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	acadia17_426
id	acadia17_426
authors	Moorman, Andrew
year	2017
title	Pattern Making and Learning: Non-Routine Practices in Generative Design
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. 426- 435
doi	https://doi.org/10.52842/conf.acadia.2017.426
summary	We now witness an upsurge in mainstream generative design tools fortified by simulation that speed up the concealed linear synthesis of optimized design alternatives. In pursuit of optimality, these tools saturate local machines or cloud servers with analysis and design iteration data, only to discard it once the procedure has concluded. Largely absent, however, are tools for an active, adaptive relationship with design exploration and the reuse of corresponding design data and metadata. In Pattern Making and Pattern Learning, we propose that these characteristics are mutually beneficial. This paper presents a series of revisions to the optimization framework for routine design synthesis that examine a potential symbiosis between the production of large datasets (big data) and non-routine practices of making in design. Our engagement with iterative design exercises is twofold: as a supply of computer-generated design information to foster user intuition and explore the design space on non-objective terms, and as a supply of human-generated design information to learn artifacts of user preference in the interest of design software personalization. These concepts are applied to the generation of functionally graded patterning in chair design, combining methods of physical production with programmable sheet material behavior through a custom interactive synthesis framework.
keywords	design methods; information processing; ai & machine learning; simulation & optimization; generative system
series	ACADIA
email
last changed	2022/06/07 07:58

_id	acadia17_238
id	acadia17_238
authors	El-Zanfaly, Dina
year	2017
title	A Multisensory Computational Model for Human-Machine Making and Learning
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
doi	https://doi.org/10.52842/conf.acadia.2017.238
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	acadia17_284
id	acadia17_284
authors	Hu, Zhengrong; Park, Ju Hong
year	2017
title	HalO [Indoor Positioning Mobile Platform]: A Data-Driven, Indoor-Positioning System With Bluetooth Low Energy Technology To Datafy Indoor Circulation And Classify Social Gathering Patterns For Assisting Post Occupancy Evaluation
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. 284-291
doi	https://doi.org/10.52842/conf.acadia.2017.284
summary	Post-Occupancy Evaluation (POE) as an integrated field between architecture and sociology has created practical guidelines for evaluating indoor human behavior within a built environment. This research builds on recent attempts to integrate datafication and machine learning into POE practices that may one day assist Building Information Modeling (BIM) and multi-agent modeling. This research is based on two premises: 1) that the proliferation of Bluetooth Low Energy (BLE) technology allows us to collect a building user’s data cost-effectively and 2) that the growing application of machine learning algorithms allows us to process, analyze and synthesize data efficiently. This study illustrates that the mobile platform HalO can serve as a generic tool for datafication and automation of data analysis of the movement of a building user. In this research, the iOS mobile application HalO, combined with BLE beacons enable building providers (architects, developers, engineers and facility managers etc.) to collect the user’s indoor location data. Triangulation was used to pinpoint the user’s indoor positions, and k-means clustering was applied to classify users into different gathering groups. Through four research procedures—Design Intention Analysis, Data Collection, Data Storage and Data Analysis—the visualized and classified data helps building providers to better evaluate building performance, optimize building operations and improve the accuracy of simulations.
keywords	design methods; information processing; data mining; IoT; AI; machine learning
series	ACADIA
email
last changed	2022/06/07 07:49

_id	caadria2024_87
id	caadria2024_87
authors	Li, Jiongye and Stouffs, Rudi
year	2024
title	Distribution of Carbon Storage and Potential Strategies to Enhance Carbon Sequestration Capacity in Singapore: A Study Based on Machine Learning Simulation and Geospatial Analysis
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 89–98
doi	https://doi.org/10.52842/conf.caadria.2024.2.089
summary	The expansion of urbanization leads to significant changes in land use, consequently affecting carbon storage. This research aims to investigate the carbon loss due to land use alterations and proposes strategies for mitigation. Utilizing existing land use data from 2017 and 2022, along with simulated data for 2025 generated by an ANN model and Cellular Automata, we identified changes in land use. These changes were then correlated with variations in carbon storage, both gains and losses. Our findings reveal a significant loss of 36,859 metric tons of carbon storage from 2017 to 2022. The projection for 2025 estimates a further reduction, reaching a total loss of 83,409 metric tons. By employing the LISA method, we identified that low-carbon storage zones are concentrated in the southeast region of the research site. By overlaying these zones with areas of carbon storage loss, we pinpointed regions severely affected by carbon depletion. Consequently, we propose that mitigation strategies should be imperatively implemented in these identified areas to counteract the trend of carbon storage loss. This approach offers urban planners a solution to identify areas experiencing carbon storage decline. Moreover, our research methodology provides a novel framework for scholars studying similar carbon issues.
keywords	land use and land cover (LULC) changes, simulated LULC, machine learning model, carbon storage changes, GIS
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	ecaade2017_083
id	ecaade2017_083
authors	Markusiewicz, Jacek and Krê¿lik, Adrian
year	2017
title	Human-driven and machine-driven decisions in urban design and architecture - A comparison of two different methods in finding solutions to a complex problem
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. 505-514
doi	https://doi.org/10.52842/conf.ecaade.2017.1.505
summary	The authors of the paper research the aspects of two approaches in human-computer collaboration to solve an urban scale problem: positioning a new cycling-pedestrian bridge in the city of Warsaw. The first approach is a machine-driven stochastic optimization combined with the shortest walk algorithm; the second one is a human-centered process involving an interactive table as a way of communication and data input. Both approaches were explored as part of a one-week student workshop. The article covers the undertaken techniques in detail and presents the outcomes of both studies. It concludes with a reflection on the necessity to inspire a discussion about the future of the architecture among apprentices of the profession: with all the potential threats and opportunities deriving from computer automation.
keywords	interface; TUI; optimization; PSO; generative design; programming
series	eCAADe
email
last changed	2022/06/07 07:59

_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	acadia19_392
id	acadia19_392
authors	Steinfeld, Kyle
year	2019
title	GAN Loci
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
doi	https://doi.org/10.52842/conf.acadia.2019.392
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	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_301
id	ecaade2017_301
authors	Kalantari, Saleh and Ghandi, Mona
year	2017
title	Data-responsive Architectural Design Processes
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
doi	https://doi.org/10.52842/conf.ecaade.2017.2.503.2
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
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
doi	https://doi.org/10.52842/conf.acadia.2017.366
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_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
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
doi	https://doi.org/10.52842/conf.caadria.2018.2.297
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	acadia17_52
id	acadia17_52
authors	Ajlouni, Rima
year	2017
title	Simulation of Sound Diffusion Patterns of Fractal-Based Surface Profiles
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
doi	https://doi.org/10.52842/conf.acadia.2017.052
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	ecaade2017_079
id	ecaade2017_079
authors	Qabshoqa, Mohammad, Kocaturk, Tuba and Kiviniemi, Arto
year	2017
title	A value-driven perspective to understand Data-driven futures in Architecture
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. 407-416
doi	https://doi.org/10.52842/conf.ecaade.2017.2.407
summary	This paper reports on an investigation of the potentials of data utilisation in Architecture from a value generation and business creation points of view, based on an ongoing PhD research by the first author. It is of crucial importance to, first, identify what data actually signifies for Architecture, and secondly to explore how the value obtained through data-driven approaches in other industries could potentially be transferred and applied in our professional context. These objectives have been achieved through a qualitative comparative analysis of various cases. Additionally, the paper discusses the multiplicity of factors which contribute to different interpretations and utilisation of data with reference to various value systems embedded into our profession (e.g. design as ideology, design as profession, design as service). A comparative analysis of the existing data utilisation methods in connection with various value systems provide crucial insights in order to answer the following questions: How can data assess values in architectural design/practice? How can data utilisation give way to the emergence of new values for the profession?
keywords	Big Data in Architecture; Data-Driven Architecture Design; Data in Architecture Design; Computational Data Design; Digital Value in Architecture
series	eCAADe
email
last changed	2022/06/07 07:58

_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
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
doi	https://doi.org/10.52842/conf.caadria.2017.075
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	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	acadia17_164
id	acadia17_164
authors	Brugnaro, Giulio; Hanna, Sean
year	2017
title	Adaptive Robotic Training Methods for Subtractive Manufacturing
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. 164-169
doi	https://doi.org/10.52842/conf.acadia.2017.164
summary	This paper presents the initial developments of a method to train an adaptive robotic system for subtractive manufacturing with timber, based on sensor feedback, machine-learning procedures and material explorations. The methods were evaluated in a series of tests where the trained networks were successfully used to predict fabrication parameters for simple cutting operations with chisels and gouges. The results suggest potential benefits for non-standard fabrication methods and a more effective use of material affordances.
keywords	design methods; information processing; construction; robotics; ai & machine learning; digital craft; manual craft
series	ACADIA
email
last changed	2022/06/07 07:52

_id	caadria2017_070
id	caadria2017_070
authors	Chen, Nai Chun, Xie, Jenny, Tinn, Phil, Alonso, Luis, Nagakura, Takehiko and Larson, Kent
year	2017
title	Data Mining Tourism Patterns - Call Detail Records as Complementary Tools for Urban Decision Making
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. 685-694
doi	https://doi.org/10.52842/conf.caadria.2017.685
summary	In this study we show how Call Detail Record (CDR) can be used to better understand the travel patterns of visitors. We show how Origin-Destination (OD) Interactive Maps can provide transportation information through CDR. We then use aggregation of CDR to show the differences between the travel patterns of visitors from different countries and of different lengths of stay. We also show that visitors move differently during event periods and non-event periods, reflecting the importance of real-time data available by CDR. From CDR, we can gain more detailed and complete information about how tourists move compared to traditional surveys, which can be used to aid smarter transportation systems and urban resource planning.
keywords	Machine Learning; Call Detail Record; Original-Destination Matrix; Urban Design Tool
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	ecaade2018_243
id	ecaade2018_243
authors	Gardner, Nicole
year	2018
title	Architecture-Human-Machine (re)configurations - Examining computational design in practice
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 139-148
doi	https://doi.org/10.52842/conf.ecaade.2018.2.139
summary	This paper outlines a research project that explores the participation in, and perception of, advanced technologies in architectural professional practice through a sociotechnical lens and presents empirical research findings from an online survey distributed to employees in five large-scale architectural practices in Sydney, Australia. This argues that while the computational design paradigm might be well accepted, understood, and documented in academic research contexts, the extent and ways that computational design thinking and methods are put-into-practice has to date been less explored. In engineering and construction, technology adoption studies since the mid 1990s have measured information technology (IT) use (Howard et al. 1998; Samuelson and Björk 2013). In architecture, research has also focused on quantifying IT use (Cichocka 2017), as well as the examination of specific practices such as building information modelling (BIM) (Cardoso Llach 2017; Herr and Fischer 2017; Son et al. 2015). With the notable exceptions of Daniel Cardoso Llach (2015; 2017) and Yanni Loukissas (2012), few scholars have explored advanced technologies in architectural practice from a sociotechnical perspective. This paper argues that a sociotechnical lens can net valuable insights into advanced technology engagement to inform pedagogical approaches in architectural education as well as strategies for continuing professional development.
keywords	Computational design; Sociotechnical system; Technology adoption
series	eCAADe
email
last changed	2022/06/07 07:51

_id	ecaade2017_042
id	ecaade2017_042
authors	Hitchings, Katie, Patel, Yusef and McPherson, Peter
year	2017
title	Analogue Automation - The Gateway Pavilion for Headland Sculpture on the Gulf
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. 347-354
doi	https://doi.org/10.52842/conf.ecaade.2017.2.347
summary	The Waiheke Gateway Pavilion, designed by Stevens Lawson Architects originally for the 2010 New Zealand Venice Biennale Pavilion, was brought to fruition for the 2017 Headland Sculpture on the Gulf Sculpture trail by students from Unitec Institute of Technology. The cross disciplinary team comprised of students from architecture and construction disciplines working in conjunction with a team of industry professionals including architects, engineers, construction managers, project managers, and lecturers to bring the designed structure, an irregular spiral shape, to completion. The structure is made up of 261 unique glulam beams, to be digitally cut using computer numerical control (CNC) process. However, due to a malfunction with the institutions in-house CNC machine, an alternative hand-cut workflow approach had to be pursued requiring integration of both digital and analogue construction methods. The digitally encoded data was extracted and transferred into shop drawings and assembly diagrams for the fabrication and construction stages of design. Accessibility to the original 3D modelling software was always needed during the construction stages to provide clarity to the copious amounts of information that was transferred into print paper form. Although this design to fabrication project was challenging, the outcome was received as a triumph amongst the architecture community.
keywords	Digital fabrication; workflow; rapid prototyping; representation; pedagogy
series	eCAADe
email
last changed	2022/06/07 07:50

_id	caadria2023_362
id	caadria2023_362
authors	Luo, Jiaxiang, Mastrokalou, Efthymia, Aldabous, Rahaf, Aldaboos, Sarah and Lopez Rodriguez, Alvaro
year	2023
title	Fabrication of Complex Clay Structures Through an Augmented Reality Assisted Platform
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 413–422
doi	https://doi.org/10.52842/conf.caadria.2023.1.413
summary	The relationship between clay manufacturing and architectural design has a long trajectory that has been explored since the early 2000s. From a 3D printing or assembly perspective, using clay in combination with automated processes in architecture to achieve computational design solutions is well established. (Yuan, Leach & Menges, 2018). Craft-based clay art, however, still lacks effective computational design integration. With the improvement of Augmented Reality (AR) technologies (Driscoll et al., 2017) and the appearance of digital platforms, new opportunities to integrate clay manufacturing and computational design have emerged. The concept of digitally transferring crafting skills, using holographic guidance and machine learning, could make clay crafting accessible to more workers while creating the potential to share and exchange digital designs via an open-source manufacturing platform. In this context, this research project explores the potential of integrating computational design and clay crafting using AR. Moreover, it introduces a platform that enables AR guidance and the digital transfer of fabrication skills, allowing even amateur users with no prior making experience to produce complex clay components.
keywords	Computer vision, Distributed manufacturing, Augmented craftsmanship, Augmented reality, Real-time modification, Hololens
series	CAADRIA
email
last changed	2023/06/15 23:14

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