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	ecaade2023_259
id	ecaade2023_259
authors	Sonne-Frederiksen, Povl Filip, Larsen, Niels Martin and Buthke, Jan
year	2023
title	Point Cloud Segmentation for Building Reuse - Construction of digital twins in early phase building reuse projects
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 327–336
doi	https://doi.org/10.52842/conf.ecaade.2023.2.327
summary	Point cloud processing has come a long way in the past years. Advances in computer vision (CV) and machine learning (ML) have enabled its automated recognition and processing. However, few of those developments have made it through to the Architecture, Engineering and Construction (AEC) industry. Here, optimizing those workflows can reduce time spent on early-phase projects, which otherwise could be spent on developing innovative design solutions. Simplifying the processing of building point cloud scans makes it more accessible and therefore, usable for design, planning and decision-making. Furthermore, automated processing can also ensure that point clouds are processed consistently and accurately, reducing the potential for human error. This work is part of a larger effort to optimize early-phase design processes to promote the reuse of vacant buildings. It focuses on technical solutions to automate the reconstruction of point clouds into a digital twin as a simplified solid 3D element model. In this paper, various ML approaches, among others KPConv Thomas et al. (2019), ShapeConv Cao et al. (2021) and Mask-RCNN He et al. (2017), are compared in their ability to apply semantic as well as instance segmentation to point clouds. Further it relies on the S3DIS Armeni et al. (2017), NYU v2 Silberman et al. (2012) and Matterport Ramakrishnan et al. (2021) data sets for training. Here, the authors aim to establish a workflow that reduces the effort for users to process their point clouds and obtain object-based models. The findings of this research show that although pure point cloud-based ML models enable a greater degree of flexibility, they incur a high computational cost. We found, that using RGB-D images for classifications and segmentation simplifies the complexity of the ML model but leads to additional requirements for the data set. These can be mitigated in the initial process of capturing the building or by extracting the depth data from the point cloud.
keywords	Point Clouds, Machine Learning, Segmentation, Reuse, Digital Twins
series	eCAADe
email
last changed	2023/12/10 10:49

_id	acadia20_382
id	acadia20_382
authors	Hosmer, Tyson; Tigas, Panagiotis; Reeves, David; He, Ziming
year	2020
title	Spatial Assembly with Self-Play Reinforcement Learning
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 382-393.
doi	https://doi.org/10.52842/conf.acadia.2020.1.382
summary	We present a framework to generate intelligent spatial assemblies from sets of digitally encoded spatial parts designed by the architect with embedded principles of prefabrication, assembly awareness, and reconfigurability. The methodology includes a bespoke constraint-solving algorithm for autonomously assembling 3D geometries into larger spatial compositions for the built environment. A series of graph-based analysis methods are applied to each assembly to extract performance metrics related to architectural space-making goals, including structural stability, material density, spatial segmentation, connectivity, and spatial distribution. Together with the constraint-based assembly algorithm and analysis methods, we have integrated a novel application of deep reinforcement (RL) learning for training the models to improve at matching the multiperformance goals established by the user through self-play. RL is applied to improve the selection and sequencing of parts while considering local and global objectives. The user’s design intent is embedded through the design of partial units of 3D space with embedded fabrication principles and their relational constraints over how they connect to each other and the quantifiable goals to drive the distribution of effective features. The methodology has been developed over three years through three case study projects called ArchiGo (2017–2018), NoMAS (2018–2019), and IRSILA (2019-2020). Each demonstrates the potential for buildings with reconfigurable and adaptive life cycles.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2023_44
id	ecaade2023_44
authors	Mayrhofer-Hufnagl, Ingrid and Ennemoser, Benjamin
year	2023
title	From Linear to Manifold Interpolation: Exemplifying the paradigm shift through interpolation
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 419–429
doi	https://doi.org/10.52842/conf.ecaade.2023.2.419
summary	The advent of artificial intelligence, specifically neural networks, has marked a significant turning point in the field of computation. During such transformative times, we are often faced with a dearth of appropriate vocabulary, which forces us to rely on existing terms, regardless of their inadequacy. This paper argues that the term “interpolation,” typically used in deep learning (DL), is a prime example of this phenomenon. It is not uncommon for beginners to misunderstand its meaning, as DL pioneer Francois Chollet (2017) has noted. This misreading is especially true in the discipline of architecture, and this study aims to demonstrate how the meaning of “interpolation” has evolved in the second digital turn. We begin by illustrating, using 2D data, the difference between linear interpolation in the context of topological figures and its use in DL algorithms. We then demonstrate how 3DGANs can be employed to interpolate across different topologies in complex 3D space, highlighting the distinction between linear and manifold interpolation. In both 2D and 3D examples, our results indicate that the process does not involve continuous morphing but instead resembles the piecing together of a jigsaw puzzle to form many parts of a larger ambient space. Our study reveals how previous architectural research on DL has employed the term “interpolation” without clarifying the crucial differences from its use in the first digital turn. We demonstrate the new possibilities that manifold interpolation offers for architecture, which extend well beyond parametric variations of the same topology.
keywords	Interpolation, 3D Generative Adversarial Networks, Deep Learning, Hybrid Space
series	eCAADe
email
last changed	2023/12/10 10:49

_id	acadia17_474
id	acadia17_474
authors	Peng, Wenzhe; Zhang, Fan; Nagakura, Takehiko
year	2017
title	Machines’ Perception of Space: Employing 3D Isovist Methods and a Convolutional Neural Network in Architectural Space Classification
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 474- 481
doi	https://doi.org/10.52842/conf.acadia.2017.474
summary	Simple and common architectural elements can be combined to create complex spaces. Different spatial compositions of elements define different spatial boundaries, and each produces a unique local spatial experience to observers inside the space. Therefore an architectural style brings about a distinct spatial experience. While multiple representation methods are practiced in the field of architecture, there lacks a compelling way to capture and identify spatial experiences. Describing an observer’s spatial experiences quantitatively and efficiently is a challenge. In this paper, we propose a method that employs 3D isovist methods and a convolutional neural network (CNN) to achieve recognition of local spatial compositions. The case studies conducted validate that this methodology works well in capturing and identifying local spatial conditions, illustrates the pattern and frequency of their appearance in designs, and indicates peculiar spatial experiences embedded in an architectural style. The case study used small designs by Mies van der Rohe and Aldo van Eyck. The contribution of this paper is threefold. First, it introduces a sampling method based on 3D Isovist that generates a 2D image that can be used to represent a 3D space from a specific observation point. Second, it employs a CNN model to extract features from the sampled images, then classifies their corresponding space. Third, it demonstrates a few case studies where this space classification method is applied to different architectural styles.
keywords	design methods; information processing; AI; machine learning; computer vision; representation
series	ACADIA
email
last changed	2022/06/07 08:00

_id	ecaade2017_009
id	ecaade2017_009
authors	Takizawa, Atsushi and Furuta, Airi
year	2017
title	3D Spatial Analysis Method with First-Person Viewpoint by Deep Convolutional Neural Network with Omnidirectional RGB and Depth Images
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 2, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 693-702
doi	https://doi.org/10.52842/conf.ecaade.2017.2.693
summary	The fields of architecture and urban planning widely apply spatial analysis based on images. However, many features can influence the spatial conditions, not all of which can be explicitly defined. In this research, we propose a new deep learning framework for extracting spatial features without explicitly specifying them and use these features for spatial analysis and prediction. As a first step, we establish a deep convolution neural network (DCNN) learning problem with omnidirectional images that include depth images as well as ordinary RGB images. We then use these images as explanatory variables in a game engine to predict a subjects' preference regarding a virtual urban space. DCNNs learn the relationship between the evaluation result and the omnidirectional camera images and we confirm the prediction accuracy of the verification data.
keywords	Space evaluation; deep convolutional neural network; omnidirectional image; depth image; Unity; virtual reality
series	eCAADe
email
last changed	2022/06/07 07:56

_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
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
doi	https://doi.org/10.52842/conf.ecaade.2021.2.351
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	ecaade2017_037
id	ecaade2017_037
authors	Hassan Khalil, Mohamed
year	2017
title	Learning by Merging 3D Modeling for CAAD with the Interactive Applications - Bearing walls, Vaults, Domes as Case study
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 1, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 353-362
doi	https://doi.org/10.52842/conf.ecaade.2017.1.353
summary	The development and the innovation of tools, techniques and digital applications represent a challenge for those who are in charge of architectural education to keep up with this development. This is because these techniques provide potentials that are not available in the traditional method of teaching. This raises an important question: can these tools and techniques help to achieve the targeted outcomes of education? This research paper discusses how to integrate both digital 3D models, of CAAD, and interactive applications for the development of architectural education curriculum. To test this, a case study has been conducted on the subject of building construction, for the second year at the faculty of engineering, specifically, the bearing walls construction system. In addition, this study has been divided into three parts. Through the first part, the scientific content of the curriculum, which tackles the bearing walls, has been prepared. The second part shows how to convert the scientific content into an interactive content in which the students learn through the experiment and the simulation of the traditional construction methods as the students a acquire construction skills and the ability to imagine different structural complexities. The third part includes the creation of both the application and the software containing the interactive curriculum. Workshop for the students has been held as a case study to test the effectiveness of this development and to recognize the pros and cons. The results confirmed the importance of integrating this applications into architectural education.
keywords	CAAD; 3D modeling ; Building Construction; Interactive applications; Bearing walls systems
series	eCAADe
email
last changed	2022/06/07 07:49

_id	ijac201715105
id	ijac201715105
authors	Nahmad Vazque, Alicia and Wassim Jabi
year	2017
title	Investigations in robotic-assisted design: Strategies for symbiotic agencies in material-directed generative design processes
source	International Journal of Architectural Computing vol. 15 - no. 1, 70-86
summary	The research described in this article utilises a phase-changing material, three-dimensional scanning technologies and a six-axis industrial robotic arms as vehicles to enable a novel framework where robotic technology is utilised as an ‘amplifier’ of the design process to realise geometries that derive from both constructive visions and architectural visions through iterative feedback loops between them. The robot in this scenario is not a fabrication tool but the enabler of an environment where the material, robotic and human agencies interact. This article describes the exploratory research for the development of a dialogic design process, sets the framework for its implementation, carries out an evaluation based on designer use and concludes with a set of observations. One of the main findings of this article is that a deeper collaboration that acknowledges the potential of these tools, in a learning-by-design method, can lead to new choreographies for architectural design and fabrication.
keywords	Robotic fabrication, human-machine networks, digital design, agency
series	other
type	normal paper
email
last changed	2019/08/02 08:28

_id	ecaade2017_220
id	ecaade2017_220
authors	Quartara, Andrea and Figliola, Angelo
year	2017
title	Tangible Computing - Manufacturing of Intertwined Logics
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 2, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 115-122
doi	https://doi.org/10.52842/conf.ecaade.2017.2.115
summary	This paper explores the process of digital materialization through robotic fabrication techniques by presenting three wooden projects. The analysis of the case studies is oriented to underline the impact that computation had on architectural construction due to its methodological and instrumental innovations over the last decades. The absorption of computing and digital fabrication logics within the discipline is explored from either an architectural point of view and from the improvements related to automation of the constructive process. On the one hand the case studies are caught because of the desire to expand material complexity and, on the other hand because of the integration with other technological systems. The narrative allows gathering pros and cons in three different investigative macro areas: material culture, methodological oversights, and operative setbacks coming from digital machine and communicational constraints. This analytical investigation helps the definition of a new pathway for future researches, looking forward the assimilation of digital materiality learning in building construction.
keywords	computational design; file-to-factory; large-scale robotic woodworking; new production methods
series	eCAADe
email
last changed	2022/06/07 08:00

_id	cf2019_009
id	cf2019_009
authors	Veloso, Pedro; Jinmo Rhee and Ramesh Krishnamurti
year	2019
title	Multi-agent space planning: a literature review (2008-2017)
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, pp. 52-74
summary	In this paper we review the research on multi-agent space planning (MASP) during the period of 2008-2017. By MASP, we refer to space planning (SP) methods based on online mobile agents that map local perceptions to actions in the environment, generating spatial representation. We group two precedents and sixteen recent MASP prototypes into three categories: (1) agents as moving spatial units, (2) agents that occupy a space, and (3) agents that partition a space. In order to compare the prototypes, we identify the occurrence of features in terms of representation, objectives, and control procedures. Upon analysis of occurrences and correlations of features in the types, we present gaps and challenges for future MASP research. We point to the limits of current systems to solve spatial conflicts and to incorporate architectural knowledge. Finally, we suggest that behavioral learning offers a promising path for robust and autonomous MASP systems in the architectural domain.
keywords	Space planning; Agent-based modeling; Multi-agent systems; Generative systems
series	CAAD Futures
email
last changed	2019/07/29 14:08

_id	ecaade2017_183
id	ecaade2017_183
authors	Wendell, Augustus and Altin, Ersin
year	2017
title	Learning Space - Incorporating spatial simulations in design history coursework
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 1, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 261-266
doi	https://doi.org/10.52842/conf.ecaade.2017.1.261
summary	Art and architectural history education has long relied on photographic imagery. The geography of architectural history often demands an analog representation for the built form and photographic recordings have long been the widely adopted standard. In many cases, specific buildings have been taught for generations based on a handful of historical exposures. The impact of this precedent is an imperfect and highly privileged conception of architectural forms. Students learn only of a particular viewpoint of any given building, rather than understanding the building as a whole. Augmenting the tradition of select and static imagery in the classroom with new technologies can create a more comprehensive understanding of architectural precedents. This paper discusses an experiment conducted in Spring 2017 in presenting an architectural case study to a history class using a Virtual Reality 3D experience in comparison to a set of canonical photographs.
keywords	Unreal Engine; Virtual Reality; Photography; 3D; Education
series	eCAADe
email
last changed	2022/06/07 07:58

_id	ecaade2017_046
id	ecaade2017_046
authors	Ezzat, Mohammed
year	2017
title	Implementing the General Theory for Finding the Lightest Manmade Structures Using Voronoi and Delaunay
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 2, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 241-250
doi	https://doi.org/10.52842/conf.ecaade.2017.2.241
summary	In previous efforts, the foundation of a general theory that searches for finding lightest manmade structures using the Delaunay diagram or its dual the Voronoi diagram was set (Ezzat, 2016). That foundation rests on using a simple and computationally cheap Centroid method. The simple Centroid method is expected to play a crucial role in the more sophisticated general theory. The Centroid method was simply about classifying a cloud of points that represents specific load case/s stresses on any object. That classification keeps changing using mathematical functions until optimal structures are found. The point cloud then is classified into different smaller points' groups; each of these groups was represented by a single positional point that is related to the points' group mean. Those representational points were used to generate the Delaunay or Voronoi diagrams, which are tested structurally to prove or disprove the optimality of the classification. There was not a single optimized classification out of that process but rather a family of them. The point cloud was the input to the centroid structural optimization, and the family of the optimized centroid method is the input to our proposed implementation of the general theory (see Figure 1). The centroid method produced promising optimized structures that performed from five to ten times better than the other tested variations. The centroid method was implemented using the two structural plugins of Millipede and Karmaba, which run under the environment of the Grasshopper plugin. The optimization itself is done using the grasshopper's component of Galapagos.
keywords	Agent-based structural optimization; Evolutionary conceptual tree representation; Heuristic structural knowledge acquisition ; Centroid structural classification optimization method
series	eCAADe
email
last changed	2022/06/07 07:55

_id	caadria2017_002
id	caadria2017_002
authors	Haeusler, M. Hank, Muehlbauer, Manuel, Bohnenberger, Sascha and Burry, Jane
year	2017
title	Furniture Design Using Custom-Optimised Structural Nodes
source	P. Janssen, P. Loh, A. Raonic, M. A. Schnabel (eds.), Protocols, Flows, and Glitches - Proceedings of the 22nd CAADRIA Conference, Xi'an Jiaotong-Liverpool University, Suzhou, China, 5-8 April 2017, pp. 841-850
doi	https://doi.org/10.52842/conf.caadria.2017.841
summary	Additive manufacturing techniques and materials have evolved rapidly during the last decade. Applications in architecture, engineering and construction are getting more attention as 3D printing is trying to find its place in the industry. Due to high material prices for metal 3d printing and in-homogenous material behaviour in printed plastic, 3D printing has not yet had a very significant impact at the scale of buildings. Limitations on scale, cost, and structural performance have also hindered the advancement of the technology and research up to this point. The research presented here takes a case study for the application of 3D printing at a furniture scale based on a novel custom optimisation approach for structural nodes. Through the concentration of non-standard geometry on the highly complex custom optimised nodes, 3D printers at industrial product scale could be used for the additive manufacture of the structural nodes. This research presents a design strategy with a digital process chain using parametric modeling, virtual prototyping, structural simulation, custom optimisation and additive CAD/CAM for a digital workflow from design to production. Consequently, the digital process chain for the development of structural nodes was closed in a holistic manner at a suitable scale.
keywords	Digital fabrication; node optimisation; structural performance; 3D printing; carbon fibre.
series	CAADRIA
email
last changed	2022/06/07 07:49

_id	cf2017_567
id	cf2017_567
authors	Kim, Ikhwan; Lee, Injung; Lee, Ji-Hyun
year	2017
title	The Expansion of Virtual Landscape in Digital Games: Classification of Virtual Landscapes Through Five principles
source	Gülen Çagdas, Mine Özkar, Leman F. Gül and Ethem Gürer (Eds.) Future Trajectories of Computation in Design [17th International Conference, CAAD Futures 2017, Proceedings / ISBN 978-975-561-482-3] Istanbul, Turkey, July 12-14, 2017, pp. 567-584.
summary	This research established classification system which contains five principles and variables to classify the types of the virtual landscape in digital games. The principles of the classification are Story, Space Shape, Space and Action Dimension, User Complexity and Interaction Level. With this classification system, our research group found the most representative types of virtual landscape in the digital game market through 1996 to 2016. Although mathematically there can be 288 types of virtual landscape, only 68 types have been used in the game market in recent twenty years. Among the 68 types, we defined 3 types of virtual landscape as the most representative types based on the growth curve and a number of cases. Those three representative types of virtual landscapes are Generating / Face / 3D-3D / Single / Partial, Providing / Chain / 3D-3D / Single / Partial and Providing / Linear / 2D-2D / Single / Partial. With the result, the researchers will be able to establish the virtual landscape design framework for the future research.
keywords	Digital Game, Virtual Landscape, Game Design, Game Classification
series	CAAD Futures
email
last changed	2017/12/01 14:38

_id	sigradi2017_053
id	sigradi2017_053
authors	Pena Martinez, Andressa Carmo; Douglas Lopes de Souza, Denise Mônaco dos Santos, Denise Mônaco dos Santos, Marianna Auxiliadora Dias Martins
year	2017
title	Simulação de desempenho estrutural baseada na prototipagem rápida com impressão 3d [Structural performance simulation based on 3D printing for rapid prototyping]
source	SIGraDi 2017 [Proceedings of the 21th Conference of the Iberoamerican Society of Digital Graphics - ISBN: 978-956-227-439-5] Chile, Concepción 22 - 24 November 2017, pp.367-373
summary	This paper presents part of the research on simulation of structural performance and aims to study the mechanical behavior of polymers, ABS and PLA in the form of thermoplastic filaments, commonly used in affordable 3D printers. It presents the preliminary results for the evaluation of the mechanical behavior of ABS and PLA in the light of ASTM E2954 and ASTM D790 standards, which establish test methods for axial compression and three-point flexure for plastic and polymer matrix.
keywords	3d printing; structural performance; rapid prototyping; computatuional simulation
series	SIGRADI
email
last changed	2021/03/28 19:59

_id	ecaade2017_041
id	ecaade2017_041
authors	Fukuda, Tomohiro, Kuwamuro, Yasuyuki and Yabuki, Nobuyoshi
year	2017
title	Optical Integrity of Diminished Reality Using Deep Learning
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 1, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 241-250
doi	https://doi.org/10.52842/conf.ecaade.2017.1.241
summary	A new method is proposed to improve diminished reality (DR) simulations to allow the demolition and removal of entire buildings in large-scale spaces. Our research goal was to obtain optical integrity by using a scientific and reliable simulation approach. Further, we tackled presumption of the texture of the background sky by applying deep learning. Our approach extracted the background sky using information from the actual sky obtained from a photographed image. This method comprised two steps: (1) detection of the sky area from the image through image segmentation and (2) creation of an image of the sky through image inpainting. The deep convolutional neural networks developed by us to train and predict images were evaluated to be feasible and effective.
keywords	Diminished Reality; Optical Integrity; Deep Learning; Augmented Reality; Landscape assessment
series	eCAADe
email
last changed	2022/06/07 07:50

_id	ecaade2017_271
id	ecaade2017_271
authors	Narahara, Taro
year	2017
title	Collective Construction Modeling and Machine Learning: Potential for Architectural Design
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 2, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 341-348
doi	https://doi.org/10.52842/conf.ecaade.2017.2.341
summary	Recently, there are significant developments in artificial intelligence using advanced machine learning algorithms such as deep neural networks. These new methods can defeat human expert players in strategy-based board games such as Go and video games such as Breakout. This paper suggests a way to incorporate such advanced computing methods into architectural design through introducing a simple conceptual design project inspired by computational interpretations of wasps' collective constructions. At this stage, the paper's intent is not to introduce a practical and fully finished tool directly useful for architectural design. Instead, the paper proposes an example of a program that can potentially become a conceptual framework for incorporating such advanced methods into architectural design.
keywords	Design tools; Stigmergy; Machine learning
series	eCAADe
email
last changed	2022/06/07 07:58

_id	ecaade2017_021
id	ecaade2017_021
authors	Agirbas, Asli
year	2017
title	The Use of Simulation for Creating Folding Structures - A Teaching Model
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 1, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 325-332
doi	https://doi.org/10.52842/conf.ecaade.2017.1.325
summary	In architectural education, the demand for creating forms with a non-Euclidean geometry, which can only be achieved by using the computer-aided design tools, is increasing. The teaching of this subject is a great challenge for both students and instructors, because of the intensive nature of architecture undergraduate programs. Therefore, for the creation of those forms with a non-Euclidean geometry, experimental work was carried out in an elective course based on the learning visual programming language. The creation of folding structures with form-finding by simulation was chosen as the subject of the design production which would be done as part of the content of the course. In this particular course, it was intended that all stages should be experienced, from the modeling in the virtual environment to the digital fabrication. Hence, in their early years of architectural education, the students were able to learn versatile thinking by experiencing, simultaneously, the use of simulation in the environment of visual programming language, the forming space by using folding structures, the material-based thinking and the creation of their designs suitable to the digital fabrication.
keywords	Folding Structures; CAAD; Simulation; Form-finding; Architectural Education
series	eCAADe
email
last changed	2022/06/07 07:54

_id	acadia17_92
id	acadia17_92
authors	Anzalone, Phillip; Bayard, Stephanie; Steenblik, Ralph S.
year	2017
title	Rapidly Deployed and Assembled Tensegrity System: An Augmented Design Approach
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 92-101
doi	https://doi.org/10.52842/conf.acadia.2017.092
summary	The Rapidly Deployable and Assembled Tensegrity (RDAT) project enables the efficient automated design and deployment of differential-geometry tensegrity structures through computation-driven design-to-installation workflow. RDAT employs the integration of parametric and solid-modeling methods with production by streamlining computer numerically controlled manufacturing through novel detailing and production techniques to develop an efficient manufacturing and assembly system. The RDAT project emerges from the Authors' research in academia and professional practice focusing on computationally produced full-scale performative building systems and their innovative uses in the building and construction industry.
keywords	design methods; information processing; AI; machine learning; form finding; VR; AR; mixed reality
series	ACADIA
email
last changed	2022/06/07 07:54

_id	sigradi2017_069
id	sigradi2017_069
authors	Briones Lazo, Carolina; Carolina Soto Ogueta
year	2017
title	La enseñanza de BIM en Chile, el desafío de un cambio de enfoque centrado en la metodología por sobre la tecnología. [BIM education in Chile, the challenge of a shift of focus centered on methodology over technology.]
source	SIGraDi 2017 [Proceedings of the 21th Conference of the Iberoamerican Society of Digital Graphics - ISBN: 978-956-227-439-5] Chile, Concepción 22 - 24 November 2017, pp.470-478
summary	This article presents the level of adoption of BIM in Chile referring to recent studies carried out in the country, demonstrating that there has not been a significant increase in the use of this methodology by the industry. According to the analysis of international cases on educational frameworks, the authors argue that the development of a national education strategy for BIM with a focus on defining BIM capabilities required to assume the national mandate 2020, along with promoting collaborative work environments and active learning methodologies would be very beneficial.
keywords	Building Information Modelling; Metodología BIM; Adopción de BIM; Estrategia de enseñanza de BIM.
series	SIGRADI
email
last changed	2021/03/28 19:58

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