been in use in the creation of generative models for architectural studies. Within the scope of this paper, architectural drawings were generated by using GANs. This generation method allows for the training of spatial layout planning to networks and for the generation of plans that do not exist in the dataset. Architectural drawings of TOKI (Housing Development Administration of the Republic of Türkiye) mass housing projects were used as datasets. In line with studies already carried out, this study attempts to create a method for further processing of the research. In this study, the differences between the plan typologies generated with raster images and the reality relations in visual productions between graph-based plan layout productions were evaluated. In this context, 157 plan datasets were obtained by multiplying plans which were spatially correlated with the RGB settings of 21 plan typologies. As a result of this research, it has been determined that the spatial layout planning of the HouseGAN algorithm provides TOK?'s current plan typologies of generation together with bubble diagrams. HouseGAN was trained using its dataset and the outputs obtained were realistic background images.
series	ASCAAD
email
last changed	2024/02/16 13:29

_id	ecaade2022_218
id	ecaade2022_218
authors	Bank, Mathias, Sandor, Viktoria, Schinegger, Kristina and Rutzinger, Stefan
year	2022
title	Learning Spatiality - A GAN method for designing architectural models through labelled sections
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. 611–619
doi	https://doi.org/10.52842/conf.ecaade.2022.2.611
summary	Digital design processes are increasingly being explored through the use of 2D generative adversarial networks (GAN), due to their capability for assembling latent spaces from existing data. These infinite spaces of synthetic data have the potential to enhance architectural design processes by mapping adjacencies across multidimensional properties, giving new impulses for design. The paper outlines a teaching method that applies 2D GANs to explore spatial characteristics with architectural students based on a training data set of 3D models of material-labelled houses. To introduce a common interface between human and neural networks, the method uses vertical slices through the models as the primary medium for communication. The approach is tested in the framework of a design course.
keywords	AI, Architectural Design, Materiality, GAN, 3D, Form Finding
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ascaad2022_047
id	ascaad2022_047
authors	Tu, Han; Yang, Chunfeng
year	2022
title	Mindful Space in Sentences: A Dataset of Virtual Emotions for Natural Language Classification
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. 713-730
summary	Spatial emotions have played a critical role in visual-spatial environmental assessment, which can be assessed using bio-sensors and language description. However, information on virtual spatial emotion assessment with objective emotion labels and natural language processing (NLP) is insufficient in literature. Thus, designers’ ability to assess spatial design quantitatively and cost effectively is limited before the design is finalized. This research measures the emotions expressed using electroencephalograms (EEGs) and descriptions in virtual reality (VR) spaces with different parameters. First, 26 subjects experienced 10 designed virtual spaces with a VR headset (Quest 2 device) corresponding to the different space parameters of shape, height, width, and length. Simultaneously, the EEG measured the emotions of the subjects using four electrodes and the five brain waves. Second, two labels – calm and active – were produced using EEGs to describe these virtual reality spaces. Last, this labeled emotion dataset compared the differences among the virtual spaces, human feelings, and the language description of the participants in the VR spatial experience. Experimental results show that the parameter changes of VR spaces can arouse significant fluctuations in the five brain waves. The EEG brain wave signals, in turn, can label the virtual rooms with calm and active emotions. Specifically, in terms of VR spaces and emotions, the experiments find that more relative spatial height results in less active emotions, while round spaces arouse calmness in the human brain waves. Moreover, the precise connection among VR spaces, brain waves in emotion, and languages still needs further research. This research attempts to offer a useful emotion measurement tool in virtual architectural design and description using EEGs. This research identifies potentials for future applications combining physiological metrics and AI methods, i.e., machine learning for synthetic design generation and evaluation.
series	ASCAAD
email
last changed	2024/02/16 13:29

_id	ecaade2022_249
id	ecaade2022_249
authors	Carrasco Hortal, Jose, Hernandez Carretero, Sergi, Abellan Alarcon, Antonio and Bermejo Pascual, Jorge
year	2022
title	Algae, Gobiidae Fish and Insects that inspire Coastal Custodian Entities - Digital models for a real-virtual space using TouchDesigner
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. 361–370
doi	https://doi.org/10.52842/conf.ecaade.2022.1.361
summary	At the beginning of the twenty-first century, a discipline at the intersection of digital art and science explores how natural and artificial species are affected, coexist, and feed back to humans based on multi-scalar hybrid models. They embody types of surveillance entities or non-human custodians, and serve as inspiration for another generation of designs produced ten years later, the case studies that are presented here. This paper explains the design and parametric fundamentals of a digital architecture installation at the University of Alicante (Spain 2021) using CNC models and the TouchDesigner programming environment. The installation contains a clan of technological-virtual hybrid species, non-human custodians, which: (a) strengthen the Proposal’s discourse on the recognition of legal identity of the Mar Menor lagoon (Southeast Spain); (b) incorporate reactive designs; (c) help raise awareness of the effect of human actions on the lagoon’s ecology and nearby streams. The viewpoint is not anthropocentric, because it adopts the perspective of the foraging fish species or the oxygen-seeking algae species, among others, in order to reveal the deterioration processes. In most cases, the result is a sort of synaesthetic conversation that interweaves light, sound, movement and data.
keywords	Human-Machine Interaction, TouchDesigner, Non-Human Custodian, Responsive Interface, Ethnography of Things
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_220
id	caadria2022_220
authors	Hsiao, Chi-Fu, Lee, Ching-Han, Chen, Chun-Yen, Fang, Yu-Cyuan and Chang, Teng-Wen
year	2022
title	Training a Vision-Based Autonomous Robot From Material Bending Analysis to Deformation Variables Predictions With an XR Approach
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. 201-210
doi	https://doi.org/10.52842/conf.caadria.2022.2.201
summary	This paper proposes a "Human Aided Hand-Eye System (HAHES)" to aid the autonomous robot for "Digital Twin Model (DTM)" sampling and correction. HAHES combining the eye-to hand and eye-in hand relationship to build an online DTM datasets. Users can download data and inspect DTM by "Human Wearable XR Device (HWD)", then continuous updating DTM by back testing the probing depth, and the overlap between physics and virtual. This paper focus on flexible linear material as experiment subject, then compares several data augmentation approaches: from 2D OpenCV homogeneous transformation, autonomous robot arm nodes depth probes, to overlap judgement by HWD. Then we train an additive regression model with back-testing DTM datasets and use the gradient boosting algorithm to inference an approximate 3D coordinate datasets with 2D OpenCV datasets to shorten the elapsed time. After all, this paper proposes a flexible mechanism to train a vision-based autonomous robot by combing different hand-eye relationship, HWD posture, and DTM in a recursive workflow for further researchers.
keywords	Digital Twin Model, Hand-Eye Relationship, Human Wearable XR Device, Homogeneous Transformation, Gradient Boosting, SDG 4, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_424
id	caadria2022_424
authors	May, Kieran, Walsh, James, Smith, Ross, Gu, Ning and Thomas, Bruce
year	2022
title	UnityRev - Bridging the gap between BIM Authoring platforms and Game Engines by creating a Real-Time Bi-directional Exchange of BIM data
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. 527-536
doi	https://doi.org/10.52842/conf.caadria.2022.2.527
summary	We present UnityRev: An open-source software package that enables a workflow designed to facilitate a real-time bi-directional and synchronous exchange of Building Information Modelling (BIM) data, by creating a direct link between a BIM authoring platform (i.e. Autodesk Revit) and a game engine (i.e. Unity 3D). Although previous works have explored the integration of BIM with game engines, the currently available tools are limited to a non-synchronous or uni-directional exchange of BIM data, and they do not address specific design issues required to make a BIM authoring platform and game engine compatible (i.e. parametric modelling). This paper describes our software which consists of a compact overview of the system, including design decisions, implementation details, and system capabilities. Two example applications are presented as concept demonstrators to -10795864108000showcase practical collaborative use-case scenarios between BIM authoring platforms and game engines which were not previously achievable without a real-time bi-directional workflow. This work will expand future Computer Aided Architectural Design (CAAD) research, and more specifically, Virtual Reality (VR)/Augmented Reality (AR) based BIM development and integration, by providing new possibilities and bridging the gap between BIM authoring platforms and game engines. The application of the system as demonstrated in the paper for real-time lighting performance simulation contributes to achieving the UN Sustainable Development Goal 11: Sustainable Cities and Communities.
keywords	building information modelling, game engines, revit, unity, virtual reality, augmented reality, lighting performance simulation, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ascaad2022_110
id	ascaad2022_110
authors	Salem, Mona; Moussa, Ramy
year	2022
title	A Hybrid Approach Based on Building Physics and Machine Learning for Thermal Comfort Prediction in Smart Buildings
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. 253-263
summary	One of the most important challenges facing the world is the application of modern technology in order to create smart buildings that achieve sustainable development goals (SDGs). Thermal comfort and reduction of energy consumption in buildings are considered important factors which, in turn, are reflected in creating a healthy environment and improving human productivity. Internet of Things (IoT) provides an ideal solution for collecting real-time data on the factors affecting indoor thermal comfort and energy consumption. However, comfort level is subjective and depends on many factors, which may not be learned by conventional models, an integrated model depending on thermal comfort factors is needed. In this work, a hybrid physics-based model incorporated with machine learning techniques is used for the prediction of thermal comfort inside buildings. XGBoost (eXtreme Gradient Boost) algorithm method was used due to its abilities to handle complex problems. A calculated dataset was extracted from the physics-based model gathered with the environmental variables data such as humidity, moisture, temperature, and air velocity collected from IoT devices. The results show an improvement in the prediction of the thermal comfort approach as compared with the conventional models. The XGBoost algorithm can exhibit an effective solution for eliminating deficiencies of traditional models and can be used when designing smart buildings, simulating, and evaluating the designed buildings, controlling energy consumption, and achieving thermal comfort.
series	ASCAAD
email
last changed	2024/02/16 13:38

_id	cdrf2022_187
id	cdrf2022_187
authors	Yunqin Li, Nobuyoshi Yabuki, and Tomohiro Fukuda
year	2022
title	A Virtual Reality-Based Tool with Human Behavior Measurement and Analysis for Feedback Design of the Indoor Light Environment
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_16
summary	Human behavior data provides essential feedback information for architects to improve a human-centered indoor light environment design. However, architects have difficulty capturing the complex, multidimensional, and unpredictable behavior of humans, often struggle to get users’ feedback on time in the schematic phase. This paper proposes a new virtual reality-based behavioral measurement and assessment tool that quantitatively collects and analyzes individual behavioral data, including travel trajectory, travel time, and gaze points, to reveal user experience and interaction of light, aiming to better help architects get timely feedback from users and create human-centered indoor light environment designs in the scheme optimization phase. To showcase this tool, we utilize an exhibition hall of a museum design as an illustrative example. The experiment demonstrates the feasibility of the proposed tool, and its results suggest that different lighting schemes influence human behavior patterns and that the introduction of natural light usually stimulates more movement. The developed virtual reality tool prototype provides valuable visual information and statistics for analyzing human behavior and evaluating indoor light environment design schemes.
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	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	ijac202220308
id	ijac202220308
authors	Rodrigues, Ricardo C; Rovenir B Duarte
year	2022
title	Generating floor plans with deep learning: A cross-validation assessment over different dataset sizes
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 3, pp. 630–644
summary	The advent of deep learning has enabled a series of opportunities; one of them is the ability to tackle subjective factors on the floor plan design and make predictions though spatial semantic maps. Nonetheless, the amount available of data grows exponentially on a daily basis, in this sense, this research seeks to investigate deep generative methods of floor plan design and its relationship between data volume, with training time, quality and diversity in the outputs; in other words, what is the amount of data required to rapidly train models that return optimal results. In our research, we used a variation of the Conditional Generative Adversarial Network algorithm, that is, Pix2pix, and a dataset of approximately 80 thousand images to train 10 models and evaluate their performance through a series of computational metrics. The results show that the potential of this data-driven method depends not only on the diversity of the training set but also on the linearity of the distribution; therefore, high-dimensional datasets did not achieve good results. It is also concluded that models trained on small sets of data (800 images) may return excellent results if given the correct training instructions (Hyperparameters), but the best baseline to this generative task is in the mid-term, using around 20 to 30 thousand images with a linear distribution. Finally, it is presented standard guidelines for dataset design, and the impact of data curation along the entire process
keywords	Dataset Reduction, Pix2pix, Artificial Intelligence, Deep Generative Models, GANs
series	journal
last changed	2024/04/17 14:30

_id	ascaad2022_099
id	ascaad2022_099
authors	Sencan, Inanc
year	2022
title	Progeny: A Grasshopper Plug-in that Augments Cellular Automata Algorithms for 3D Form Explorations
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. 377-391
summary	Cellular automata (CA) is a well-known computation method introduced by John von Neumann and Stanislaw Ulam in the 1940s. Since then, it has been studied in various fields such as computer science, biology, physics, chemistry, and art. The Classic CA algorithm is a calculation of a grid of cells' binary states based on neighboring cells and a set of rules. With the variation of these parameters, the CA algorithm has evolved into alternative versions such as 3D CA, Multiple neighborhood CA, Multiple rules CA, and Stochastic CA (Url-1). As a rule-based generative algorithm, CA has been used as a bottom-up design approach in the architectural design process in the search for form (Frazer,1995; Dinçer et al., 2014), in simulating the displacement of individuals in space, and in revealing complex relations at the urban scale (Güzelci, 2013). There are implementations of CA tools in 3D design software for designers as additional scripts or plug-ins. However, these often have limited ability to create customized CA algorithms by the designer. This study aims to create a customizable framework for 3D CA algorithms to be used in 3D form explorations by designers. Grasshopper3D, which is a visual scripting environment in Rhinoceros 3D, is used to implement the framework. The main difference between this work and the current Grasshopper3D plug-ins for CA simulation is the customizability and the real-time control of the framework. The parameters that allow the CA algorithm to be customized are; the initial state of the 3D grid, neighborhood conditions, cell states and rules. CA algorithms are created for each customizable parameter using the framework. Those algorithms are evaluated based on the ability to generate form. A voxel-based approach is used to generate geometry from the points created by the 3D cellular automata. In future, forms generated using this framework can be used as a form generating tool for digital environments.
series	ASCAAD
email
last changed	2024/02/16 13:38

_id	cdrf2022_453
id	cdrf2022_453
authors	Si-Yuan Rylan Wang
year	2022
title	Soft Pneumatic Robotic Architectural System: Prefabricated Inflatable Module-Based Cybernetic Adaptive Space Model Manipulated Through Human-System Interaction
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_39
summary	In this paper, a cybernetic adaptive space model based on prefabricated inflatable modules and physical interaction manipulation is introduced. The research aimed to redefine an intelligent and organic trend of residing and working by providing an adjustable and performative space system. The conjunction of human-space interaction, as well as the soft and hard architectural elements adaptive to dynamic living modalities and environmental conditions, are included in the methodology. The datasets based on the human body posture are collected through IMU sensors to provide coding inputs for defining modular inflatable structures, which anticipate generating heterogeneous morphological variations apt for flexible scenarios. The elaborated pre-fabricated samples successfully conform to the expected inflating behavior through silicone patterns. The results demonstrated the possibility of future architecture as an unrestrained configuration. Integrating the shape-shifting space within modular manufacturing and interactive technology can deprive the performance of many constraints. It can render a responsive ecosystem through a behavioral transformation of the in-habitants.
series	cdrf
email
last changed	2024/05/29 14:03

_id	ecaade2022_64
id	ecaade2022_64
authors	Sopher, Hadas and Dorta, Tomás
year	2022
title	Using Social VR System in Multidisciplinary Codesign
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. 547–556
doi	https://doi.org/10.52842/conf.ecaade.2022.1.547
summary	Social VR (SVR) systems are potentially adequate to support remote collaboration by allowing multidisciplinary students to codesign through an immersive shared display and 3D sketching. These characteristics become substantial for Multidisciplinary Codesign (MC) courses with the appreciation of the skills gained as knowledge is co-constructed. In codesign, participants ideate and develop together a design solution through verbal exchanges and design representations, relying on each participant’s expertise. Considering that non-design students lack design skills, design progress becomes highly challenging. Research focusing on how SVRs support MC is limited, what hinders integrating SVRs in these courses. Aiming to demonstrate how SVRs are used in MC courses, we monitored MC sessions involving three universities, from Industrial design, Ergonomics and Engineering. Data include three sessions of three remote multidisciplinary teams using three interconnected SVRs and three sessions involving collocated Industrial design students using a single SVR. The verbal and representational activities generated during the sessions were analysed, accounting for elements of collaborative ideation. Results showed a dominance of Industrial design students in generating representations and collaborative ideation. A rise in 3D representations in advanced MC sessions indicates the SVRs’ role in the process, understandings that enable the integration of SVRs in inter-university collaborations.
keywords	Social VR, Multidisciplinary Codesign, Codesign Learning, Design Conversations, 3D Sketching, Immersive Learning Environments
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_409
id	ecaade2022_409
authors	Sviták, Daniel, Tsikoliya, Shota and Vaško, Imro
year	2022
title	Multimateriality as a Driver of Additive Robotic Fabrication - Agent system used for toolpath generator
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. 151–156
doi	https://doi.org/10.52842/conf.ecaade.2022.2.151
summary	Designing for robotic 3D printing shows many challenges. This project speculates about the possibilities of material, and specifically multi-materiality, to be a design driver of the printing process. Second driver of the design is a bottom-up process of generating the fabrication data. A generalized agent system can act as a procedural generator of fabrication data, utilizing its digital awareness of data around its path. With this approach a smaller scale fabrication prototype was analysed, prepared for fabrication and robotically printed.
keywords	Multimateriality, Robotic Fabrication, Additive Deposition, Particle System, Large-Scale Printing
series	eCAADe
email
last changed	2024/04/22 07:10

_id	sigradi2022_187
id	sigradi2022_187
authors	Andia, Alfredo
year	2022
title	SynBio-Design: Building new infrastructures and territories with Synthetic Biology.
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. 1213–1224
summary	Which kind of imagination do we need for the future of our planet? In the past 150 years, we have completely transformed our biosphere. Today we have arrived at points of no return in global warming! The temperature of the Arctic Ocean will increase by 3-5°C by mid-century. This will lead to disastrous ocean acidification, sea-level rise, and worst of all the thawing of the permafrost that will release 1 trillion tons of carbon dioxide into the atmosphere. In this paper, we argue that building with biology will be the most important force to transform our planet. Since 2006, Synthetic Biology (SynBio) has surfaced as the fastest-growing technology in human history. SynBio involves emerging techniques that allow us to design, edit, and engineer all kinds of living organisms. In this paper, we elaborate on its potential development in growing infrastructures and its impacts on architectural thinking.
keywords	Bio-Inspired Design, Synthetic Biology, Bio-Architecture, Climate Change, Biotechnology
series	SIGraDi
email
last changed	2023/05/16 16:57

_id	sigradi2022_246
id	sigradi2022_246
authors	Bustos Lopez, Gabriela; Aguirre, Erwin
year	2022
title	Walking the Line: UX-XR Design Experiment for Ephemeral Installations in Pandemic Times
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. 699–710
summary	Throughout COVID 19 Pandemic since 2020, it was necessary to generate instructional strategies including digital platforms for creative processes in architecture. This article exposes an experience that integrates pedagogical, operational, and technical dimensions in architecture virtual teaching. A pedagogical methodology was designed and implemented, fusing User Experience (UX) and Extended Reality (XR) during the architectural design process in a virtual experimental studio. The use of UX-XR as a designing-reviewing strategy in architecture, positively impacted the creative experience of both students and reviewers by enriching the perception of the space and interactively simulating the user experience. A friendly, fun, and socially inclusive environment was generated for learning architecture using synthetic media and Multiuser Virtual Environments (MUVEs). The successful results of the students’ projects by phase are shown, revealing the significance of combining UX and XR, incorporating the metaverse as a canvas to review, recreate, interact, and assess architectural designs.
keywords	User Experience (UX), Extended Reality (XR), Multiuser Virtual Environments (MUVE), Virtual Campus, Usability
series	SIGraDi
email
last changed	2023/05/16 16:56

_id	caadria2022_152
id	caadria2022_152
authors	Deshpande, Rutvik, Nisztuk, Maciej, Cheng, Cesar, Subramanian, Ramanathan, Chavan, Tejas, Weijenberg, Camiel and Patel, Sayjel Vijay
year	2022
title	Synthetic Machine Learning for Real-time Architectural Daylighting Prediction
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. 313-322
doi	https://doi.org/10.52842/conf.caadria.2022.1.313
summary	"Synthetic Machine Learning‚ offers a revolutionary leap in real-time environmental analysis for conceptual architectural design. By integrating automatic synthetic data generation, artificial neural network (ANN) training and online deployment, Synthetic Machine Learning offers two main advantages over conventional simulation; First, it reduces the analysis time for a reference simulation from minutes to seconds; Second, it is possible to deploy ANN as a web service in an online design environment, which therein increases accessibility, significantly reducing simulation costs and setup time. The application of Synthetic Machine Learning to perform Daylight Autonomy (DA) and Spatial Daylight Autonomy (sDA) studies to maximise building daylighting for a given use, window to wall ratio, and floorplan arrangement is showcased through a preliminary demonstration work. Comparatively the use of algorithmically generated synthetic data versus real-world data is becoming ubiquitous in other disciplines, the advantages of this approach to the building design process are further discussed.
keywords	Daylight Autonomy, machine learning, building energy performance, synthetic data-sets, SDG 7, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

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