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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	ijac202321205
id	ijac202321205
authors	Zhuang, Xinwei; Ju, Yi; Yang, Allen; Caldas, Luisa
year	2023
title	Synthesis and generation for 3D architecture volume with generative modeling
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 2, 297–314
summary	Generative design in architecture has long been studied, yet most algorithms are parameter-based and require explicit rules, and the design solutions are heavily experience-based. In the absence of a real understanding of the generation process of designing architecture and consensus evaluation matrices, empirical knowledge may be difficult to apply to similar projects or deliver to the next generation. We propose a workflow in the early design phase to synthesize and generate building morphology with artificial neural networks. Using 3D building models from the financial district of New York City as a case study, this research shows that neural networks can capture the implicit features and styles of the input dataset and create a population of design solutions that are coherent with the styles. We constructed our database using two different data representation formats, voxel matrix and signed distance function, to investigate the effect of shape representations on the performance of the generation of building shapes. A generative adversarial neural network and an auto decoder were used to generate the volume. Our study establishes the use of implicit learning to inform the design solution. Results show that both networks can grasp the implicit building forms and generate them with a similar style to the input data, between which the auto decoder with signed distance function representation provides the highest resolution results.
keywords	data-driven design, 3D deep learning, architecture morphology representation, auto decoder, generative adversarial neural network
series	journal
last changed	2024/04/17 14:30

_id	acadia23_v2_532
id	acadia23_v2_532
authors	Zhuang, Xinwei; Huang, Zixun; Zeng, Wentao; Caldas, Luisa
year	2023
title	Encoding Urban Ecologies: Automated Building Archetype Generation through Self-Supervised Learning for Energy Modeling
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 2: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-0-3]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 532-541.
summary	As the global population and urbanization expand, the building sector has emerged as the predominant energy consumer and carbon emission contributor. The need for inno- vative Urban Building Energy Modeling grows, yet existing building archetypes often fail to capture the unique attributes of local buildings and the nuanced distinctions between different cities, jeopardizing the precision of energy modeling. This paper presents an alternative tool employing self-supervised learning to distill complex geometric data into representative, locale-specific archetypes. This study attempts to foster a new paradigm of interaction with built environments, incorporating local parameters to conduct bespoke energy simulations at the community level. The catered archetypes can augment the precision and applicability of energy consumption modeling at the different scales across diverse building inventories. This tool provides a potential solution that encourages the exploration of emerging local ecologies. By integrating building envelope characteristics and cultural granularity into the building archetype generation process, we seek a future where architecture and urban design are intricately interwoven with the energy sector in shaping our built environments.
series	ACADIA
type	paper
email
last changed	2024/12/20 09:13

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