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	caadria2021_001
id	caadria2021_001
authors	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.)
year	2021
title	CAADRIA 2021: Projections, Volume 2
doi	https://doi.org/10.52842/conf.caadria.2021.2
source	PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, 764 p.
summary	Rapidly evolving technologies are increasingly shaping our societies as well as our understanding of the discipline of architecture. Computational developments in fields such as machine learning and data mining enable the creation of learning networks that involve architects alongside algorithms in developing new understanding. Such networks are increasingly able to observe current social conditions, plan, decide, act on changing scenarios, learn from the consequences of their actions, and recognize patterns out of complex activity networks. While digital technologies have already enabled architecture to transcend static physical boxes, new challenges of the present and visions for the future continue to call for both innovative responses integrating emerging technologies into experimental architectural practice and their critical reflection. In this process, the capability of adapting to complex social and environmental challenges through learning, prototyping and verifying solution proposals in the context of rapidly shifting realities has become a core challenge to the architecture discipline. Supported by advancing technologies, architects and researchers are creating new frameworks for digital workflows that engage with new challenges in a variety of ways. Learning networks that recognize patterns from massive data, rapid prototyping systems that flexibly iterate innovative physical solutions, and adaptive design methods all contribute to a flexible and networked digital architecture that is able to learn from both past and present to evolve towards a promising vision of the future.
series	CAADRIA
last changed	2022/06/07 07:49

_id	caadria2021_000
id	caadria2021_000
authors	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.)
year	2021
title	CAADRIA 2021: Projections, Volume 1
doi	https://doi.org/10.52842/conf.caadria.2021.1
source	PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, 768 p.
summary	Rapidly evolving technologies are increasingly shaping our societies as well as our understanding of the discipline of architecture. Computational developments in fields such as machine learning and data mining enable the creation of learning networks that involve architects alongside algorithms in developing new understanding. Such networks are increasingly able to observe current social conditions, plan, decide, act on changing scenarios, learn from the consequences of their actions, and recognize patterns out of complex activity networks. While digital technologies have already enabled architecture to transcend static physical boxes, new challenges of the present and visions for the future continue to call for both innovative responses integrating emerging technologies into experimental architectural practice and their critical reflection. In this process, the capability of adapting to complex social and environmental challenges through learning, prototyping and verifying solution proposals in the context of rapidly shifting realities has become a core challenge to the architecture discipline. Supported by advancing technologies, architects and researchers are creating new frameworks for digital workflows that engage with new challenges in a variety of ways. Learning networks that recognize patterns from massive data, rapid prototyping systems that flexibly iterate innovative physical solutions, and adaptive design methods all contribute to a flexible and networked digital architecture that is able to learn from both past and present to evolve towards a promising vision of the future.
series	CAADRIA
last changed	2022/06/07 07:49

_id	ascaad2021_118
id	ascaad2021_118
authors	Abdelmohsen, Sherif; Passaint Massoud
year	2021
title	Material-Based Parametric Form Finding: Learning Parametric Design through Computational Making
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 521-535
summary	Most approaches developed to teach parametric design principles in architectural education have focused on universal strategies that often result in the fixation of students towards perceiving parametric design as standard blindly followed scripts and procedures, thus defying the purpose of the bottom-up framework of form finding. Material-based computation has been recently introduced in computational design, where parameters and rules related to material properties are integrated into algorithmic thinking. In this paper, we discuss the process and outcomes of a computational design course focused on the interplay between the physical and the digital. Two phases of physical/digital exploration are discussed: (1) physical exploration with different materials and fabrication techniques to arrive at the design logic of a prototype panel module, and (2) deducing and developing an understanding of rules and parameters, based on the interplay of materials, and deriving strategies for pattern propagation of the panel on a façade composition using variation and complexity. The process and outcomes confirmed the initial hypothesis, where the more explicit the material exploration and identification of physical rules and relationships, the more nuanced the parametrically driven process, where students expressed a clear goal oriented generative logic, in addition to utilizing parametric design to inform form finding as a bottom-up approach.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	caadria2021_006
id	caadria2021_006
authors	Agirachman, Fauzan Alfi and Shinozaki, Michihiko
year	2021
title	VRDR - An Attempt to Evaluate BIM-based Design Studio Outcome Through Virtual Reality
doi	https://doi.org/10.52842/conf.caadria.2021.2.223
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 223-232
summary	During the COVID-19 pandemic situation, educational institutions were forced to conduct all academic activities in distance learning formats, including the architecture program. This act barred interaction between students and supervisors only through their computers screen. Therefore, in this study, we explored an opportunity to utilize virtual reality (VR) technology to help students understand and evaluate design outcomes from an architectural design studio course in a virtual environment setting. The design evaluation process is focused on building affordance and user accessibility aspect based on the design objectives that students must achieve. As a result, we developed a game-engine based VR system called VRDR for evaluating design studio outcomes modeled as Building Information Modeling (BIM) models.
keywords	virtual reality; building information modeling; building affordance; user accessibility; architectural education
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	ascaad2021_071
id	ascaad2021_071
authors	Al Maani, Duaa; Saba Alnusairat, Amer Al-Jokhadar
year	2021
title	Transforming Learning for Architecture: Online Design Studio as New Norm for Crises Adaptation Under COVID-19
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 129-141
summary	For students, studying architecture necessitates a fundamental shift in learning mode and attitude in the transition from school. Beginner students are often surprised by the new mode of learning-by-doing and the new learner identity that they must adopt and adapt to in the design studio. Moreover, due to the COVID-19 pandemic, architecture teaching has moved online. Both instructors and students are experiencing dramatic changes in their modes of teaching and learning due to the sudden move from on-campus design studios to a virtual alternative, with only the bare minimum of resources and relevant experience. This study explored the virtual design studio as a transformative learning model for disaster and resilience context, including the factors that affect foundation students’ perceptions and experiences of the quality of this adaptation. Data obtained from 248 students who took online design studios during the lockdown in 15 universities in Jordan highlight many factors that make the experience of the online design studio more challenging. Despite these challenges, strongly positive aspects of the online studio were evident and widely discussed. A model of hyper-flexible design studio in which students can have a direct contact with their instructors when needed – in addition to online activities, reviews, and written feedback – is highly recommended for the beginner years. This HyFlex model will enrich students’ learning and understanding of the fundamentals of design and ensure that technology solutions deliver significant and sustainable benefits.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	ascaad2021_021
id	ascaad2021_021
authors	Albassel, Mohamed; Mustafa Waly
year	2021
title	Applying Machine Learning to Enhance the Implementation of Egyptian Fire and Life Safety Code in Mega Projects
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 7-22
summary	Machine Learning has become a significant research area in architecture; it can be used to retrieve valuable information for available data used to predict future instances. the purpose of this research was to develop an automated workflow to enhance the implementation of The Egyptian fire & life safety (FLS) code in mega projects and reduce the time wasted on the traditional process of rooms’ uses, occupant load, and egress capacity calculations to increase productivity by applying Supervised Machine Learning based on classification techniques through data mining and building datasets from previous projects, and explore the methods of preparation and analyzing data (text cleanup- tokenization- filtering- stemming-labeling). Then, provide an algorithm for classification rules using C# and python in integration with BIM tools such as Revit-Dynamo to calculate cumulative occupant load based on factors which are mentioned in the Egyptian FLS code, determine classification and uses of rooms to validate all data related to FLS. Moreover, calculating the egress capacity of means of egress for not only exit doors but also exit stairs. In addition, the research is to identify a clear understanding about ML and BIM through project case studies and how to build a model with the needed accuracy.
series	ASCAAD
email
last changed	2021/08/09 13:11

_id	caadria2021_399
id	caadria2021_399
authors	Alsalman, Osama, Erhan, Halil, Haas, Alyssa, Abuzuraiq, Ahmed M. and Zarei, Maryam
year	2021
title	Design Analytics and Data-Driven Collaboration in Evaluating Alternatives
doi	https://doi.org/10.52842/conf.caadria.2021.2.101
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 101-110
summary	Evaluation of design ideas is an important task throughout the life cycle of design development in the AEC industry. It involves multiple stakeholders with diverse backgrounds and interests. However, there is limited computational support which through this collaboration is facilitated, in particular for projects that are complex. Current systems are either highly specialized for designers or configured for a particular purpose or design workflow overlooking other stakeholders' needs. We present our approach to motivating participatory and collaborative design decision-making on alternative solutions as early as possible in the design process. The main principle motivating our approach is giving the stakeholders the control over customizing the data presentation interfaces. We introduce our prototype system D-ART as a collection of customizable web interfaces supporting design data form and performance presentation, feedback input, design solutions comparisons, and feedback compiling and presentation. Finally, we started the evaluation of these interfaces through an expert evaluation process which generally reported positive results. Although the results are not conclusive, they hint towards the need for presenting and compiling feedback back to the designers which will be the main point of our future work.
keywords	Design Analytics; Collaboration; Visualizations
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	ascaad2021_146
id	ascaad2021_146
authors	Aly, Zeyad; Aly Ibrahim, Sherif Abdelmohsen
year	2021
title	Augmenting Passive Actuation of Hygromorphic Skins in Desert Climates: Learning from Thorny Devil Lizard Skins
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 264-278
summary	The exploitation of latent properties of natural materials such as wood in the passive actuation of adaptive building skins is of growing interest due to their added value as a low-cost and low-energy approach. The control of wood response behavior is typically conducted via physical experiments and numerical simulations that explore the impact of hygroscopic design parameters. Desert climates however suffer from water scarcity and high temperatures. Complementary mechanisms are needed to provide sufficient sources of water for effective hygroscopic operation. This paper aims to exploit such mechanisms, with specific focus on thorny devil lizard skins whose microstructure surface properties allow for maximum humidity absorption. We put forward that this process enhances hygroscopic-based passive actuation systems and their adaptation to both humidity and temperature in desert climates. Specific parameters and rules are deduced based on the lizard skin properties. Physical experiments are conducted to observe different actuation mechanisms. These mechanisms are recorded, and texture and bending morphologies are modeled for adaptive skins using Grasshopper.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	acadia21_238
id	acadia21_238
authors	Anifowose, Hassan; Yan, Wei; Dixit, Manish
year	2021
title	BIM LOD + Virtual Reality
doi	https://doi.org/10.52842/conf.acadia.2021.238
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 238-245.
summary	Architectural Education faces limitations due to its tactile approach to learning in classrooms with only 2-D and 3-D tools. At a higher level, virtual reality provides a potential for delivering more information to individuals undergoing design learning. This paper investigates a hypothesis establishing grounds towards a new research in Building Information Modeling (BIM) and Virtual Reality (VR). The hypothesis is projected to determine best practices for content creation and tactile object virtual interaction, which potentially can improve learning in architectural & construction education with a less costly approach and ease of access to well-known buildings. We explored this hypothesis in a step-by-step game design demonstration in VR, by showcasing the exploration of the Farnsworth House and reproducing assemblage of the same with different game levels of difficulty which correspond with varying BIM levels of development (LODs). The game design prototype equally provides an entry way and learning style for users with or without a formal architectural or construction education seeking to understand design tectonics within diverse or cross-disciplinary study cases. This paper shows that developing geometric abstract concepts of design pedagogy, using varying LODs for game content and levels, while utilizing newly developed features such as snap-to-grid, snap-to-position and snap-to-angle to improve user engagement during assemblage may provide deeper learning objectives for architectural precedent study.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2021_203
id	ecaade2021_203
authors	Arora, Hardik, Bielski, Jessica, Eisenstadt, Viktor, Langenhan, Christoph, Ziegler, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2021
title	Consistency Checker - An automatic constraint-based evaluator for housing spatial configurations
doi	https://doi.org/10.52842/conf.ecaade.2021.2.351
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 2, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 351-358
summary	The gradual rise of artificial intelligence (AI) and its increasing visibility among many research disciplines affected Computer-Aided Architectural Design (CAAD). Architectural deep learning (DL) approaches are being developed and published on a regular basis, such as retrieval (Sharma et al. 2017) or design style manipulation (Newton 2019; Silvestre et al. 2016). However, there seems to be no method to evaluate highly constrained spatial configurations for specific architectural domains (such as housing or office buildings) based on basic architectural principles and everyday practices. This paper introduces an automatic constraint-based consistency checker to evaluate the coherency of semantic spatial configurations of housing construction using a small set of design principles to evaluate our DL approaches. The consistency checker informs about the overall performance of a spatial configuration followed by whether it is open/closed and the constraints it didn't satisfy. This paper deals with the relation of spaces processed as mathematically formalized graphs contrary to existing model checking software like Solibri.
keywords	model checking, building information modeling, deep learning, data quality
series	eCAADe
email
last changed	2022/06/07 07:54

_id	sigradi2023_270
id	sigradi2023_270
authors	Asevedo, Laíze, Monteiro, Verner, Medeiros, Deisyanne, Rodrigues, Fernanda, Moura, Marcone and Rocha, Thuany
year	2023
title	Parameterization and Gamification in Descriptive Geometry Learning: One Study, Two Scenarios
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 1047–1058
summary	Despite the complexity of parametric modeling, it is possible to apply it in educational context with a simpler approach. The COVID-19 pandemic increased the use of active methodologies in education. Gamification, particularly, should be emphasized regarding its association with parametric modeling. Post-pandemic scenario made possible the reinsertion of traditional practices, thus adding successful learning methods from online teaching. This paper aims to compare the adoption of parameterization and gamification to teach Descriptive Geometry on both teaching scenarios: online and presential. Two experiments were implemented to four Technical Drawing classes - A and B (2021), C and D (2022) -, in high school and technician level. The quantitative results addressed to the efficiency of parametric modeling as a didactic tool, and the qualitative results indicated that the students accepted the experiences of parameterization and gamification, on both scenarios. Nevertheless, there were subtle differences between the results from online and presential scenarios.
keywords	Online learning, presential learning, parameterization, gamification, descriptive geometry
series	SIGraDi
email
last changed	2024/03/08 14:08

_id	ascaad2021_142
id	ascaad2021_142
authors	Bakir, Ramy; Sara Alsaadani, Sherif Abdelmohsen
year	2021
title	Student Experiences of Online Design Education Post COVID-19: A Mixed Methods Study
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 142-155
summary	This paper presents findings of a survey conducted to assess students’ experiences within the online instruction stage of their architectural education during the lockdown period caused by the COVID-19 pandemic between March and June 2020. The study was conducted in two departments of architecture in both Cairo branches of the Arab Academy for Science, Technology & Maritime Transport (AASTMT), Egypt, with special focus on courses involving a CAAD component. The objective of this exploratory study was to understand students’ learning experiences within the online period, and to investigate challenges facing architectural education. A mixed methods study was used, where a questionnaire-based survey was developed to gather qualitative and quantitative data based on the opinions of a sample of students from both departments. Findings focus on the qualitative component to describe students’ experiences, with quantitative data used for triangulation purposes. Results underline students’ positive learning experiences and challenges faced. Insights regarding digital tool preferences were also revealed. Findings are not only significant in understanding an important event that caused remote architectural education in Egypt but may also serve as an important stepping-stone towards the future of design education in light of newly-introduced disruptive online learning technologies made necessary in response to lockdowns worldwide
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	ascaad2021_022
id	ascaad2021_022
authors	Baºarir, Lale; Kutluhan Erol
year	2021
title	Briefing AI: From Architectural Design Brief Texts to Architectural Design Sketches
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 23-31
summary	The main focus of this research is to uncover the underlying intuitive knowledge of architecture with the help of machine learning models. To achieve this, a generic architectural design process is considered and divided into iterative portions based on their output for each phase. This study looks into the initial portion of the architectural design process called “Briefing”. The authors search for the intuition that exists within the design process and how it can be learned by artificial intelligence (AI) that is currently gained through master-apprentice relationship and experience that builds up this knowledge. In this study, a way to enable users to attain an architectural design sketch while defining an architectural design problem with text is explored. This on-going research decomposes the components of the briefing and preliminary design sketching processes. Therefore the domain knowledge at each phase is considered for translating to constraints via natural language processing (NLP) and machine learning (ML) models such as Generative Adversarial Networks (GANs).
series	ASCAAD
type	normal paper
email
last changed	2021/08/09 13:11

_id	ijac202119101
id	ijac202119101
authors	Budig, Michael; Oliver Heckmann, Markus, Hudert, Amanda Qi Boon Ng, Zack Xuereb Conti, and Clement Jun Hao Lork
year	2021
title	Computational screening-LCA tools for early design stages
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 1, 6–22
summary	Life Cycle Assessment (LCA) has been widely adopted to identify the Global Warming Potential (GWP) in the construction industry and determine its high environmental impact through Greenhouse Gas (GHG) emissions, energy and resource consumptions. The consideration of LCA in the early stages of design is becoming increasingly important as a means to avoid costly changes at later stages of the project. However, typical LCA-based tools demand very detailed information about structural and material systems and thus become too laborious for designers in the conceptual stages, where such specifications are still loosely defined. In response, this paper presents a workflow for LCA-based evaluation where the selection of the construction system and material is kept open to compare the impacts of alternative design variants. We achieve this through a strict division into support and infill systems and a simplified visualization of a schematic floor layout using a shoebox approach, inspired from the energy modelling domain. The shoeboxes in our case are repeatable modules within a schematic floor plan layout, whose enclosures are defined by parametric 2D surfaces representing total ratios of permanent supports versus infill components. Thus, the assembly of modular surface enclosures simplifies the LCA evaluation process by avoiding the need to accurately specify the physical properties of each building component across the floor plan. The presented workflow facilitates the selection of alternative structural systems and materials for their comparison, and outputs the Global Warming Potential (GWP) in the form of an intuitive visualization output. The workflow for simplified evaluation is illustrated through a case study that compares the GWP for selected combinations of material choice and construction systems.
keywords	Computational life cycle assessment tool, embodied carbon, parametric design, construction systems, global warming potential
series	journal
email
last changed	2021/06/03 23:29

_id	acadia21_160
id	acadia21_160
authors	Cao, Shicong; Zheng, Hao
year	2021
title	A POI-Based Machine Learning Method in Predicting Health
doi	https://doi.org/10.52842/conf.acadia.2021.160
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 160-169.
summary	This research aims to explore the quantitative relationship between urban planning decisions and the health status of residents. By modeling the Point of Interest (POI) data and the geographic distribution of health-related outcomes, the research explores the critical factors in urban planning that could influence the health status of residents. It also informs decision-making regarding a healthier built environment and opens up possibilities for other data-driven methods. The data source constitutes two data sets, the POI data from OpenStreetMap, and the PLACES: Local Data for Better Health dataset from CDC. After the data is collected and joined spatially, a machine learning method is used to select the most critical urban features in predicting the health outcomes of residents. Several machine learning models are trained and compared. With the chosen model, the prediction is evaluated on the test dataset and mapped geographically. The relations between factors are explored and interpreted. Finally, to understand the implications for urban design, the impact of modified POI data on the prediction of residents' health status is calculated and compared. This research proves the possibility of predicting resident's health from urban conditions with machine learning methods. The result verifies existing healthy urban design theories from a different perspective. This approach shows vast potential that data could in future assist decision-making to achieve a healthier built environment.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	sigradi2021_359
id	sigradi2021_359
authors	Carrasco-Walburg, Carolina, Valenzuela-Astudillo, Eduardo, Maino-Ansaldo, Sandro, Correa-Díaz, Matías and Zapata-Torres, Diego
year	2021
title	Experiential Teaching-learning Tools: Critical Study of Representational Media and Immersion in Architecture
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 475–488
summary	The use of Virtual Reality (VR) in teaching-learning process of design, theory and history of architecture has increased in terms of virtual tours. A preliminary study of techniques and capabilities of Immersive Virtual Reality (IVR) systems allowed us to establish that the immersive and interactive virtual experience facilitates the perception and enhancement of spatial qualities. In addition, it facilitates analysis since it promotes observation and the development of spatial thinking. However, the use of this medium as a tool for analysis is less frequent. Therefore, in this research we comparatively evaluate the impact that VR has on such a task. We developed an analysis instrument using experiential learning cycles that was tested with students in control and experimental groups. As a result, we found that the experience of inhabiting facilitates integration of fundamental concepts, allowing empirical evaluation of architecture and streamlining communication in the classroom as an active learning strategy.
keywords	Virtual Reality, Architecture, Spatial Perception, Experiential Learning, Teaching-Learning Process
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	sigradi2021_264
id	sigradi2021_264
authors	Cenci, Laline, Pinheiro Pires, Julio Cesar, Olivo, Paula, Keith Yonegura, Robison and Avalone Neto, Olavo
year	2021
title	The Experience of Digital Manufacturing and Rapid Prototyping in the Transdisciplinary Homo-Faber Workshop: Sharing the Game
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 1269–1281
summary	The work presents the experience of Digital Fabrication and Rapid Prototyping with the objective of introducing concepts of Homo Faber, digital fabrication and rapid prototyping through the adoption of a teaching-learning strategy by gamification Homo Ludens for the construction of collective furniture. This challenge incorporates, not only the instrumentalization of new technologies for users of the Workshop, but extrapolates this field to expand the exercise of reflecting on an activity focused on the process rather than on the product.
keywords	Gamification, Digital Manufacturing, Rapid Prototyping, Architecture.
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	sigradi2021_114
id	sigradi2021_114
authors	Cesar Rodrigues, Ricardo, Kenzo Imagawa, Marcelo, Rubio Koga, Renan and Bertola Duarte, Rovenir
year	2021
title	Big Data vs Smart Data on the Generation of Floor Plans with Deep Learning
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 217–228
summary	Due to the progressive growth of data dimensionality, addressing how much data and time is required to train deep learning models has become an important research topic. Thus, in this paper, we present a benchmark for generating floor plans with Conditional Generative Adversarial Networks in which we compare 10 trained models on a dataset of 80.000 samples, the models use different data dimensions and hyper-parameters on the training phase, beyond this objective, we also tested the capability of Convolutional Neural Networks (CNN) to reduce the dataset noise. The models' assessment was made on more than 6 million with the Frétche Inception Distance (FID). The results show that such models can rapidly achieve similar or even better FID results if trained with 800 images of 512x512 pixels, in comparison to high dimensional datasets of 256x256 pixels, however, using CNNs to enhance data consistency reproduced optimal results using around 27.000 images.
keywords	Floor plans, Generative design, Generative adversarial networks, Smart Data, Dataset reduction.
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	caadria2021_038
id	caadria2021_038
authors	Chen, Jielin and Stouffs, Rudi
year	2021
title	From Exploration to Interpretation - Adopting Deep Representation Learning Models to Latent Space Interpretation of Architectural Design Alternatives
doi	https://doi.org/10.52842/conf.caadria.2021.1.131
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 131-140
summary	An informative interpretation of the hyper-dimensional design solution space can potentially enhance the cognitive capacity of designers with respect to both conventional design practice and the research domain of computational-aided generative design. However, the hitherto research of design space exploration has had limited focus on the interpretation of the hyper solution space per se due to the knowledge gap pertaining to representation and generation. Representation learning techniques, as a core paradigm in the statistically empowered domain of machine learning, possess the capability of extracting a convoluted probabilistic distribution of hyperspace with latent features from unorganized data sources in a generalized manner, which can be an intuitive modus operandi for a structural interpretation of the intricate latent design solution space and benefit the challenging task of architectural design exploration. We examine and demonstrate the potential capabilities of representation learning techniques for the interpretation of latent architectural design solution space with consideration of disentanglement and diversity.
keywords	Design space exploration; latent space interpretation; representation learning; deep generative modelling; generative architectural design
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	caadria2021_039
id	caadria2021_039
authors	Chen, Jielin, Stouffs, Rudi and Biljecki, Filip
year	2021
title	Hierarchical (multi-label) architectural image recognition and classification
doi	https://doi.org/10.52842/conf.caadria.2021.1.161
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 161-170
summary	The task of architectural image recognition for both architectural functionality and style remains an open challenge. In addition, the paucity of well-organized, large-scale architectural image datasets with specific consideration for the domain of architectural design research has hindered the exploration of these challenging tasks. Drawing upon images from the professional architectural website ArchdailyÂ®, and leveraging state-of-the-art deep-learning-based classification models, we explore a hierarchical multi-label classification model as a potential baseline for the task of architectural image classification. The resulting model showcases the potential for innovative architectural discipline-related analyses and demonstrates some heuristic insights for visual feature extraction pertaining to both architectural functionality and architectural style.
keywords	image recognition; hierarchical classification; multi-label classification; architectural functionality; style
series	CAADRIA
email
last changed	2022/06/07 07:55

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