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	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	ecaade2022_378
id	ecaade2022_378
authors	Dokonal, Wolfgang, Mosler, Pascal, Gehring, Maximilian and Rüppel, Uwe
year	2022
title	On the Road towards? - Developing a toolset for a low-cost VR-enhanced design approach
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. 163–169
doi	https://doi.org/10.52842/conf.ecaade.2022.1.163
summary	For several years, we have been experimenting with Head Mounted Displays (HMD) being used as Virtual Reality (VR) interfaces. We tried to develop easy to use workflows for these devices so that they can be integrated into the architectural design process. Additionally, we were able to upgrade those systems with sensor boxes and designed new systems for movement control, collision detection, and additional effects for an increased feeling of immersion. Our systems focused on the use of the ultra-low-cost HMD devices and the intention was to clarify how much benefit within the design process we can achieve already at an early design phase in using this workflow without having extremely detailed models available. We experienced with our students in the past that the change from analogue design methods towards software-supported design reduced their understanding of space and scale and was therefore a negative factor in the design process. In this paper, we will focus on scripts for the game engine Unity with new functionalities that we tested with the students in two workshops.
keywords	Sense of Space, Virtual Reality, Unity Toolset
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_366
id	ecaade2022_366
authors	Geropanta, Vasiliki, Karagianni, Anna, Parthenios, Panagiotis, Ampatzoglou, Triantafyllos, Fatouros, Loukas, Simantiraki, Vasiliki, Brokos-Melissaratos, Orestis and Eleftheriadis, Dimitris
year	2022
title	Digitalization of Participatory Greening - The case of UnionYouth in Chania
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. 469–478
doi	https://doi.org/10.52842/conf.ecaade.2022.1.469
summary	The contemporary climate crisis pushed communities of actors, cities and citizens to use smart technology, digital platforms, and data-based intelligence to steer creative solutions for greening in their urban ecosystems. This phenomenon brought about an increasing imperative for citizen participation and inclusion, in the co-design of green infrastructures, suggesting alternative ways to deal with the lack or misuse of public space. In this framework, this paper analyzes the case of ''UnionYouth in Chania'', a project that aims a) to build an environmental awareness strategy for Generation Z, b) to promote capacity-building processes related to climate change and environmental protection, c) actually transform the city public space through participatory processes. Specifically, the project describes the creation of a digital platform and a mobile app consisting of several engagement tools that allow interaction between the digital community of youth, the city's decision-makers, and city greening actors. Therefore, the first part of the paper talks about the necessity of promoting today's participatory processes in the city for climate change mitigation through a literature review that emerged in the last decade. The second part of the paper examines a case study, namely UnionYouth in Chania, a digital collaborative platform that promotes methods for greening the city through district-based, activity-based, and network-based redesign solutions. The third part of the paper brings about interesting reflections on the relationship between the analog and digital world, and how bottom-up processes may be an important tool in city planning. The overall scope of the analysis of the specific case study is to bring insights into the architectural world, as a means to create more bridges with citizens and communities and contribute to their greening understanding.
keywords	Climate Change, Generation Z, Green Infrastructure, Raise Awareness, Mobile Application, Participatory Design, Smart City
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_82
id	caadria2022_82
authors	Globa, Anastasia, Reinhardt, Dagmar, Keane, Adrienne and Davies, Peter
year	2022
title	Building Resilience - Using Parametric Modelling and Game Engines to Simulate the Impacts of Secondary Structures in Bushfire Events
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. 749-758
doi	https://doi.org/10.52842/conf.caadria.2022.2.749
summary	Bushfires are a global phenomenon, closely connected to climate change and safety, resilience and sustainability of cities and human settlements. Government agencies, architects and researchers across institutions are committed to improving Australia‚s resilience to bushfires yet grappling with ways to further mitigate risks. ‚Build back better‚ is the often-used phrase to support bushfire resilience, yet there remains a limited understanding of how secondary structures, such as storage sheds, garages, and fences contribute to or mitigate fire loss. These secondary structures are integral to properties yet fall, largely, outside land use planning approval processes and other regulations. Computational modelling can be adapted to deliver visualisations that increase awareness. We developed several simulation approaches which addressed distances, relationship to and the construction materials of secondary structures, terrain slopes and environmental forces. We conclude that gaming engines may offer the optimal immersive opportunity for residents and others to visualise fire risks related to secondary structures to increase awareness and improve bushfire readiness behaviours.
keywords	bushfire, auxiliary structures, game engine, visualisation modelling, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2024_186
id	caadria2024_186
authors	Huang, Jingfei and Tu, Han
year	2024
title	Inconsistent Affective Reaction: Sentiment of Perception and Opinion in Urban Environments
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 395–404
doi	https://doi.org/10.52842/conf.caadria.2024.2.395
summary	The ascension of social media platforms has transformed our understanding of urban environments, giving rise to nuanced variations in sentiment reaction embedded within human perception and opinion, and challenging existing multidimensional sentiment analysis approaches in urban studies. This study presents novel methodologies for identifying and elucidating sentiment inconsistency, constructing a dataset encompassing 140,750 Baidu and Tencent Street view images to measure perceptions, and 984,024 Weibo social media text posts to measure opinions. A reaction index is developed, integrating object detection and natural language processing techniques to classify sentiment in Beijing Second Ring for 2016 and 2022. Classified sentiment reaction is analysed and visualized using regression analysis, image segmentation, and word frequency based on land-use distribution to discern underlying factors. The perception affective reaction trend map reveals a shift toward more evenly distributed positive sentiment, while the opinion affective reaction trend map shows more extreme changes. Our mismatch map indicates significant disparities between the sentiments of human perception and opinion of urban areas over the years. Changes in sentiment reactions have significant relationships with elements such as dense buildings and pedestrian presence. Our inconsistent maps present perception and opinion sentiments before and after the pandemic and offer potential explanations and directions for environmental management, in formulating strategies for urban renewal.
keywords	Urban Sentiment, Affective Reaction, Social Media, Machine Learning, Urban Data, Image Segmentation.
series	CAADRIA
email
last changed	2024/11/17 22:05

_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	sigradi2022_253
id	sigradi2022_253
authors	Sanatani, Rohit Priyadarshi; Nagakura, Takehiko; Tsai, Daniel
year	2022
title	The Tourist’s Image of the City: A comparative analysis of visual features and textual themes of interest across three metropolises
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. 89–100
summary	Tourist attractions play a major role in shaping ‘mental images’ of cities. The growing availability of urban big-data in recent years has opened up novel lines of inquiry into the nuances of urban imageability and sentiment. Drawing upon crowdsourced hybrid data in the form of both textual descriptions as well as photographs for 750 tourist attractions across Boston, Singapore and Sydney, this work compares the predominant themes of discussion and visual features of interest that shape tourist sentiment towards these cities. The study collects over 3500 user reviews and uses Latent Dirichlet Allocation (LDA) for the extraction of high-level topics of discussion. Object detection is also run on over 6000 photographs, and unsupervised clustering is carried out on extracted features to identify clusters of visual elements which capture tourist attention. The findings reinforce the popular identity of Boston as a city steeped in history, while strong perceptions of nature and greenery emerge from Singapore. Tourist interest in Sydney is dominated by specific anchors such as the Sydney Harbor Bridge.
keywords	Data Analytics, Urban Tourism, Topic Modeling, Sentiment Analysis, Unsupervised Clustering, Big Data
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	cdrf2022_274
id	cdrf2022_274
authors	Zhiyong Dong and Jinru Lin
year	2022
title	Nolli Map: Interpretation of Urban Morphology Based on Machine Learning
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_24
summary	Nolli map is the earliest diagram tool to simplify and quantify urban form, which most intuitively reflects the spatial layout of tangible elements in the city. The urban morphology contains its inherent evolutionary laws. Exploring the inner rules of cities is helpful for people to conduct urban research and design. Unlike the traditional research methods of urban morphology, the neural network algorithm provides us with new ideas for understanding urban morphology. In this experiment, we label 136 European cities samples in the rules of Nolli map as a training set for machine learning. We use Generative Adversarial Networks (GAN) for multiple mapping experiments. The generated images present recognizable and plausible images of the urban fabric. The results show that the machine can learn the inherent laws of complex urban fabrics, which expands a new applied method for the study of urban morphology.
series	cdrf
email
last changed	2024/05/29 14:02

_id	ascaad2022_037
id	ascaad2022_037
authors	Affara, Lama; Nakhal, Bilal
year	2022
title	Computer Vision Aided Hotspot Creation in Virtual Environments
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. 61-73
summary	Hotspot creation is one of the most important modules within virtual environments which helps show the navigators of these environments some information about semantic elements within it and facilitate the navigation between the virtual spaces. In this paper, a system for automatic hotspot proposals and creation in virtual environments is proposed. The system uses computer vision modules to automatically propose hotspot locations in addition to identifying and creating these hotspots with candidate labels. Two main modules used in the system are object detection and scene segmentation. The scene segmentation helps give candidate hotspot areas and provides an overall understanding of the semantics of the virtual environment. The object detection module also uses pretrained deep networks for automatic hotspot creation over these objects. The system helps speed up the hotspot creation process and offers a tool for virtual environment users and creators.
series	ASCAAD
email
last changed	2024/02/16 13:24

_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	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

_id	ecaade2022_16
id	ecaade2022_16
authors	Bailey, Grayson, Kammler, Olaf, Weiser, Rene, Fuchkina, Ekaterina and Schneider, Sven
year	2022
title	Performing Immersive Virtual Environment User Studies with VREVAL
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. 437–446
doi	https://doi.org/10.52842/conf.ecaade.2022.2.437
summary	The new construction that is projected to take place between 2020 and 2040 plays a critical role in embodied carbon emissions. The change in material selection is inversely proportional to the budget as the project progresses. Given the fact that early-stage design processes often do not include environmental performance metrics, there is an opportunity to investigate a toolset that enables early-stage design processes to integrate this type of analysis into the preferred workflow of concept designers. The value here is that early-stage environmental feedback can inform the crucial decisions that are made in the beginning, giving a greater chance for a building with better environmental performance in terms of its life cycle. This paper presents the development of a tool called LearnCarbon, as a plugin of Rhino3d, used to educate architects and engineers in the early stages about the environmental impact of their design. It facilitates two neural networks trained with the Embodied Carbon Benchmark Study by Carbon Leadership Forum, which learns the relationship between building geometry, typology, and construction type with the Global Warming potential (GWP) in tons of C02 equivalent (tCO2e). The first one, a regression model, can predict the GWP based on the massing model of a building, along with information about typology and location. The second one, a classification model, predicts the construction type given a massing model and target GWP. LearnCarbon can help improve the building life cycle impact significantly through early predictions of the structure’s material and can be used as a tool for facilitating sustainable discussions between the architect and the client.
keywords	Pre-Occupancy Evaluation, Immersive Virtual Environment, Wayfinding, User Centered Design, Architectural Study Design
series	eCAADe
email
last changed	2024/04/22 07:10

_id	sigradi2022_54
id	sigradi2022_54
authors	Balci, Ozan; Alaçam, Sema
year	2022
title	Zone-sensitive RIZOBots in Action: Examining the Behavior of Mobile Robots In a Heterogeneous Environment
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. 397–408
summary	This study proposes a framework for the use of mobile robots namely RIZOBots in form studies in the early phases of design. The proposed framework was tested in two experiments. An agent-based model was utilized for the movement of mobile robots, a drawing task was defined as the task. In particular, rule sets for agent-agent and agent-environment interaction were used. Light-sensitivity rules were utilized to achieve agent-environment interaction, apart from obstacle detection. This study focuses on the effects of two different zone-related states on the behavior of RIZOBot which is a configurable differential-drive wheeled robot developed by authors using off-the-shelf products and 3D printed body parts. Two zone types with very basic features are used to define environmental conditions. The traces left on the canvas, the irregularities in the movement of the robots, and the robot-environment interaction will be evaluated in the study. The results and analysis of the two selected experiments are presented and the potential of the proposed framework is discussed.
keywords	Robotics, Swarm robotics, Swarm behaviour, Mobile agents, Zone-sensitivity
series	SIGraDi
email
last changed	2023/05/16 16:56

_id	caadria2022_59
id	caadria2022_59
authors	Banihashemi, Farzan, Reitberger, Roland and Lang, Werner
year	2022
title	Investigating Urban Heat Island and Vegetation Effects Under the Influence of Climate Change in Early Design Stages
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. 679-688
doi	https://doi.org/10.52842/conf.caadria.2022.2.679
summary	Different criteria need to be considered for optimal strategies in the early design stages of urban developments. Under the influence of climate change, the urban heat island effect (UHI) is a phenomenon that gains importance in the early design stages. Here, different parameters, for instance, vegetation ratio in the city district and building density, play a significant role in the UHI effect. These parameters need to be quantified through different simulation tools for optimal climate adaptation and mitigation measures on the urban district scale. However, not all parameters and their influence are clear to the decision-makers and actors in the early design stages. Hence, we propose a Monte Carlo based sensitivity analysis (SA) and uncertainty analysis (UA) to show the significance of different parameters and quantify them. The SA aims to identify the major influencing parameters, whereas the UA quantifies the effect on the energy performance and indoor thermal comfort of occupants. The workflow is integrated into a collaborative design platform and applied in a case study to support decision-makers in the early design stages for new developments, densification, or refurbishment scenarios.
keywords	Monte Carlo Simulation, Sensitivity Analysis, Uncertainty Analysis, Building Energy Simulation, SDG 13, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_312
id	ecaade2022_312
authors	Bhagat, Puja and Gursoy, Benay
year	2022
title	Stretch – 3D Print – Release: Formal descriptions of shape-change in 3D printed shapes on stretched fabrics
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. 301–310
doi	https://doi.org/10.52842/conf.ecaade.2022.1.301
summary	Researchers have previously explored 3D printing 2D shapes on stretched fabrics using plastic filaments. When released, the 3D printed plastic constrains the fabric to take a 3D form. By leveraging the material properties and resultant tension between the rigid plastic and pliable fabric, it is possible to create 3D forms which would otherwise be difficult to construct with traditional fabrication techniques. Multiple factors are in play in this shape-change. Therefore, it is often difficult to anticipate the 3D form that will emerge when the stretched fabric is released. In this paper, we present our systematic bottom-up explorations on the effects of various parameters on shape-change and formalize our findings as rules. These rules help to visualize the interrelations between (abstract) shapes designed for 3D printing, (material) shapes 3D printed on stretched fabric, and (material) shapes that emerge when the fabric is released. The rules also help to explore design possibilities with this technique in a more controlled, communicable, and repeatable way. We also present a series of vaulted forms that we generated using these rules and by stretching - 3D printing - releasing the fabric.
keywords	Material Computing, Shape-change, Adaptive Architecture, Digital Fabrication, 3D Printing on Textiles
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_205
id	caadria2022_205
authors	Bielski, Jessica, Langenhan, Christoph, Ziegler, Christoph, Eisenstadt, Viktor, Dengel, Andreas and Althoff, Klaus-Dieter
year	2022
title	Quantifying the Intangible, A Tool for Retrospective Protocol Studies of Sketching During the Early Conceptual Design of Architecture
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. 403-411
doi	https://doi.org/10.52842/conf.caadria.2022.1.403
summary	Sketching is a craft supporting the development of ideas and design intentions, as well as an effective tool for communication during the early architectural design stages by making them tangible. Even though sketch-based interaction is a promising approach for Computer-Aided Architectural Design (CAAD) systems, it remains a challenge for computers to recognise information in a sketch. Design protocol studies conducted to deconstruct the sketch and sketching process collect solely qualitative data so far. However, the 'metis' projects aim to create an intelligent design assistant, using an artificial neural network (ANN), in the manner of Negroponte‚s Architecture Machine. By assimilating to the user's idiosyncrasies, the system suggests further design steps to the architect to improve the design decision making process for economic growth, qualitative self-education through the dialogue and reducing stress. For training such ANN quantitative data is needed. In order to produce quantifiable results from such a study, we propose our open-source web-tool ‚Sketch Protocol Analyser‚. By correlating different parameters (i.e. video, transcript and sketch built) through the same labels and their timestamps, we create quantitative data for further use.
keywords	Design Protocol Studies, Sketching, Data Collection, Architectural Design Process, ANN, SDG 3, SDG 4, SDG 8, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_125
id	ecaade2022_125
authors	Chen, Emily, Lu, Glenn, Barnik, Lyric and Correa, David
year	2022
title	Fast and Reversible Bistable Hygroscopic Actuators for Architectural Applications Based on Plant Movement Strategies
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. 261–270
doi	https://doi.org/10.52842/conf.ecaade.2022.1.261
summary	Plant movement is of great inspiration for the development of actuators in architectural applications. Since plants lack muscles, they have developed unique hygroscopic mechanisms that use specialized tissue to generate movement in response to stimuli such as touch, light, temperature, or gravity. Most research in architecture has been focused on the stress-induced bending that can be achieved with a bilayer structure – particularly using wood composites and bi-metals. The speed of these mechanisms is mostly limited by the rules of bilayers, as described by Timoshenko, and the speed of moisture/heat diffusion. This paper presents methods to use bistable mechanisms, and their elastic instability, to enable rapid movements of “snap-through” buckling that can greatly improve the speed of transformation. The research covers biomimetic studies on the Mimosa pudica, Oxalis triangularis, and the Maranta leuconeura to develop hygroscopic mechanisms whose kinematic actuation can be amplified through the integration of a bi- stable system. The presented mechanisms make it possible to significantly increase the speed of response of the hygroscopically driven mechanism while maintaining the ability to operate over several reversible cycles. Calibration of the mechanism to specific relative humidity conditions is presented together with some initial prototypes with the potential for manual override strategies. It is the aim of this combined approach that the actuation mechanisms are better able to match users’ expectations of fast shape-change actuation in relation to environmental changes.
keywords	Stimulus-Responsive, Biomimetics, Hygroscopic, Elastic Instability, Actuators
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_42
id	caadria2022_42
authors	Chen, Jielin and Stouffs, Rudi
year	2022
title	Robust Attributed Adjacency Graph Extraction Using Floor Plan Images
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
doi	https://doi.org/10.52842/conf.caadria.2022.2.385
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	cdrf2022_253
id	cdrf2022_253
authors	Chuheng Tan and Ximing Zhong
year	2022
title	A Rapid Wind Velocity Prediction Method in Built Environment Based on CycleGAN Model
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_22
summary	Although the wind microclimate and wind environment play important roles in urban prediction, the time-consuming and complicated setup and process of wind simulation are widely regarded as challenges. There are several methods to use deep learning (DL) models for wind speed prediction by labeling pairs of wind simulation dataset samples. However, many wind simulation experiments are needed to obtain paired datasets, which is still time-consuming and cumbersome. Compared with previous studies, we propose a method to train a DL model without labelling paired data, which is based on Cycle Generative Adversarial Network (cycleGAN). To verify our hypothesis, we evaluate the results and process of the pix2pix model (requires paired datasets) and cycleGAN (does not requires paired datasets), and explore the difference of results between these two DL models and professional CFD software. The result shows that cycleGAN can perform as well as pix2pix in accuracy, indicating that some random city plans image samples and random wind simulation samples can train surrogate models as accurate as labelled DL methods. Although the DL method has similar results to the professional CFD method, the details of the wind flow results still need improvement. This study can help designers and policymakers to make informed decisions to choose Dl methods for real-time wind speed prediction for early-stage design exploration.
series	cdrf
email
last changed	2024/05/29 14:02

_id	ijac202220307
id	ijac202220307
authors	Cicek, Selen; Gozde Damla Turhan
year	2022
title	Computational generation of a spatial layout through syntactical evaluation and multi-objective evolutionary optimization
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 3, pp. 610–629
summary	The space layout problem encompasses challenges that rely on a diverse range of contexts regarding urban planning and architectural design, during the traditional design phases which require immense effort and time for the evaluation of the spatial elements’ characteristic needs. In order to eliminate the burden of considering all multidimensional design aspects at the same time, this research presents a three-bodied computational method for locating the spaces of the given architectural design program in a project site, according to the defined list of design objectives and criteria. Besides the determination of the layout according to the requirements of the spatial elements, this research proposes an integration of the space syntax theory’s analytical compounds in terms of Justified Graph Analysis and Integration Values as the fitness criteria for the multi-objective evolutionary optimization in the computational model. To satisfy the integrity levels of each various characterized element within site organization, that are implied inherently by the architectural design program and generate a sustainable space network layout for the project site
keywords	computational space layout, space syntax, spatial organization, spatial network, evolutionary algorithms
series	journal
last changed	2024/04/17 14:30

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