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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Hits 1 to 20 of 658

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_id	ecaade2022_270
id	ecaade2022_270
authors	Akcay Kavakoglu, Aysegul, Almac, Bihter, Eser, Begum and Alacam, Sema
year	2022
title	AI Driven Creativity in Early Design Education - A pedagogical approach in the age of Industry 5.0
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. 133–142
doi	https://doi.org/10.52842/conf.ecaade.2022.1.133
summary	This study presents a pedagogical experiment on the integration of AI into the project studio in the early stages of design education. The motivation of the study is to support creative encounters in design studios by promoting student-design representation, student-student, and student-artificial intelligence (AI) interaction. In the scope of this study, a short-term studio project is used as a case study to examine these creative encounters. The experiment covers five stages that enable a recursive analysis-synthesis action. The stages include (i) precedent analysis of a given set of building façades images, (ii) feature extraction, (iii) composing new façade representations through employing previously generated features, (iv) training an AI by the use of styleGAN2-ADA with the outcomes of stage 3, (v) Use of synthetically generated façade images as a design driver. The pedagogical experiment is evaluated through the lenses of novelty, style, surprisingness, and complexity concepts. The challenges and potentials are introduced, as well as elaborations on the future directions of the interplay between AI-oriented making and first-year student making.
keywords	Artificial Intelligence, Computational Creativity, Design Education, StyleGAN2-ADA
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_222
id	ecaade2022_222
authors	Eisenstadt, Viktor, Bielski, Jessica, Langenhan, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2022
title	Autocompletion of Design Data in Semantic Building Models using Link Prediction and Graph Neural Networks
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. 501–510
doi	https://doi.org/10.52842/conf.ecaade.2022.1.501
summary	This paper presents an approach for AI-based autocompletion of graph-based spatial configurations using deep learning in the form of link prediction through graph neural networks. The main goal of the research presented is to estimate the probability of connections between the rooms of the spatial configuration graph at hand using the available semantic information. In the context of early design stages, deep learning-based prediction of spatial connections helps to make the design process more efficient and sustainable using the past experiences collected in a training dataset. Using the techniques of transfer learning, we adapted methods available in the modern graph-based deep learning frameworks in order to apply them for our autocompletion purposes to suggest possible further design steps. The results of training, testing, and evaluation showed very good results and justified application of these methods.
keywords	Spatial Configuration, Autocompletion, Link Prediction, Deep Learning
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_508
id	caadria2022_508
authors	Yousif, Shermeen and Bolojan, Daniel
year	2022
title	Deep Learning-Based Surrogate Modeling for Performance-Driven Generative Design Systems
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. 363-372
doi	https://doi.org/10.52842/conf.caadria.2022.1.363
summary	Within the context of recent research to augment the design process with artificial intelligence (AI), this work contributes by introducing a new method. The proposed method automates the design environmental performance evaluation by developing a deep learning-based surrogate model to inform the early design stages. The project is aimed at automating performative design aspects, enabling designers to focus on creative design space exploration while retrieving real-time predictions of environmental metrics of evolving design options in generative systems. This shift from a simulation-based to a prediction-based approach liberates designers from having to conduct simulation and optimization procedures and allows for their native design abilities to be augmented. When introduced into design systems, AI strategies can improve existing protocols, and enable attaining environmentally conscious designs and achieve UN Sustainable Development Goal 11.
keywords	Deep Learning, Artificial Intelligence, Surrogate Modeling, Automating Building Performance Simulation, Generative Design Systems, SDG11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_52
id	ecaade2022_52
authors	Nejur, Andrei and Balaban, Thomas
year	2022
title	The A(fin)ne Pavilion - Pandemic adapted architectural studio fabrication
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. 507–516
doi	https://doi.org/10.52842/conf.ecaade.2022.2.507
summary	This paper presents the didactical and research process of a pandemic-adapted digital fabrication, material-driven research master studio held at University of Montreal School of Architecture in early 2021 that concluded with the construction of a large-scale research pavilion assembled by the students with hand tools only. The paper focuses on the structure of the studio and how the research was re-oriented to permit material investigations using limited physical interaction between the participants, intermittent access to on-campus fabrication facilities, limited financial resources, and a cohort of students with near-zero computational design experience.
keywords	DIY, Education, Pavilion, Construction, Folding, Pandemic, Digital Fabrication
series	eCAADe
email
last changed	2024/04/22 07:10

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

_id	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	caadria2022_166
id	caadria2022_166
authors	Eisenstadt, Viktor, Bielski, Jessica, Mete, Burak, Langenhan, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2022
title	Autocompletion of Floor Plans for the Early Design Phase in Architecture: Foundations, Existing Methods, and Research Outlook
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. 323-332
doi	https://doi.org/10.52842/conf.caadria.2022.1.323
summary	This paper contributes the current research state and possible future developments of AI-based autocompletion of architectural floor plans and shows demand for its establishment in computer-aided architectural design to facilitate decent work, economic growth through accelerating the design process to meet the future workload. Foundations of data representations together with the autocompletion contexts are defined, existing methods described and evaluated in the integrated literature review, and criteria for qualitative and sustainable autocompletion are proposed. Subsequently, we contribute three unique deep learning-based autocompletion methods currently in development for the research project metis-II. They are described in detail from a technical point of view on the backdrop of how they adhere to the proposed criteria for creating our novel AI.
keywords	Artificial Intelligence, Architectural Design, Floor Plan, Autocompletion, SDG 8, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_78
id	ecaade2022_78
authors	Eroglu, Ruºen and Gül, Leman Figen
year	2022
title	Architectural Form Explorations through Generative Adversarial Networks - Predicting the potentials of StyleGAN
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. 575–582
doi	https://doi.org/10.52842/conf.ecaade.2022.2.575
summary	In recent years, generative models have been rapidly transforming into a broad field of research, and artificial intelligence (AI) works are increasing. Since deep learning technologies such as Generative Adversarial Networks (GANs) providing synthesized new images are becoming more accessible, researchers in the design and related fields are very much interested in adapting GANs into practice. Especially, StyleGAN has a strong capability for image learning, reconstruction simulation, and absorbing the pixel characteristics of images in the input dataset. StyleGAN also produces similar imitation outputs and summarizes all the input data into one "average output". The study aims to reveal the potential of these outputs that can be employed as a visual inspiration aid for designers. This article will discuss the outputs of the experiments, findings, and prospects of StyleGAN.
keywords	Artificial Intelligence, Machine Learning, Generative Adversarial Networks, StyleGAN
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_65
id	ecaade2022_65
authors	Halici, Süheyla Müge and Gül, Leman Figen
year	2022
title	Utilizing Generative Adversarial Networks for Augmenting Architectural Massing Studies: AI-assisted Mixed Reality
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. 323–330
doi	https://doi.org/10.52842/conf.ecaade.2022.1.323
summary	A technique for architectural massing studies in Mixed Reality (MR) is described. Generative Adversarial Networks let an object appear to have a different material than it actually has. The benefits during design are twofold. From one side the congruence between shape and material are subject to verification in real-time. From the other side, the designer is liberated from the usual restrictions and biases as to shape that are inevitable due to the mechanical properties of a mock-up. This is referred to as artificial intelligence assisted MR (AI-A MR) in this work. The technique consists of two steps: based on preparing synthetic data in Rhino/Grasshopper to be trained with an image-to- image translation model and implemented to the trained model in MR design environment. Next to the practical merits, a contribution of the work with respect to MR methodology is that it exemplifies the solution of some persistent tracking and registration problems.
keywords	Hybrid Design Environment, Dynamic Design Models, Mixed Reality, Generative Adversarial Networks, Image-to-Image Translation, Tracking
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_176
id	ecaade2022_176
authors	Kotov, Anatolii, Starke, Rolf and Vukorep, Ilija
year	2022
title	Spatial Agent-based Architecture Design Simulation Systems
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. 105–112
doi	https://doi.org/10.52842/conf.ecaade.2022.2.105
summary	This paper presents case studies and analysis of agent-based reinforcement learning (RL) systems towards practical applications for specific architecture/engineering tasks using Unity 3D-based simulation methods. Finding and implementing sufficient abstraction for architecture and engineering problems to be solved by agent-based systems requires broad architectural knowledge and the ability to break down complex problems. Modern artificial intelligence (AI) and machine learning (ML) systems based on artificial neural networks can solve complex problems in different domains such as computer vision, language processing, and predictive maintenance. The paper will give a theoretical overview, such as more theoretical abstractions like zero-sum games, and a comparison of presented games. The application section describes a possible categorization of practical usages. From more general applications to more narrowed ones, we explore current possibilities of RL application in the field of relatable problems. We use the Unity 3D engine as the basis of a robust simulation environment.
keywords	AI Aided Architecture, Reinforcement Learning, Agent Simulation
series	eCAADe
email
last changed	2024/04/22 07:10

_id	acadia22_742
id	acadia22_742
authors	Leach, Neil
year	2022
title	What is Creativity?
source	ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 742-751.
summary	This paper explores what we can perhaps begin to understand about the nature of creativity in the mirror of AI, with reference to the now famous Go match between AlphaGo and Lee Sedol. It argues that one particular famous move in that match sheds light on some of the crucial questions regarding creativity. It compares this move to the ‘smart’ architectural designs generated by AI, and asks whether computers can be creative, or whether they are simply conducting a ‘search and synthesis’ operation. Finally, the paper asks the provocative question, as to whether creativity even exists, or whether it is a myth that can now be debunked, thanks to our insights from the world of AI.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:04

_id	ecaade2022_346
id	ecaade2022_346
authors	Sartorius, Marie P. and von Both, Petra
year	2022
title	Rule-Based Design for the Integration of Humanoid Assistance Robotics into the Living Environment of Senior Citizens
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. 367–376
doi	https://doi.org/10.52842/conf.ecaade.2022.2.367
summary	The following paper deals with the hypothesis that in a few years, the everyday lives of seniors and those in need of care will be considerably facilitated by using humanoid assistance robots to improve and prolong their independent lives. The interdisciplinary research project JuBOT of the Karl-Zeiss Foundation deals with developing and applying AI-supported humanoid robots in seniors living spaces. In the research area of architecture, several questions arise in the sense of co-design: Which requirements and design rules can be derived and generalised to develop suitable typologies? And how can these requirements of robotics and buildings be integrated into a digital design process? In this matter, a master thesis identified and categorised areas of action for the assistance robot through a user- and function-based analysis process. Afterwards, a proposal for a generalisable typology for residential modules has been developed, applied, and evaluated. In addition to gaining architectural knowledge, the JuBOT project is also about implementing a suitable digital design process. Thus, the identified planning requirements must be implemented in a BIM-based checking process (ModelCheck).
keywords	Senior Residence, Accessibility, Care Concept, Living Concept, Architectural Psychology, Assistant Robot, BIM, Building Information Modeling, ModelCheck
series	eCAADe
email
last changed	2024/04/22 07:10

_id	acadia22_628
id	acadia22_628
authors	Sung, Woongki; Nagakura, Takehiko; Tsai, Daniel
year	2022
title	Design Contextualism by AI
source	ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 628-637.
summary	This paper presents a data- driven method for encoding and representing the statistical information of an architectural site layout in the form of a Bayesian network. Given a set of simplified satellite photos and maps, the site layout model is formulated that consists of variables of interest. Structured learning is performed to find an optimal Bayesian network structure that best fits the dataset and is then trained to calculate its parameters.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:04

_id	caadria2022_503
id	caadria2022_503
authors	Yousif, Shermeen and Vermisso, Emmanouil
year	2022
title	Towards AI-Assisted Design Workflows for an Expanded Design Space
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. 335-344
doi	https://doi.org/10.52842/conf.caadria.2022.2.335
summary	The scope of this paper is to formulate and evaluate the structure of a viable design workflow that combines a variety of computational tools and uses artificial intelligence (AI) to enhance the designer‚s capacity to explore design options within an expanded design space. In light of the autonomous and progressively post-anthropocentric generative capability of recent AI strategies for architectural design, we are interested in investigating some of the challenges involved in the insertion of such AI strategies into a new generative design system, involving data curation and the placement of any AI-assisted model in the overall workflow, as well as its (AI‚s) reciprocity with other computational methods such as discrete assembly and agent-based modeling. The paper presents our interrogation of the proposed AI-assisted framework, demonstrated in experiments of formulating multiple design workflows following different strategies. The workflow strategies show that integrating AI networks into a framework with other computational tools affords a different kind of design exploration than other methods; the prospect of novel solutions is heavily dependent on the interconnectedness of such methods and the dataset curation process. Collectively, the work contributes to innovation in architectural education and practice through enhancing scientific research (in line with UN Sustainable Development Goal 9).
keywords	Artificial Intelligence, Deep Learning, AI-assisted Design Workflows, Design Space Exploration, Generative Systems, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_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	ecaade2022_85
id	ecaade2022_85
authors	Ataman, Cem, Herthogs, Pieter, Tuncer, Bige and Perrault, Simon
year	2022
title	Multi-Criteria Decision Making in Digital Participation - A framework to evaluate participation in urban design processes
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. 401–410
doi	https://doi.org/10.52842/conf.ecaade.2022.1.401
summary	Data-driven urban design processes consist of iterative actions of many stakeholders, which require digital participatory approaches for collecting data from a high number of participants to make informed decisions. It is important to evaluate such processes to justify the necessary costs and efforts while continuously improving digital participation. Nevertheless, such evaluation remains a challenge due to the involvement of different stakeholders including participants, designers, and policymakers in decision-making processes, and the lack of a systematic method to generalize participation outputs that are mostly situated and context based. By addressing this challenge, this paper introduces a Multi-Criteria Decision Analysis (MCDA) based framework to measure the effectiveness and quality of digital participation systematically and quantitatively. To achieve such evaluation, we conducted a digital participation experiment and investigated such processes with the help of participants, designers, and policymakers from Singapore and Hamburg. By formulating this framework, we aim to reveal perspectives of different stakeholders towards digital participation and enable the evaluation and comparison of digital participation processes based on the introduced digital participation criteria.
keywords	Data-Driven Urban Design, Digital Participation, Stakeholder Involvement, Multi-Criteria Decision Analysis (MCDA), Participation Quantification
series	eCAADe
email
last changed	2024/04/22 07:10

_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	ecaade2022_171
id	ecaade2022_171
authors	Daher, Elie, Kubicki, Sylvain and Pak, Burak
year	2022
title	Propositions for Enabling Participation in Performance-Driven Design
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 1, Ghent, 13-16 September 2022, pp. 421–430
doi	https://doi.org/10.52842/conf.ecaade.2022.1.421
summary	In Performance-Driven Design, it is challenging for different stakeholders such as the end-users to participate in the co-designing process. Performance-driven design requires complex algorithmic calculations, simulations, and optimizations. These computational functionalities enabled for this design process lack of transparency and can be sometimes complicated to understand. Therefore, the current applications of Performance-Driven Design contradict the participatory design where social interactions are considered as important steps to produce desirable and accepted design outcomes. In this context, the main aim of this study reported in this paper based on a 4-years PhD thesis at Luxembourg Institute of Science and Technology and KU Leuven, is to address research methods suitable for enabling higher levels of participation in Performance-Driven Design and thus to provide recommendations and guidelines.
keywords	Performance-Driven Design, Participation Design Process, Architectural Design, Performance and Requirements Modeling
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_367
id	ecaade2022_367
authors	Doumpioti, Christina and Huang, Jeffrey
year	2022
title	Field Condition - Environmental sensibility of spatial configurations with the use of machine intelligence
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. 67–74
doi	https://doi.org/10.52842/conf.ecaade.2022.2.067
summary	Within computational environmental design (CED), different Machine Learning (ML) models are gaining ground. They aim for time efficiency by automating simulation and speeding up environmental performance feedback. This study suggests an approach that enhances not the optimization but the generative aspect of environmentally driven ML processes in architectural design. We follow Stan Allen's (2009) idea of 'field conditions' as a bottom-up phenomenon according to which form and space emerge from local invisible and dynamic connections. By employing parametric modeling, environmental analysis data, and conditional Generative Adversarial Networks [cGAN] we introduce a generative approach in design that reverses the typical design process of going from formal interpretation to analysis and encourages the emergence of spatial configurations with embedded environmental intelligence. We call it Intensive-driven Environmental Design Computation [IEDC], and we employ it in a case study on a residential building typology encountered in the Mediterranean. The paper describes the process, emphasizing dataset preparation as the stage where the logic of field conditions is established. The proposed research differentiates from cGAN models that offer automatic environmental performance predictions to one that spatial predictions stem from dynamic fields.
keywords	Field Architecture, Environmental Design, Generative Design, Machine Learning, Residential Typologies
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_239
id	caadria2022_239
authors	Huang, Chenyu, Zhang, Gengjia, Yin, Minggang and Yao, Jiawei
year	2022
title	Energy-driven Intelligent Generative Urban Design, Based on Deep Reinforcement Learning Method With a Nested Deep Q-R Network
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.2022.1.233
summary	To attain "carbon neutrality," lowering urban energy use and increasing the use of renewable resources have become critical concerns for urban planning and architectural design. Traditional energy consumption evaluation tools have a high operational threshold, requiring specific parameter settings and cross-disciplinary knowledge of building physics. As a result, it is difficult for architects to manage energy issues through 'trial and error' in the design process. The purpose of this study is to develop an automated workflow capable of providing urban configurations that minimizing the energy use while maximizing rooftop photovoltaic power potential. Based on shape grammar, parametric meta models of three different urban forms were developed and batch simulated for its energy performance. Deep reinforcement learning (DRL) is introduced to find the optimal solution of the urban geometry. A neural network was created to fit a real-time mapping of urban form indicators to energy performance and was utilized to predict reward for the DRL process, namely a Deep R-Network, while nested within a Deep Q-Network. The workflow proposed in this paper promotes efficiency in optimizing the energy performance of solutions in the early stages of design, as well as facilitating a collaborative design process with human-machine interaction.
keywords	energy-driven urban design, intelligent generative design, rooftop photovoltaic power, deep reinforcement learning, SDG 11, SDG 12
series	CAADRIA
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last changed	2022/07/22 07:34

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