Cumincad : CUMINCAD Papers : Search Results

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

PDF papers
References

Hits 1 to 20 of 576

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

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

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

_id	ecaade2017_142
id	ecaade2017_142
authors	Gönenç Sorguç, Arzu, Kruºa Yemiºcio?lu, Müge, Özgenel, Ça?lar F?rat, Katipo?lu, Mert Ozan and Rasulzade, Ramin
year	2017
title	The Role of VR as a New Game Changer in Computational Design Education
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. 401-408
doi	https://doi.org/10.52842/conf.ecaade.2017.1.401.2
summary	With the rapid advances in technology, virtual reality(VR) re-emerged as an affordable technology providing new potentials for virtual learning environments(VLE). Within the scope of this study, firstly a general perspective on potentials of VR to create an appropriate VLE is put forward regarding the potentials related with learning modalities. Then, VR as a VLE in architectural education is discussed and utilization of VR is revisited considering the fundamentals of education as how to enhance skills regarding creativity, furnish students to adopt future skills and how VR can be used to enhance design understanding as well as space perception and spatial relations. It is deliberated that instead of mirroring the real spaces, allowing students to understand the virtuality with its own constituents will broaden the understanding of space, spatial relations, scale, motion, and time both in physical and virtual. The dichotomy between physical and virtual materiality, the potentials and pitfalls in the process of transformation from real/physical to virtual - virtual to real/physical are discussed in relation with the student projects designed in the scope of Digital Design Studio course in Middle East Technical University. It is also shown that VR stimulates different learning modalities especially kinesthetic modality and helping students to develop creativity and metacognition about space and spatial relations.
keywords	computational design education; virtual reality; digital tools; virtual learning environment
series	eCAADe
email
last changed	2022/06/07 07:50

_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	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	caadria2017_051
id	caadria2017_051
authors	Liu, Yuezhong and Stouffs, Rudi
year	2017
title	Familiar and Unfamiliar Data Sets in Sustainable Urban Planning
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. 705-714
doi	https://doi.org/10.52842/conf.caadria.2017.705
summary	Achieving energy efficient urban planning requires a multi-disciplinary planning approach. The huge increase in data from sensors and simulations does not help to reduce the burden of planners. On the contrary, unfamiliar multi-disciplinary data sets can bring planners into a hopeless tangle. This paper applies semi-supervised learning methods to address such planning data issues. A case study is used to demonstrate the proposed method with respect to three performance issues: solar heat gains, natural ventilation and daylight. The result shows that the method addressing both familiar and unfamiliar data has the ability to guide the planner during the planning process.
keywords	energy performance; S3VM; decision tree; familiar and unfamiliar.
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	ijac201715105
id	ijac201715105
authors	Nahmad Vazque, Alicia and Wassim Jabi
year	2017
title	Investigations in robotic-assisted design: Strategies for symbiotic agencies in material-directed generative design processes
source	International Journal of Architectural Computing vol. 15 - no. 1, 70-86
summary	The research described in this article utilises a phase-changing material, three-dimensional scanning technologies and a six-axis industrial robotic arms as vehicles to enable a novel framework where robotic technology is utilised as an ‘amplifier’ of the design process to realise geometries that derive from both constructive visions and architectural visions through iterative feedback loops between them. The robot in this scenario is not a fabrication tool but the enabler of an environment where the material, robotic and human agencies interact. This article describes the exploratory research for the development of a dialogic design process, sets the framework for its implementation, carries out an evaluation based on designer use and concludes with a set of observations. One of the main findings of this article is that a deeper collaboration that acknowledges the potential of these tools, in a learning-by-design method, can lead to new choreographies for architectural design and fabrication.
keywords	Robotic fabrication, human-machine networks, digital design, agency
series	other
type	normal paper
email
last changed	2019/08/02 08:28

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

_id	acadia17_552
id	acadia17_552
authors	Sjoberg, Christian; Beorkrem, Christopher; Ellinger, Jefferson
year	2017
title	Emergent Syntax: Machine Learning for the Curation of Design Solution Space
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. 552- 561
doi	https://doi.org/10.52842/conf.acadia.2017.552
summary	The expanding role of computational models in the process of design is producing exponential growth in parameter spaces. As designers, we must create and implement new methods for searching these parameter spaces, considering not only quantitative optimization metrics but also qualitative features. This paper proposes a methodology that leverages the pattern modeling properties of artificial neural networks to capture designers' inexplicit selection criteria and create user-selection-based fitness functions for a genetic solver. Through emulation of learned selection patterns, fitness functions based on trained networks provide a method for qualitative evaluation of designs in the context of a given population. The application of genetic solvers for the generation of new populations based on the trained network selections creates emergent high-density clusters in the parameter space, allowing for the identification of solutions that satisfy the designer’s inexplicit criteria. The results of an initial user study show that even with small numbers of training objects, a search tool with this configuration can begin to emulate the design criteria of the user who trained it.
keywords	design methods; information processing; AI; machine learning; generative system
series	ACADIA
email
last changed	2022/06/07 07:56

_id	ecaade2017_202
id	ecaade2017_202
authors	Sollazzo, Aldo, Trento, Armando and Baseta, Efilena
year	2017
title	Machinic Agency - Implementing aerial robotics and machine learning to map public space
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. 611-618
doi	https://doi.org/10.52842/conf.ecaade.2017.2.611
summary	The research presented in this paper is focused on proposing a new digital workflow, involving unmanned aerial vehicles (UAV) and machines learning systems, in order to detect and map citizen's behaviors in the context of public spaces.Novel machinic abilities can be implemented in the understanding of the human context, decoding, through computer visions and machine learning, complex systems into intelligible outputs (Olson, 2008), mapping the relationships of our reality. In this framework, robotic and computational strategies can be implemented in order to offer a new description of public spaces, bringing to light the hidden forces and multiple layers constituting the urban habitat. The presented study focuses on the development of a methodology turning video frames collected from cameras installed on drones into large datasets used to train convolutional networks and enable machines learning systems to detect and map pedestrians in public spaces.
keywords	mapping; drones; machine learning; computer vision; city
series	eCAADe
email
last changed	2022/06/07 07:56

_id	ijac201715304
id	ijac201715304
authors	Tosello, María Elena and María Georgina Bredanini
year	2017
title	A personal space in the Web. Bases, processes and evaluation of a collaborative digital design experience for significant learning
source	International Journal of Architectural Computing vol. 15 - no. 3, 230-245
summary	We live constantly networked, performing multiple activities in virtual spaces which are intertwined with physical space, shaping an augmented and symbiotic chronotope. Considering that personal space is an area surrounding individuals that provides a framework for developing activities wouldn’t it be necessary to count on a virtual personal space? This article presents the bases, processes, and results of a didactic experience which purpose was to imagine and design a personal space in the Web, representing its properties and characteristics through a transmedia narrative unfolded through diverse languages and media. Three cases are presented, selected because they propose different strategies to approach the problem. In order to perform a comparative analysis of the results, the categories were defined based on the triadic structure of Peirce’s Theory of Signs, which in turn were divided into sub-categories that incorporate the Principles of Design and Evaluation of Interface-Spaces.
keywords	Personal space, transmedia storytelling, parametric design, video games, interface-space
series	journal
email
last changed	2019/08/07 14:03

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

_id	ecaade2017_157
id	ecaade2017_157
authors	Date, Kartikeya, Schaumann, Davide and Kalay, Yehuda E.
year	2017
title	A Parametric Approach To Simulating Use-Patterns in Buildings - The Case Of Movement
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. 503-510
doi	https://doi.org/10.52842/conf.ecaade.2017.2.503
summary	We describe one of the three core use-pattern building blocks of a parametric approach to simulating use-patterns in buildings. Use-patterns are modeled as events which use specified descriptions of spaces, actors and activities which constitute them. The simulation system relies on three fundamental patterns of use - move, meet and do. The move pattern is considered in detail in this paper with specific reference to what we term the partial knowledge issue. Modeling decision making about how to move through the space (what path to take) depends on modeling the actor's partial access to knowledge. Visibility is used as an example of partial knowledge. The parametric approach described in the paper enables the clear separation of syntactical and semantic conditions which inform decisions and the coordination of decisions made by agents in a simulation of use-patterns. This approach contributes to extending the analytical capability of Building Information Models from the point of view of evaluating how a proposed building design may be used, given complex, interrelated patterns of use.
keywords	Agent-Based Systems, Simulation, Use-Patterns, Design Tools
series	eCAADe
email
last changed	2022/06/07 07:55

_id	acadia17_266
id	acadia17_266
authors	Gonzalez Rojas,Paloma
year	2017
title	Space and Motion: Data-Driven Model of 4D Pedestrian Behavior
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. 266-273
doi	https://doi.org/10.52842/conf.acadia.2017.266
summary	The understanding of space relies on motion, as we experience space by crossing it in time, space’s fourth dimension. However, architects lack the necessary tools to incorporate people's motion into their design of space. As a consequence, architects fail to connect space with the motion of the people that inhabit their buildings, creating disorienting environments. Further, what if augmentation technology changes how we inhabit space and the static built environment does not fit people anymore? This paper explores the problem of developing a model from people's motion, to inform and augment the architecture design process in the early stages. As an outcome, I have designed a model based on data from human-space interaction obtained through field work. First, relevant behavior was identified and recorded. Second, a metric was extracted from the data and composed by speed, the 4th D dimension as time, and gestures. Third, the original behavior was rebuilt, producing a set of rules. The rules were combined to form the model of human-space interaction. This generalizable model provides a novel approach to designing space based on data from people. Moreover, this paper presents a means of incorporating inhabitants' behavior into digital design. Finally, the model contributes to the advancement of people's motion research for general applications, such as in transport engineering, robotics, and cognitive sciences.
keywords	design methods; information processing; simulation & optimization; data visualization
series	ACADIA
email
last changed	2022/06/07 07:51

For more results click below: