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	caadria2019_350
id	caadria2019_350
authors	Tomarchio, Ludovica, Hasler, Stephanie, Herthogs, Pieter, MÃ¼ller, Johannes, TunÃ§er, Bige and He, Peijun
year	2019
title	Using an Online Participation Tool to Collect Relevant Data for Urban Design - The construction of two participation exercices
doi	https://doi.org/10.52842/conf.caadria.2019.2.747
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 747-756
summary	This paper discusses the design of an online digital participation campaign, developed as an academic research project in Singapore. In order to develop appropriate exercises which fitted the tool and the context, we addressed several questions: how can online participation tools maintain a negotiation and education power? What data generated by citizens, in the form of a design proposals, is useful for urban design? We created two different exercises, at different scales: one exercise asking people to design proposals with functional blocks and one where citizens could decide the equipment and furniture in a public space. For each exercise we discuss the scale, the elements, the educating and mediating impact, but also the way we intended to use the gathered local knowledge in urban design. The exercise did not receive the expected contributions, gathering little attention from internet users. More results were obtained using an offline experimental setup. In conclusion, we reconsider the weakest points of the design in a critical analysis and provide direction for future online participation tools.
keywords	participation; urban design ; online tool; engagement
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	ecaadesigradi2019_179
id	ecaadesigradi2019_179
authors	Castelo-Branco, Renata, Leit?o, António and Santos, Guilherme
year	2019
title	Immersive Algorithmic Design - Live Coding in Virtual Reality
doi	https://doi.org/10.52842/conf.ecaade.2019.2.455
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 455-464
summary	As many other areas of human activity, the architectural design process has been recently shaken by Virtual Reality (VR), as it offers new ways to experience and communicate architectural space. In this paper we propose Live Coding in Virtual Reality (LCVR), a design approach that allows architects to benefit from the advantages of VR within an algorithmic design workflow. LCVR integrates a live coding solution, where the architect programs his design intent and immediately receives feedback on the changes applied to the program; and VR, which means this workflow takes place inside the virtual environment, where the architect is immersed in the model that results from the program he is concurrently updating from inside VR. In this paper we discuss the possible impacts of such an approach, as well as the most pressing implementation issues. We offer a critical analysis and comparison of the various solutions available in the context of two different programming paradigms: visual and textual.
keywords	Virtual Reality; Algorithmic Design; Live Coding
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:55

_id	ecaadesigradi2019_273
id	ecaadesigradi2019_273
authors	Hadighi, Mahyar and Duarte, Jose
year	2019
title	Using Grammars to Trace Architectural Hybridity in American Modernism - The case of William Hajjar single-family house
doi	https://doi.org/10.52842/conf.ecaade.2019.1.529
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 529-540
summary	In this paper, mid-century modern single-family houses designed by William Hajjar are analyzed through a shape grammar methodology within the context of the traditional architecture of an American college town. A member of the architecture faculty at the Pennsylvania State University, Hajjar was a practitioner in State College, PA, where the University Park campus is located, and an influential figure in the history of architecture in the area. The residential architecture he designed for and built in the area incorporates many of the formal and functional features typical of both modern European architecture and traditional American architecture. Based on a computational methodology, this study offers an investigation into this hybridity phenomenon by exploring Hajjar's architecture in relation to the traditional American architecture prevalent in the college town of State College.
keywords	shape grammar; American architecture; William Hajjar; hybridity; college town
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:49

_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
doi	https://doi.org/10.52842/conf.acadia.2020.1.382
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.
summary	We present a framework to generate intelligent spatial assemblies from sets of digitally encoded spatial parts designed by the architect with embedded principles of prefabrication, assembly awareness, and reconfigurability. The methodology includes a bespoke constraint-solving algorithm for autonomously assembling 3D geometries into larger spatial compositions for the built environment. A series of graph-based analysis methods are applied to each assembly to extract performance metrics related to architectural space-making goals, including structural stability, material density, spatial segmentation, connectivity, and spatial distribution. Together with the constraint-based assembly algorithm and analysis methods, we have integrated a novel application of deep reinforcement (RL) learning for training the models to improve at matching the multiperformance goals established by the user through self-play. RL is applied to improve the selection and sequencing of parts while considering local and global objectives. The user’s design intent is embedded through the design of partial units of 3D space with embedded fabrication principles and their relational constraints over how they connect to each other and the quantifiable goals to drive the distribution of effective features. The methodology has been developed over three years through three case study projects called ArchiGo (2017–2018), NoMAS (2018–2019), and IRSILA (2019-2020). Each demonstrates the potential for buildings with reconfigurable and adaptive life cycles.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

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

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