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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	acadia21_112
id	acadia21_112
authors	Kahraman, Ridvan; Zechmeister, Christoph; Dong, Zhetao; Oguz, Ozgur S.; Drachenberg, Kurt; Menges, Achim; Rinderspacher, Katja
year	2021
title	Augmenting Design
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 112-121.
doi	https://doi.org/10.52842/conf.acadia.2021.112
summary	In recent years, generative machine learning methods such as variational autoencoders (VAEs) and generative adversarial networks (GANs) have opened up new avenues of exploration for architects and designers. The presented work explores how these methods can be expanded by incorporating multiple abstract criteria directly into the formulation of the algorithm that negotiates these complex criteria and proposes a fitting design. It draws inspiration from the works of several design theorists who have developed such goal-oriented approaches to design, and sets up multiple-objective VAE and GAN frameworks with this idea in mind. The research demonstrates that by incorporating multiple constraints using auxiliary discriminator networks, the developed algorithms are able to generate innovative solutions to two example problems: the design of 2D digits, and the design of 3D voxel chairs. By speculating and examining the role of the designer in data based generative computational design workflows, the research aims to provide an approach for solving design tasks in the age of big data.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2021_406
id	caadria2021_406
authors	Sun, Maoran, Sun, Pengcheng, Dong, Yuebin and Lopez, Jose Luis Garcia del Castillo
year	2021
title	Mass Production - Towards Multidimensional,Real-time Feedback in Early Stages of Urban Design Processes
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 649-658
doi	https://doi.org/10.52842/conf.caadria.2021.2.649
summary	Urban design, especially in its early stages, focuses mainly on massing studies rather than architectural detail or engineering. Traditional urban design workflows involve a mix of sketching and modeling. However, the back and forth between the sketching-modeling loop is typically fairly time-consuming, resulting in a reduced capacity to iterate efficiently over design concepts, even in their digital form. In this paper, we present a workflow for producing digital massing tests from hand-drawn sketches. The goal of Mass Production is to help quick iteration on volumetric design enhanced by real-time feedback on quantitative and qualitative parameters of the model, thus helping designers make better informed decisions on early stages of urban design processes. The architecture of the proposed workflow consists of three main elements: a tangible user interface (UI) for designer input, a real-time dashboard of diagrams and models for massing analysis, and an augmented reality (AR) environment for enhanced feedback on design form and shaping. In this research, Mass Production is tested in different design scenarios, a discussion about the future and its impact is presented, including emerging technology while keeping traditional workflows.
keywords	Urban Design; Massing Study; Augmented Reality
series	CAADRIA
email
last changed	2022/06/07 07:56

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