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	ecaade2017_041
id	ecaade2017_041
authors	Fukuda, Tomohiro, Kuwamuro, Yasuyuki and Yabuki, Nobuyoshi
year	2017
title	Optical Integrity of Diminished Reality Using Deep Learning
doi	https://doi.org/10.52842/conf.ecaade.2017.1.241
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
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	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
doi	https://doi.org/10.52842/conf.ecaade.2023.2.419
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
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	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
doi	https://doi.org/10.52842/conf.ecaade.2017.2.693
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
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
doi	https://doi.org/10.52842/conf.ecaade.2021.2.351
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 2, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 351-358
summary	The gradual rise of artificial intelligence (AI) and its increasing visibility among many research disciplines affected Computer-Aided Architectural Design (CAAD). Architectural deep learning (DL) approaches are being developed and published on a regular basis, such as retrieval (Sharma et al. 2017) or design style manipulation (Newton 2019; Silvestre et al. 2016). However, there seems to be no method to evaluate highly constrained spatial configurations for specific architectural domains (such as housing or office buildings) based on basic architectural principles and everyday practices. This paper introduces an automatic constraint-based consistency checker to evaluate the coherency of semantic spatial configurations of housing construction using a small set of design principles to evaluate our DL approaches. The consistency checker informs about the overall performance of a spatial configuration followed by whether it is open/closed and the constraints it didn't satisfy. This paper deals with the relation of spaces processed as mathematically formalized graphs contrary to existing model checking software like Solibri.
keywords	model checking, building information modeling, deep learning, data quality
series	eCAADe
email
last changed	2022/06/07 07:54

_id	acadia17_238
id	acadia17_238
authors	El-Zanfaly, Dina
year	2017
title	A Multisensory Computational Model for Human-Machine Making and Learning
doi	https://doi.org/10.52842/conf.acadia.2017.238
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. 238-247
summary	Despite the advancement of digital design and fabrication technologies, design practices still follow Alberti’s hylomorphic model of separating the design phase from the construction phase. This separation hinders creativity and flexibility in reacting to surprises that may arise during the construction phase. These surprises often come as a result of a mismatch between the sophistication allowed by the digital technologies and the designer’s experience using them. These technologies and expertise depend on one human sense, vision, ignoring other senses that could be shaped and used in design and learning. Moreover, pedagogical approaches in the design studio have not yet fully integrated digital technologies as design companions; rather, they have been used primarily as tools for representation and materialization. This research introduces a multisensory computational model for human-machine making and learning. The model is based on a recursive process of embodied, situated, multisensory interaction between the learner, the machines and the thing-in-the-making. This approach depends heavily on computational making, abstracting, and describing the making process. To demonstrate its effectiveness, I present a case study from a course I taught at MIT in which students built full-scale, lightweight structures with embedded electronics. This model creates a loop between design and construction that develops students’ sensory experience and spatial reasoning skills while at the same time enabling them to use digital technologies as design companions. The paper shows that making can be used to teach design while enabling the students to make judgments on their own and to improvise.
keywords	education, society & culture; fabrication
series	ACADIA
email
last changed	2022/06/07 07:55

_id	ecaade2017_301
id	ecaade2017_301
authors	Kalantari, Saleh and Ghandi, Mona
year	2017
title	Data-responsive Architectural Design Processes
doi	https://doi.org/10.52842/conf.ecaade.2017.2.503.2
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. 503-512
summary	Current advancements in information technology and mechanical components offer incredible new possibilities for innovation in architecture. Many aspects of our physical environment are becoming integrated with information systems, a phenomenon that has been referred to as the "Internet of Things." The implications and applications of this technology are far-reaching, and students who are learning about design in today's environment have a bewildering array of new tools available for their exploration. This paper reviews some of the central concepts of contemporary data-driven design, and describes how these concepts can be used in a pedagogical framework to encourage student innovation. The authors provide details about their work with students in IDR Studios, and highlight some of the innovative design solutions created by students using information-based toolsets. This research provides a pedagogical framework for helping design students to engage with new technological resources as they work to develop the architectural intelligence.
keywords	Adaptive Systems; Internet of Things; Big Data; Data Driven Design Process
series	eCAADe
email
last changed	2022/06/07 07:52

_id	acadia17_366
id	acadia17_366
authors	Lin, Yuming; Huang, Weixin
year	2017
title	Behavior Analysis and Individual Labeling Using Data from Wi-Fi IPS
doi	https://doi.org/10.52842/conf.acadia.2017.366
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. 366- 373
summary	It is fairly important for architects and urban designers to understand how different people interact with the environment. However, traditional investigation methods for studying environmental behavior are quite limited in their coverage of samples and regions, which are not sufficient to delve into the behavioral differences of people. Only recently, the development of indoor positioning systems (IPS) and data-mining techniques has made it possible to collect full-time, full-coverage data for behavioral difference research and individualized identification. In our research, the Wi-Fi IPS system is chosen among the various IPS systems as the data source due to its extensive applicability and acceptable cost. In this paper, we analyzed a 60-day anonymized dataset from a ski resort, collected by a Wi-Fi IPS system with 110 Wi-Fi access points. Combining this with mobile phone data and questionnaires, we revealed some interesting characteristics of tourists from different origins through spatial-temporal behavioral data, and further conducted individual labeling through supervised learning. Through this case study, temporal-spatial behavioral data from an IPS system exhibited great potential in revealing individual characteristics besides exploring group differences, shedding light on the prospect of architectural space personalization.
keywords	design methods; information processing; data mining; big data
series	ACADIA
email
last changed	2022/06/07 07:59

_id	caadria2018_210
id	caadria2018_210
authors	Lin, Yuqiong, Zheng, Jingyun, Yao, Jiawei and Yuan, Philip F.
year	2018
title	Research on Physical Wind Tunnel and Dynamic Model Based Building Morphology Generation Method
doi	https://doi.org/10.52842/conf.caadria.2018.2.165
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 165-174
summary	The change of the building morphology directly affects the surrounding environment, while the evaluation of these environment data becomes the main basis for the genetic iterations of the building morphology. Indeed, due to the complexity of the outdoor natural ventilation, multiple factors in the site could be the main reasons for the change of air flow. Thus, the architect is suggested to take the wind environment as the main morphology generation factor in the early stage of the building design. Based on the research results of 2017 DigitalFUTURE Wind Tunnel Visualization Workshop, a novel self-form-finding method in design infancy has been proposed. This method uses Arduino to carry out the dynamic design of the building model, which can not only connect the sensor to monitor the wind environment data, but also contribute the building model to correlate with the wind environment data in real time. The integration of the Arduino platform and the physical wind tunnel can create the possibility of continuous and real-time physical changes, data collection and wind environment simulation, using quantitative environmental factors to control building morphology, and finally achieve the harmony among the building, environment and human.
keywords	Physical wind tunnel; dynamic model; building morphology generation; environmental performance design; wind environment visualization
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	caadria2018_322
id	caadria2018_322
authors	Lu, Hangxin, Gu, Jiaxi, Li, Jin, Lu, Yao, Müller, Johannes, Wei, Wenwen and Schmitt, Gerhard
year	2018
title	Evaluating Urban Design Ideas from Citizens from Crowdsourcing and Participatory Design
doi	https://doi.org/10.52842/conf.caadria.2018.2.297
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 297-306
summary	Participatory planning aims at engaging multiple stakeholders including citizens in various stages of planning projects. Adopting participatory design approach in the early stage of planning project facilitates the ideation process of citizens. We have implemented a participatory design study during the 2017 Beijing Design Week and have conducted an interactive design project called "Design your perfect Dashilar: You Place it!". Participants including local residents and visitors were asked to redesign the Yangmeizhu street, a historical street located in Dashilar area by rearranging the buildings of residential, commercial, administration, and cultural functionalities. Apart from using digital design tools, questionnaires, interviews, and sensor network were applied to collect personal preferences data. Computational approaches were used to extract features from designs and personal preferences. In this paper, we illustrate the implementation of the participatory design and the possible applications by combining with crowdsourcing. Participatory design data and citizens profiles with personal preferences were analysed and their correlations were computed. By using crowdsourcing and participatory design, this study shows that the digitalization of participatory design with data science perspective can indicate the implicit requirements, needs and design ideas of citizens.
keywords	Participatory design; Crowdsourcing; Human computation; Citizen Design Science; Human Computer Interaction
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	ecaade2017_271
id	ecaade2017_271
authors	Narahara, Taro
year	2017
title	Collective Construction Modeling and Machine Learning: Potential for Architectural Design
doi	https://doi.org/10.52842/conf.ecaade.2017.2.341
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
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	caadria2017_129
id	caadria2017_129
authors	Patt, Trevor Ryan
year	2017
title	Toward Temporal and Punctual Urban Redevelopment in Dynamic, Informal Contexts - An Adaptive Masterplan Driven by Architectural Interventions Using Multiagent Modeling
doi	https://doi.org/10.52842/conf.caadria.2017.221
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. 221-230
summary	This paper presents design research speculating on new planning approaches for informal urban sites that enables coordinated planning to operate within the realm of spontaneous, bottom-up redevelopment. In opposition to the /tabula rasa/ Modernist development, this project reacts to the dynamic metabolism of the village and engages with the rapid turnover of the built environment of the village as a mechanism through which to implement incremental redevelopment. A radical reorientation of the object of masterplanning, this replaces the singular image or document as the guiding authority with a collection of opportunistic adaptations, temporal sequences, and localized procedures. Enabling this approach is a computational approach that analyzes the morphology of the public space network to identify opportunities to address issues in the composition of the village. A multiagent system driven by weighted random walks through the circulation network conducts local analyses of the urban fabric while changes are made and proposes potential modifications to discrete areas. The model simulates the potential for such a planning tool to be used over a long time span and updated with empirically gathered data, having the benefit of flexibility and resilience in the face of the changing and unregulated conditions in the context of informal urbanism.
keywords	generative design; responsive masterplanning; informal urbanism; network analysis; agent-based modeling
series	CAADRIA
email
last changed	2022/06/07 07:59

_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
doi	https://doi.org/10.52842/conf.acadia.2017.474
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
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	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
doi	https://doi.org/10.52842/conf.ecaade.2023.2.327
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
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	acadia19_392
id	acadia19_392
authors	Steinfeld, Kyle
year	2019
title	GAN Loci
doi	https://doi.org/10.52842/conf.acadia.2019.392
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. 392-403
summary	This project applies techniques in machine learning, specifically generative adversarial networks (or GANs), to produce synthetic images intended to capture the predominant visual properties of urban places. We propose that imaging cities in this manner represents the first computational approach to documenting the Genius Loci of a city (Norberg-Schulz, 1980), which is understood to include those forms, textures, colors, and qualities of light that exemplify a particular urban location and that set it apart from similar places. Presented here are methods for the collection of urban image data, for the necessary processing and formatting of this data, and for the training of two known computational statistical models (StyleGAN (Karras et al., 2018) and Pix2Pix (Isola et al., 2016)) that identify visual patterns distinct to a given site and that reproduce these patterns to generate new images. These methods have been applied to image nine distinct urban contexts across six cities in the US and Europe, the results of which are presented here. While the product of this work is not a tool for the design of cities or building forms, but rather a method for the synthetic imaging of existing places, we nevertheless seek to situate the work in terms of computer-assisted design (CAD). In this regard, the project is demonstrative of a new approach to CAD tools. In contrast with existing tools that seek to capture the explicit intention of their user (Aish, Glynn, Sheil 2017), in applying computational statistical methods to the production of images that speak to the implicit qualities that constitute a place, this project demonstrates the unique advantages offered by such methods in capturing and expressing the tacit.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:56

_id	ecaade2017_007
id	ecaade2017_007
authors	Wurzer, Gabriel, Lorenz, Wolfgang E., Cerovsek, Tomo and Martens, Bob
year	2017
title	Contrasting Publications in Design and Scientific Research
doi	https://doi.org/10.52842/conf.ecaade.2017.1.385
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. 385-394
summary	This paper explores the differences between 'design' and 'science' papers published at eCAADe conferences through use of automatic classification. The latter is conducted using a set of differentiating criteria (e.g. number of figures determines a paper to be either 'design' or 'science') which are calibrated with the help of a manual selection of papers from eCAADe 2015 as ground truth. Results show that we predict 83% of the papers correctly; experiments using data from eCAADe 2014 until eCAADe 2016 furthermore show the stability of our results. However, we are not so much after the development this automatic classification but rather want to characterize the two research cultures of design and science. This is achieved by taking a close look at the differentiating criteria, which can inform tools such as ProceeDings over possible future directions and adaptation needs.
keywords	Differentiation; Design; Science; ProceeDings; CumInCAD
series	eCAADe
email
last changed	2022/06/07 07:57

_id	caadria2017_131
id	caadria2017_131
authors	Abe, U-ichi, Hotta, Kensuke, Hotta, Akito, Takami, Yosuke, Ikeda, Hikaru and Ikeda, Yasushi
year	2017
title	Digital Construction - Demonstration of Interactive Assembly Using Smart Discrete Papers with RFID and AR codes
doi	https://doi.org/10.52842/conf.caadria.2017.075
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. 75-84
summary	This paper proposes and examines a new way of cooperation between human workers and machine intelligence in architectural scale construction. For the transfer of construction information between the physical and digital world, mature technologies such as Radio Frequency IDentifier (RFID), and emerging technologies like Augmented Reality (AR) are used in parallel to supplement each other. Dynamic data flow is implemented to synchronize digital and physical models by following the ID signatures of individual building parts. The contributions of this paper includes the demonstration of current technological limitations, and the proposal of a hybrid system between human and computer, which is tested in order to explore the possibilities of digitally enhanced construction methods.
keywords	Digital Construction; Augmented Reality; Human-Machine interaction
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	ijac201715301
id	ijac201715301
authors	Afsari, Kereshmeh; Charles Eastman and Dennis Shelden
year	2017
title	Building Information Modeling data interoperability for Cloud-based collaboration: Limitations and opportunities
source	International Journal of Architectural Computing vol. 15 - no. 3, 187-202
summary	Collaboration within Building Information Modeling process is mainly based on the manual transfer of document files in either vendor-specific formats or neutral format using Industry Foundation Classes. However, since the web enables Cloud-based Building Information Modeling services, it provides an opportunity to exchange data with web technologies. Alternative data sharing solutions include the federation of Building Information Modeling models and an interchange hub for data exchange in real time. These solutions face several challenges, are vendor locked, and integrate Building Information Modeling applications to a third new system. The main objective of this article is to investigate current limitations as well as opportunities of Cloud interoperability to outline a framework for a loosely coupled network-based Building Information Modeling data interoperability. This study explains that Cloud-Building Information Modeling data exchange needs to deploy major components of Cloud interoperability such as Cloud application programming interfaces, data transfer protocols, data formats, and standardization to redefine Building Information Modeling data flow in Cloud-based applications and to reshape collaboration process.
keywords	Building Information Modeling, Cloud, data exchange, interoperability, Industry Foundation Classes
series	journal
email
last changed	2019/08/07 14:03

_id	ecaade2017_021
id	ecaade2017_021
authors	Agirbas, Asli
year	2017
title	The Use of Simulation for Creating Folding Structures - A Teaching Model
doi	https://doi.org/10.52842/conf.ecaade.2017.1.325
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
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_52
id	acadia17_52
authors	Ajlouni, Rima
year	2017
title	Simulation of Sound Diffusion Patterns of Fractal-Based Surface Profiles
doi	https://doi.org/10.52842/conf.acadia.2017.052
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. 52-61
summary	Acoustical design is one of the most challenging aspects of architecture. A complex system of competing influences (e.g., space geometry, size, proportion, material properties, surface detail, etc.) contribute to shaping the quality of the auditory experience. In particular, architectural surfaces affect the way that sound reflections propagate through space. By diffusing the reflected sound energy, surface designs can promote a more homogeneous auditory atmosphere by mitigating sharp and focused reflections. One of the challenges with designing an effective diffuser is the need to respond to a wide band of sound wavelengths, which requires the surface profile to precisely encode a range of detail sizes, depths and angles. Most of the available sound diffusers are designed to respond to a narrow band of frequencies. In this context, fractal-based surface designs can provide a unique opportunity for mitigating such limitations. A key principle of fractal geometry is its multilevel hierarchical order, which enables the same pattern to occur at different scales. This characteristic makes it a potential candidate for diffusing a wider band of sound wavelengths. However, predicting the reflection patterns of complicated fractal-based surface designs can be challenging using available acoustical software. These tools are often costly, complicated and are not designed for predicting early sound propagation paths. This research argues that writing customized algorithms provides a valuable, free and efficient alternative for addressing targeted acoustical design problems. The paper presents a methodology for designing and testing a customized algorithm for predicting sound diffusion patterns of fractal-based surfaces. Both quantitative and qualitative approaches were used to develop the code and evaluate the results.
keywords	design methods; information processing; simulation & optimization; data visualization
series	ACADIA
email
last changed	2022/06/07 07:54

_id	acadia17_82
id	acadia17_82
authors	Andreani, Stefano; Sayegh, Allen
year	2017
title	Augmented Urban Experiences: Technologically Enhanced Design Research Methods for Revealing Hidden Qualities of the Built Environment
doi	https://doi.org/10.52842/conf.acadia.2017.082
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. 82-91
summary	The built environment is a complex juxtaposition of static matter and dynamic flows, tangible objects and human experiences, physical realities and digital spaces. This paper offers an alternative understanding of those dichotomies by applying experimental design research strategies that combine objective quantification and subjective perception of urban contexts. The assumption is that layers of measurable datasets can be afforded with personal feedback to reveal "hidden" characteristics of cities. Drawing on studies from data and cognitive sciences, the proposed method allows us to analyze, quantify and visualize the individual experience of the built environment in relation to different urban qualities. By operating in between the scientific domain and the design realm, four design research experiments are presented. Leveraging augmenting and sensing technologies, these studies investigate: (1) urban attractors and user attention, employing eye-tracking technologies during walking; (2) urban proxemics and sensory experience, applying proximity sensors and EEG scanners in varying contexts; (3) urban mood and spatial perception, using mobile applications to merge tangible qualities and subjective feelings; and (4) urban vibe and paced dynamics, combining vibration sensing and observational data for studying city beats. This work demonstrates that, by adopting a multisensory and multidisciplinary approach, it is possible to gain a more human-centered, and perhaps novel understanding of the built environment. A lexicon of experimented urban situations may become a reference for studying different typologies of environments from the user experience, and provide a framework to support creative intuition for the development of more engaging, pleasant, and responsive spaces and places.
keywords	design methods; information processing; art and technology; hybrid practices
series	ACADIA
email
last changed	2022/06/07 07:54

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