CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

PDF papers
References

Hits 1 to 20 of 574

_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 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 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 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

_id acadia17_330
id acadia17_330
authors Krietemeyer, Bess; Bartosh, Amber; Covington, Lorne
year 2017
title Shared Realities: A Method for Adaptive Design Incorporating Real-Time User Feedback using Virtual Reality and 3D Depth-Sensing Systems
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. 330- 339
doi https://doi.org/10.52842/conf.acadia.2017.330
summary When designing interactive architectural systems and environments, the ability to gather user feedback in real time provides valuable insight into how the system is received and ultimately performs. However, physically testing or simulating user behavior with an interactive system outside of the actual context of use can be challenging due to time constraints and assumptions that do not reflect accurate social, behavioral, or environmental conditions. Employing evidence based, user-centered design practices from the field of human–computer interaction (HCI) coupled with emerging architectural design methodologies creates new opportunities for achieving optimal system performance and design usability for interactive architectural systems. This paper presents a methodology for developing a mixed reality computational workflow combining 3D depth sensing and virtual reality (VR) to enable iterative user-centered design. Using an interactive museum installation as a case study, user pointcloud data is observed via VR at full scale and in real time for a new design feedback experience. Through this method, the designer is able to virtually position him/herself among the museum installation visitors in order to observe their actual behaviors in context and iteratively make modifications instantaneously. In essence, the designer and user effectively share the same prototypical design space in different realities. Experimental deployment and preliminary results of the shared reality workflow are presented to demonstrate the viability of the method for the museum installation case study and for future interactive architectural design applications. Contributions to computational design, technical challenges, and ethical considerations are discussed for future work.
keywords design methods; information processing; hci; VR; AR; mixed reality; computer vision
series ACADIA
email
last changed 2022/06/07 07:52

_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_234
id ecaade2017_234
authors Benetti, Alberto, Favargiotti, Sara and Ricci, Mos?
year 2017
title RE.S.U.ME. - REsilient and Smart Urban MEtabolism
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. 1113-1120
doi https://doi.org/10.52842/conf.ecaade.2017.1.1113
summary New technologies and uncontrolled open-data policies lead public to a new way of approaching the built environment. To enlarge the competences of the professionals that work within the cities, we believe that providing a deep and dynamic knowledge on the heritage and urban built environment is the more effective solution to offer a unique support to the needs. By providing a boosted geographical database with detailed information about the status of each building, we aim to support the professional by providing a neat vision about vacant buildings available citywide. We think this knowledge is an important asset in covering every kind of public requests: from flat to rent to an abandoned building to restore or to drive better investors. The city of Trento will be the pilot project to test these statements.We studied the phenomenon of pushing new constructions rather investing on the reuse of abandoned buildings with the consequences of unsustainable land use. To address the work we adopted a comprehensive approach across the fields of urbanism, ICT engineering and social sciences. We believe that sharing knowledge and know-hows with municipalities, agencies, and citizens is the way to support better market strategies as well as urban transformation policies.
keywords Information Technology; Urban Metabolism; Re-cycle; Urban Reserves; Policy Decision-Making; Data-driven Analysis
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
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 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_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_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 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 acadia17_000
id acadia17_000
authors Nagakura, Takehiko; Tibbits, Skylar; Iba?ez, Mariana and Mueller, Caitlin (eds.)
year 2017
title ACADIA 2017: DISCIPLINES & DISRUPTION
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), 706 p.
doi https://doi.org/10.52842/conf.acadia.2017
summary The Proceedings of the ACADIA 2017 conference contains peer reviewed research papers presented at the 37th annual conference of the Association for Computer Aided Design in Architecture. Disciplines & Disruption initiates a dialog about the state of the discipline of architecture and the impact of technology in shaping or disrupting design, methods and cultural fronts. For the past 30 years, distinctive advancements in technologies have delivered unprecedented possibilities to architects and enabled new expressions, performance, materials, fabrication and construction processes. Simultaneously, digital technology has permeated the social fabric around architecture with broad influences ranging from digital preservation to design with the developing world. Driven by technological, data and material advances, architecture now witnesses the moment of disruption, whereby formerly distinct areas of operation become increasingly connected and accessible to architecture's sphere of concerns in ways never before possible. Distinctions between design and making, building and urban scale, architecture and engineering, real and virtual, on site and remote, physical and digital data, professionals and crowds, are diminishing as technology increases the designer's reach far beyond the confines of the drafting board. This conference provides a platform to investigate the shifting landscape of the discipline today, and to help define and navigate the future.
keywords Computer Aided Design, ACADIA, ACADIA 2017, ACADIA Conference, Architecture
series ACADIA
email
last changed 2022/06/07 07:49

_id acadia17_138
id acadia17_138
authors Berry, Jaclyn; Park, Kat
year 2017
title A Passive System for Quantifying Indoor Space Utilization
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. 138-145
doi https://doi.org/10.52842/conf.acadia.2017.138
summary This paper presents the development of a prototype for a new sensing device for anonymously evaluating space utilization, which includes usage factors such as occupancy levels, congregation and circulation patterns. This work builds on existing methods and technology for measuring building performance, human comfort and occupant experience in post-occupancy evaluations as well as pre-design strategic planning. The ability to collect data related to utilization and occupant experience has increased significantly due to the greater accessibility of sensor systems in recent years. As a result, designers are exploring new methods to empirically verify spatial properties that have traditionally been considered more qualitative in nature. With this premise, this study challenges current strategies that rely heavily on manual data collection and survey reports. The proposed sensing device is designed to supplement the traditional manual method with a new layer of automated, unbiased data that is capable of capturing environmental and social qualities of a given space. In a controlled experiment, the authors found that the data collected from the sensing device can be extrapolated to show how layout, spatial interventions or other design factors affect circulation, congregation, productivity, and occupancy in an office setting. In the future, this sensing device could provide designers with real-time feedback about how their designs influence occupants’ experiences, and thus allow the designers to base what are currently intuition-based decisions on reliable data and evidence.
keywords design methods; information processing; smart buildings; IoT
series ACADIA
email
last changed 2022/06/07 07:52

_id acadia17_154
id acadia17_154
authors Brown, Nathan; Mueller, Caitlin
year 2017
title Designing With Data: Moving Beyond The Design Space Catalog
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. 154-163
doi https://doi.org/10.52842/conf.acadia.2017.154
summary Design space catalogs, which present a collection of different options for selection by human designers, have become commonplace in architecture. Increasingly, these catalogs are rapidly generated using parametric models and informed by simulations that describe energy usage, structural efficiency, daylight availability, views, acoustic properties, and other aspects of building performance. However, by conceiving of computational methods as a means for fostering interactive, collaborative, guided, expert-dependent design processes, many opportunities remain to improve upon the originally static archetype of the design space catalog. This paper presents developments in the areas of interaction, automation, simplification, and visualization that seek to improve on the current catalog model while also describing a vision for effective computer-aided, performance-based design processes in the future.
keywords design methods; information processing; simulation & optimization; data visualization
series ACADIA
email
last changed 2022/06/07 07:54

_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 ecaade2017_129
id ecaade2017_129
authors Li, Qinying and Teng, Teng
year 2017
title Integrated Adaptive and Tangible Architecture Design Tool
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. 619-628
doi https://doi.org/10.52842/conf.ecaade.2017.1.619
summary In this paper, we identified two majority issues of current CAAD development situating from the standpoint of CAAD history and the nature of design. On one hand, current CAAD tools are not adaptive enough for early design stage, since most of CAAD tools are designed to be mathematical correct. as we conducted a detailed survey of CAAD development history, we find out that most of the techniques of Computer-Aided Design applied into architecture are always adopted from engineering track. On other hand, the interaction between Architects/Designer and CAAD tools needs to be enhanced. Design objects are operated by 2d based tools such as keyboard, mouse as well as monitors which are less capable of comprehensively representing physical 3D building objects. In addition, we proposed a working in progress potential solution with HCI approaches to fix these issues. We summarize that , the prototype proved that architects and designers could benefit from utilizing adaptive and tangible design tools, especially during massing studies in the early phases of architectural design.
keywords CAAD development,; Human Computer Interaction; Tangible User Interfaces; Design Tool development; Design Process
series eCAADe
email
last changed 2022/06/07 07:59

For more results click below:

this is page 0show page 1show page 2show page 3show page 4show page 5... show page 28HOMELOGIN (you are user _anon_476138 from group guest) CUMINCAD Papers Powered by SciX Open Publishing Services 1.002