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

PDF papers
References

Hits 1 to 20 of 624

Reformat results as: short short into frame detailed detailed into frame

_id	cf2019_020
id	cf2019_020
authors	Belém, Catarina; Luís Santos and António Leitão
year	2019
title	On the Impact of Machine Learning: Architecture without Architects?
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, pp. 148-167
summary	Architecture has always followed and adopted technological breakthroughs of other areas. As a case in point, in the last decades, the field of computation changed the face of architectural practice. Considering the recent breakthroughs of Machine Learning (ML), it is expectable to see architecture adopting ML-based approaches. However, it is not yet clear how much this adoption will change the architectural practice and in order to forecast this change it is necessary to understand the foundations of ML and its impact in other fields of human activity. This paper discusses important ML techniques and areas where they were successfully applied. Based on those examples, this paper forecast hypothetical uses of ML in the realm of building design. In particular, we examine ML approaches in conceptualization, algorithmization, modeling, and optimization tasks. In the end, we conjecture potential applications of such approaches, suggest future lines of research, and speculate on the future face of the architectural profession.
keywords	Machine Learning, Algorithmic Design, AI for Building Design
series	CAAD Futures
type	normal paper
email
last changed	2019/07/29 14:54

_id	acadia19_130
id	acadia19_130
authors	Devadass, Pradeep; Heimig, Tobias; Stumm, Sven; Kerber, Ethan; Cokcan, Sigrid Brell
year	2019
title	Robotic Constraints Informed Design Process
doi	https://doi.org/10.52842/conf.acadia.2019.130
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 130-139
summary	Promising results in efficiently producing highly complex non-standard designs have been accomplished by integrating robotic fabrication with parametric design. However, the project workflow is hampered due to the disconnect between designer and robotic fabricator. The design is most often developed by the designer independently from fabrication process constraints. This results in fabrication difficulties or even non manufacturable components. In this paper we explore the various constraints in robotic fabrication and assembly processes, analyze their influence on design, and propose a methodology which bridges the gap between parametric design and robotic production. Within our research we investigate the workspace constraints of robots, end effectors, and workpieces used for the fabrication of an experimental architectural project: “The Twisted Arch.” This research utilizes machine learning approaches to parameterize, quantify, and analyze each constraint while optimizing how those parameters impact the design output. The research aims to offer a better planning to production process by providing continuous feedback to the designer during early stages of the design process. This leads to a well-informed “manufacturable” design.
keywords	Robotic Fabrication and Assembly, Mobile Robotics, Machine Learning, Parametric Design, Constraint Based Design.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:55

_id	ecaadesigradi2019_510
id	ecaadesigradi2019_510
authors	Giannopoulou, Effima, Baquero, Pablo, Warang, Angad, Orciuoli, Affonso and T. Estévez, Alberto
year	2019
title	Stripe Segmentation for Branching Shell Structures - A Data Set Development as a Learning Process for Fabrication Efficiency and Structural Performance
doi	https://doi.org/10.52842/conf.ecaade.2019.3.063
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 3, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 63-70
summary	This article explains the evolution towards the subject of digital fabrication of thin shell structures, searching for the computational design techniques which allow to implement biological pattern mechanisms for efficient fabrication procedures. The method produces data sets in order to analyse and evaluate parallel alternatives of branching topologies, segmentation patterns, material usage, weight and deflection values as a user learning process. The importance here is given to the selection of the appropriate attributes, referring to which specific geometric characteristics of the parametric model are affecting each other and with what impact. The outcomes are utilized to train an Artificial Neural Network to predict new building information based on new combinations of desired parameters so that the user can decide and adjust the design based on the new information.
keywords	Digital Fabrication; Shell Structures; Segmentation; Machine Learning; Branching Topologies; Bio-inspired
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:51

_id	caadria2019_626
id	caadria2019_626
authors	Hahm, Soomeen, Maciel, Abel, Sumitiomo, Eri and Lopez Rodriguez, Alvaro
year	2019
title	FlowMorph - Exploring the human-material interaction in digitally augmented craftsmanship
doi	https://doi.org/10.52842/conf.caadria.2019.1.553
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 1, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 553-562
summary	It has been proposed that, after the internet age, we are now entering a new era of the '/Augmented Age/' (King, 2016). Physician Michio Kaku imagined the future of architects will be relying heavily on Augmented Reality technology (Kaku, 2015). Augmented reality technology is not a new technology and has been evolving rapidly. In the last three years, the technology has been applied in mainstream consumer devices (Coppens, 2017). This opened up possibilities in every aspect of our daily lives and it is expected that this will have a great impact on every field of consumer's technology in near future, including design and fabrication. What is the future of design and making? What kind of new digital fabrication paradigm will emerge from inevitable technological development? What kind of impact will this have on the built environment and industry? FlowMorph is a research project developed in the Bartlett School of Architecture, B-Pro AD with the collaboration of the authors and students as a 12 month MArch programme, we developed a unique design project trying to answer these questions which will be introduced in this paper.
keywords	Augmented Reality, Mixed Reality, Virtual Reality, Design Augmentation, Digital Fabrication, Cognition models, Conceptual Designing, Design Process, Design by Making, Generative Design, Computational Design, Human-Machine Collaboration, Human-Computer Collaboration, Human intuition in digital fabrication
series	CAADRIA
email
last changed	2022/06/07 07:51

_id	caadria2019_650
id	caadria2019_650
authors	Papasotiriou, Tania
year	2019
title	Identifying the Landscape of Machine Learning-Aided Architectural Design - A Term Clustering and Scientometrics Study
doi	https://doi.org/10.52842/conf.caadria.2019.2.815
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 815-824
summary	Recent advances in Machine Learning and Deep Learning revolutionise many industry disciplines and underpin new ways of problem-solving. This paradigm shift hasn't left Architecture unaffected. To investigate the impact on architectural design, this study utilises two approaches. First, a text mining method for content analysis is employed, to perform a robust review of the field's literature. This allows identifying and discussing current trends and possible future directions of this research domain in a systematic manner. Second, a Scientometrics study based on bibliometric reviews is employed to obtain quantitative measures of the global research activity in the described domain. Insights on research trends and identification of the most influential networks in this dataset were acquired by analysing terms co-occurrence, scientific collaborations, geographic distribution, and co-citation analysis. The paper concludes with a discussion on the limitations, opportunities and future research directions in the field of Machine Learning-aided architectural design.
keywords	Machine Learning; Text mining; Scientometrics
series	CAADRIA
email
last changed	2022/06/07 08:00

_id	ecaadesigradi2019_327
id	ecaadesigradi2019_327
authors	Silva, Daniela, Paio, Alexandra and Sousa, José Pedro
year	2019
title	Reprogramming Practice - Revising design thinking through digital fabrication
doi	https://doi.org/10.52842/conf.ecaade.2019.1.379
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 379-386
summary	Questioning the importance and impact of design thinking methodologies in the architectural design studios is a backbone of architectural education in twenty first century. 3D printing and digital manufacturing are disruptive technologies that are changing architects and designers daily lives. These trends require new skills, based on a deep understanding of digital continuum from design to production, from generation to fabrication. This continuity transcends the merely instrumental contributions of a person-machine relationship to praxis, has begun to evolve as a medium that supports a continuous logic of design thinking and making. Design thinking methodologies associated with digital fabrication emerged as a leading technological and design issue of digital research and design. As designers, we are witnessing a no frontier between computational design and digital fabrication. For this paper is taken into consideration the work of two architecture studios that share a unique background on new methodologies by embracing the digital technology in their own practice. Their work reflects on new design methodologies facing the expansion of digital technology in architectural practice. This paper discusses the possibility of new design thinking methods driven by digital fabrication.
keywords	Design thinking; Digital Fabrication; AEC; Collaborative Design; Architectural Practice
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:56

_id	acadia19_288
id	acadia19_288
authors	Vivaldi, Jordi
year	2019
title	Surrealist Aesthetics in Second-Order, Cybernetic Architecture
doi	https://doi.org/10.52842/conf.acadia.2019.288
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 288-297
summary	In experimental architecture and during the last decade, second-order cybernetic systems (SOCA) have been broadly explored. Under this umbrella, the implementation of robotics and machine learning in recent experimental projects has impacted academia through new fabrication strategies, new design methods, and new adaptive devices. This paper presents a theoretical approach to the aesthetic side of this impact. In particular, it argues that SOCA rearticulates Benjamin’s concept of “distracted perception” through three structural principles of Surrealism: the emphasis of presentation over representation; the centrality of the notion of automatism; and the simultaneous management of closeness and distance. Each alignment is doubly articulated. First it establishes a comparison between Surrealist artwork from the first half of the 20th century and three SOCA projects in which the notion of autonomy and ubiquity are crucial. Second, it evaluates the impact on Benjamin’s notion of “distracted perception.” The paper concludes that the Surrealist aesthetic structures analysed in SOCA differ from traditional Surrealism in the replacement of an inner and unconscious other by an outer and algorithmic other. Its presence simultaneously expands and contracts Benjamin’s architectural understanding of “distracted perception,” a double movement whose perception paradoxically occurs under the single framework of Benjamin’s haptic vision.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:58

_id	ecaadesigradi2019_671
id	ecaadesigradi2019_671
authors	Jabi, Wassim, Chatzivasileiadi, Aikaterini, Wardhana, Nicholas Mario, Lannon, Simon and Aish, Robert
year	2019
title	The synergy of non-manifold topology and reinforcement learning for fire egress
doi	https://doi.org/10.52842/conf.ecaade.2019.2.085
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 85-94
summary	This paper illustrates the synergy of non-manifold topology (NMT) and a branch of artificial intelligence and machine learning (ML) called reinforcement learning (RL) in the context of evaluating fire egress in the early design stages. One of the important tasks in building design is to provide a reliable system for the evacuation of the users in emergency situations. Therefore, one of the motivations of this research is to provide a framework for architects and engineers to better design buildings at the conceptual design stage, regarding the necessary provisions in emergency situations. This paper presents two experiments using different state models within a simplified game-like environment for fire egress with each experiment investigating using one vs. three fire exits. The experiments provide a proof-of-concept of the effectiveness of integrating RL, graphs, and non-manifold topology within a visual data flow programming environment. The results indicate that artificial intelligence, machine learning, and RL show promise in simulating dynamic situations as in fire evacuations without the need for advanced and time-consuming simulations.
keywords	Non-manifold topology; Topologic; Reinforcement Learning; Fire egress
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:52

_id	ecaadesigradi2019_605
id	ecaadesigradi2019_605
authors	Andrade Zandavali, Bárbara and Jiménez García, Manuel
year	2019
title	Automated Brick Pattern Generator for Robotic Assembly using Machine Learning and Images
doi	https://doi.org/10.52842/conf.ecaade.2019.3.217
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 3, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 217-226
summary	Brickwork is the oldest construction method still in use. Digital technologies, in turn, enabled new methods of representation and automation for bricklaying. While automation explored different approaches, representation was limited to declarative methods, as parametric filling algorithms. Alternatively, this work proposes a framework for automated brickwork using a machine learning model based on image-to-image translation (Conditional Generative Adversarial Networks). The framework consists of creating a dataset, training a model for each bond, and converting the output images into vectorial data for robotic assembly. Criteria such as: reaching wall boundary accuracy, avoidance of unsupported bricks, and brick's position accuracy were individually evaluated for each bond. The results demonstrate that the proposed framework fulfils boundary filling and respects overall bonding structural rules. Size accuracy demonstrated inferior performance for the scale tested. The association of this method with 'self-calibrating' robots could overcome this problem and be easily implemented for on-site.
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:54

_id	ecaadesigradi2019_034
id	ecaadesigradi2019_034
authors	Chen, Dechen, Luo, Dan, Xu, Weiguo, Luo, Chen, Shen, Liren, Yan, Xia and Wang, Tianjun
year	2019
title	Re-perceive 3D printing with Artificial Intelligence
doi	https://doi.org/10.52842/conf.ecaade.2019.1.443
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 443-450
summary	How can machine learning be combined with intelligent construction, material testing and other related topics to develop a new method of fabrication? This paper presents a set of experiments on the dynamic control of the heat deflection of thermoplastics in searching for a new 3D printing method with the dynamic behaviour of PLA and with a comprehensive workflow utilizing mechanic automation, computer vision, and artificial intelligence. Additionally, this paper will discuss in-depth the performance of different types of neural networks used in the research and conclude with solid data on the potential connection between the structure of neural networks and the dynamic, complex material performance we are attempting to capture.
keywords	3D printing; AI; automation; material; fabrication
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:55

_id	ecaadesigradi2019_514
id	ecaadesigradi2019_514
authors	de Miguel, Jaime, Villafa?e, Maria Eugenia, Piškorec, Luka and Sancho-Caparrini, Fernando
year	2019
title	Deep Form Finding - Using Variational Autoencoders for deep form finding of structural typologies
doi	https://doi.org/10.52842/conf.ecaade.2019.1.071
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 71-80
summary	In this paper, we are aiming to present a methodology for generation, manipulation and form finding of structural typologies using variational autoencoders, a machine learning model based on neural networks. We are giving a detailed description of the neural network architecture used as well as the data representation based on the concept of a 3D-canvas with voxelized wireframes. In this 3D-canvas, the input geometry of the building typologies is represented through their connectivity map and subsequently augmented to increase the size of the training set. Our variational autoencoder model then learns a continuous latent distribution of the input data from which we can sample to generate new geometry instances, essentially hybrids of the initial input geometries. Finally, we present the results of these computational experiments and lay out the conclusions as well as outlook for future research in this field.
keywords	artificial intelligence; deep neural networks; variational autoencoders; generative design; form finding; structural design
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:55

_id	caadria2019_553
id	caadria2019_553
authors	del Campo, Matias, Manninger, Sandra, Sanche, Marianne and Wang, Leetee
year	2019
title	The Church of AI - An examination of architecture in a posthuman design ecology
doi	https://doi.org/10.52842/conf.caadria.2019.2.767
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 767-772
summary	The Project, the Church of AI, taps into the opportunities of Artificial Intelligence as a device for Architecture Design in a twofold way: On the one side by employing a design technique that is based on the ability of Artificial Intelligence to generate form autonomously of human interaction, and on the other hand by speculating about the nature of devotion, the sublime and awe in a posthuman society.
keywords	Artificial Intelligence; Posthuman; Postdigital; Machine Learning; DeepDream
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	acadia19_412
id	acadia19_412
authors	Del Campo, Matias; Manninger, Sandra; Carlson, Alexandra
year	2019
title	Imaginary Plans
doi	https://doi.org/10.52842/conf.acadia.2019.412
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 412-418
summary	Artificial Neural Networks (NN) have become ubiquitous across disciplines due to their high performance in modeling the real world to execute complex tasks in the wild. This paper presents a computational design approach that uses the internal representations of deep vision neural networks to generate and transfer stylistic form edits to both 2D floor plans and building sections. The main aim of this paper is to demonstrate and interrogate a design technique based on deep learning. The discussion includes aspects of machine learning, 2D to 2D style transfers, and generative adversarial processes. The paper examines the meaning of agency in a world where decision making processes are defined by human/machine collaborations (Figure 1), and their relationship to aspects of a Posthuman design ecology. Taking cues from the language used by experts in AI, such as Hallucinations, Dreaming, Style Transfer, and Vision, the paper strives to clarify the position and role of Artificial Intelligence in the discipline of Architecture.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:55

_id	ecaadesigradi2019_357
id	ecaadesigradi2019_357
authors	Gönenç Sorguç, Arzu, Özgenel, Ça?lar F?rat, Kruºa Yemiºcio?lu, Müge, Küçüksubaº?, Fatih, Y?ld?r?m, Soner, Antonini, Ernesto, Bartolomei, Luigi, Ovesen, Nis and Stein?, Nicolai
year	2019
title	STEAM Approach for Architecture Education
doi	https://doi.org/10.52842/conf.ecaade.2019.1.137
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 137-146
summary	Starting with the first founded university, higher education has been evolving continuously, yet the pace of this evolution is not as fast as the changes that we observe in practice. Today, this discrepancy is not only limited to the content of the curricula but also the expected skills and competencies. It is evident that 21st-century skills and competencies should be much different than the ones delivered in the 20th-century due to rapidly developing and spreading new design and information technologies. Each and every discipline has been in continuous search of the "right" way of formalization of education both content and skill wise. This paper focuses on architectural design education incorporating discussions on the role of STEAM (Science Technology, Engineering, Art and Mathematics). The study presents the outcomes of the ArchiSTEAM project, which is funded by EU Erasmus+ Programme, with the aim of re-positioning STEAM in architectural design education by contemplating 21st-century skills (a.k.a. survival skills) of architects. Three educational modules together with the andragogic approaches, learning objectives, contents, learning/teaching activities and assessment methods determined with respect to the skill sets defined for 21st-century architects.
keywords	STEAM; Architectural Education; Survival Skills
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:50

_id	ecaadesigradi2019_346
id	ecaadesigradi2019_346
authors	Kaftan, Martin, Sautter, Sebastian and Kubicek, Bernhard
year	2019
title	Integrating BIPV during Early Stages of Building Design
doi	https://doi.org/10.52842/conf.ecaade.2019.2.139
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 139-144
summary	In the quest to achieve the ambitious climate and clean energy targets the broad implementation of Integrated Photovoltaics (BIPV) is one of the keys. Photovoltaic (PV) modules can be installed above or on current roofing or traditional wall structures. In addition, BIPV devices substitute the skin of the exterior construction frame, i.e. the weather screen, thus simultaneously acting as both a climate screen and an energy producing source. However, while the integral planning strategy to building projects promotes the effective execution of BIPV, the limitation lies in the absence of both instruments and easy-to-use planning aid guidelines, particularly by non-PV experts in the early design stage. This study presents computational methods that help to quickly analyze the BIPV potential for a given building project and to suggest the optimal economical amount and location of the panels based on the building's energy demand profile.
keywords	building integrated photovoltaic (BIPV); integral planning; design rules; simplified models; machine learning
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:52

_id	caadria2019_109
id	caadria2019_109
authors	Kim, Jinsung, Song, Jaeyeol and Lee, Jin-Kook
year	2019
title	Approach to Auto-recognition of Design Elements for the Intelligent Management of Interior Pictures
doi	https://doi.org/10.52842/conf.caadria.2019.2.785
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 785-794
summary	This paper explores automated recognition of elements in interior design pictures for an intelligent design reference management system. Precedent design references have a significant role to help architects, designer and even clients in general architecture design process. Pictures are one of the representation that could exactly show a kind of design idea and knowledge. Due to the velocity, variety and volume of reference pictures data with growth of references platform, it is hard and time-consuming to handle the data with current manual way. To solve this problem , this paper depicts a deep learning-based approach to figuring out design elements and recognizing the design feature of them on the interior pictures using faster-RCNN and CNN algorithms. The targets are the residential furniture such as a table and a seating. Through proposed application, input pictures can automatically have tagging data as follows; seating1(type: sofa, seating capacity: two-seaters, design style: classic)
keywords	Interior design picture; Design element; Design feature; Automated recognition; Design Reference management
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	ecaadesigradi2019_339
id	ecaadesigradi2019_339
authors	Kinugawa, Hina and Takizawa, Atsushi
year	2019
title	Deep Learning Model for Predicting Preference of Space by Estimating the Depth Information of Space using Omnidirectional Images
doi	https://doi.org/10.52842/conf.ecaade.2019.2.061
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 61-68
summary	In this study, we developed a method for generating omnidirectional depth images from corresponding omnidirectional RGB images of streetscapes by learning each pair of omnidirectional RGB and depth images created by computer graphics using pix2pix. Then, the models trained with different series of images shot under different site and weather conditions were applied to Google street view images to generate depth images. The validity of the generated depth images was then evaluated visually. In addition, we conducted experiments to evaluate Google street view images using multiple participants. We constructed a model that estimates the evaluation value of these images with and without the depth images using the learning-to-rank method with deep convolutional neural network. The results demonstrate the extent to which the generalization performance of the streetscape evaluation model changes depending on the presence or absence of depth images.
keywords	Omnidirectional image; depth image; Unity; Google street view; pix2pix; RankNet
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:52

_id	acadia19_664
id	acadia19_664
authors	Koshelyuk, Daniil; Talaei, Ardeshir; Garivani, Soroush; Markopoulou, Areti; Chronis, Angelo; Leon, David Andres; Krenmuller, Raimund
year	2019
title	Alive
doi	https://doi.org/10.52842/conf.acadia.2019.664
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 664-673
summary	In the context of data-driven culture, built space still maintains low responsiveness and adaptability. Part of this reality lies in the low resolution of live information we have about the behavior and condition of surfaces and materials. This research addresses this issue by exploring the development of a deformation-sensing composite membrane material system following a bottom-up approach and combining various technologies toward solving related technical issues—exploring conductivity properties of graphene and maximizing utilization within an architecture-related proof-of-concept scenario and a workflow including design, fabrication, and application methodology. Introduced simulation of intended deformation helps optimize the pattern of graphene nanoplatelets (GNP) to maximize membrane sensitivity to a specific deformation type while minimizing material usage. Research explores various substrate materials and graphene incorporation methods with initial geometric exploration. Finally, research introduces data collection and machine learning techniques to train recognition of certain types of deformation (single point touch) on resistance changes. The final prototype demonstrates stable and symmetric readings of resistance in a static state and, after training, exhibits an 88% prediction accuracy of membrane shape on a labeled sample data-set through a pre-trained neural network. The proposed framework consisting of a simulation based, graphene-capturing fabrication method on stretchable surfaces, and includes initial exploration in neural network training shape detection, which combined, demonstrate an advanced approach to embedding intelligence.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:51

_id	ecaadesigradi2019_389
id	ecaadesigradi2019_389
authors	Mohite, Ashish, Kochneva, Mariia and Kotnik, Toni
year	2019
title	Speed of Deposition - Vehicle for structural and aesthetic expression in CAM
doi	https://doi.org/10.52842/conf.ecaade.2019.1.729
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 729-738
summary	This paper presents intermediate results of an experimental research directed towards development of a method that uses additive manufacturing technology as a generative agent in architectural design process. The primary technique is to variate speed of material deposition of a 3D printer in order to produce undetermined textural effects. These effects demonstrate local variation of material distribution, which is treated as a consequence of interaction between machining parameters and material properties. Current stage of inquiry is concerned with studying the impact of these textural artefacts on structure. Experiments demonstrate that manipulating distribution of matter locally results in more optimal structural performance, it solves printability issues of overhanging geometry without the need for additional supports and provides variation to the surface. The research suggests aesthetic and structural benefits of applying the developed method for mass-customized fabrication. It questions the linear thinking that is predominant in the field of 3D printing and provides an approach that articulates interaction between digital and material logics as it directs the formation of an object that is informed by both.
keywords	digital fabrication; digital craft; texture; ceramic 3D printing
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:58

_id	ecaadesigradi2019_135
id	ecaadesigradi2019_135
authors	Newton, David
year	2019
title	Deep Generative Learning for the Generation and Analysis of Architectural Plans with Small Datasets
doi	https://doi.org/10.52842/conf.ecaade.2019.2.021
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 21-28
summary	The field of generative architectural design has explored a wide range of approaches in the automation of design production, but these approaches have demonstrated limited artificial intelligence. Generative Adversarial Networks (GANs) are a leading deep generative model that use deep neural networks (DNNs) to learn from a set of training examples in order to create new design instances with a degree of flexibility and fidelity that outperform competing generative approaches. Their application to generative tasks in architecture, however, has been limited. This research contributes new knowledge on the use of GANs for architectural plan generation and analysis in relation to the work of specific architects. Specifically, GANs are trained to synthesize architectural plans from the work of the architect Le Corbusier and are used to provide analytic insight. Experiments demonstrate the efficacy of different augmentation techniques that architects can use when working with small datasets.
keywords	generative design; deep learning; artificial intelligence; generative adversarial networks
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:58

For more results click below: