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	acadia14_619
id	acadia14_619
authors	Erhan, Halil; Wang, Ivy; Shireen, Naghmi
year	2014
title	Interacting with Thousands: A Parametric-Space Exploration Method in Generative Design
doi	https://doi.org/10.52842/conf.acadia.2014.619
source	ACADIA 14: Design Agency [Proceedings of the 34th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 9781926724478]Los Angeles 23-25 October, 2014), pp. 619-626
summary	Although generative and parametric design methods open possibilities for working with a large number of solutions, there is almost no computational support for designers to directly manage, sort, filter, and select the generated designs. In this study, we propose an approach that presents a similarity-based design exploration relying on similarity indices that aims to reduce and collapse design space into manageable scales.
keywords	parametric design, generative methods, design space reduction, similarity metrics and indices, similarity matrix; BIG DATA
series	ACADIA
type	Normal Paper
email
last changed	2022/06/07 07:55

_id	cdrf2023_273
id	cdrf2023_273
authors	Pixin Gong, Xiaoran Huang, Chenyu Huang, Shiliang Wang
year	2023
title	Modeling on Outdoor Thermal Comfort in Traditional Residential Neighborhoods in Beijing Based on GAN
doi	https://doi.org/https://doi.org/10.1007/978-981-99-8405-3_23
source	Proceedings of the 2023 DigitalFUTURES The 5st International Conference on Computational Design and Robotic Fabrication (CDRF 2023)
summary	With the support of new urban science and technology, the bottom-up and human-centered space quality research has become the key to delicacy urban governance, of which the Universal Thermal Climate Index (UTCI) have a severe influence. However, in the studies of actual UTCI, datasets are mostly obtained from on-site measurement data or simulation data, which is costly and ineffective. So, how to efficiently and rapidly conduct a large-scale and fine-grained outdoor environmental comfort evaluation based on the outdoor environment is the problem to be solved in this study. Compared to the conventional qualitative analysis methods, the rapidly developing algorithm-supported data acquisition and machine learning modelling are more efficient and accurate. Goodfellow proposed Generative Adversarial Nets (GANs) in 2014, which can successfully be applied to image generation with insufficient training data. In this paper, we propose an approach based on a generative adversarial network (GAN) to predict UTCI in traditional blocks. 36000 data samples were obtained from the simulations, to train a pix2pix model based on the TensorFlow framework. After more than 300 thousand iterations, the model gradually converges, where the loss of the function gradually decreases with the increase of the number of iterations. Overall, the model has been able to understand the overall semantic information behind the UTCI graphs to a high degree. Study in this paper deeply integrates the method of data augmentation based on GAN and machine learning modeling, which can be integrated into the workflow of detailed urban design and sustainable construction in the future.
series	cdrf
email
last changed	2024/05/29 14:04

_id	ecaade2014_230
id	ecaade2014_230
authors	Tsung-Hsien Wang
year	2014
title	Reasoning Spatial Relationships in Building Information Models using Voxels
doi	https://doi.org/10.52842/conf.ecaade.2014.2.465
source	Thompson, Emine Mine (ed.), Fusion - Proceedings of the 32nd eCAADe Conference - Volume 2, Department of Architecture and Built Environment, Faculty of Engineering and Environment, Newcastle upon Tyne, England, UK, 10-12 September 2014, pp. 465-471
summary	This paper investigates a voxel-based approach to automate the space construction of an ill-defined building information model, namely, a building model without specific spatial definitions. The objective is to provide a simplified representation through clustering voxels to reconstruct spaces, with which a spatial topological algorithm is designed to infer the implicit connectivity. This approach is treated as the first step to automate building information exchange for building performance simulation and knowledge-intensive reasoning.
wos	WOS:000361385100049
keywords	Building information modelling; voxel; automatic building information exchange; topological spatial relationship
series	eCAADe
email
last changed	2022/06/07 07:57

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