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 3 of 3

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

_id	caadria2020_054
id	caadria2020_054
authors	Shen, Jiaqi, Liu, Chuan, Ren, Yue and Zheng, Hao
year	2020
title	Machine Learning Assisted Urban Filling
doi	https://doi.org/10.52842/conf.caadria.2020.2.679
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 679-688
summary	When drawing urban scale plans, designers should always define the position and the shape of each building. This process usually costs much time in the early design stage when the condition of a city has not been finally determined. Thus the designers spend a lot of time working forward and backward drawing sketches for different characteristics of cities. Meanwhile, machine learning, as a decision-making tool, has been widely used in many fields. Generative Adversarial Network (GAN) is a model frame in machine learning, specially designed to learn and generate image data. Therefore, this research aims to apply GAN in creating urban design plans, helping designers automatically generate the predicted details of buildings configuration with a given condition of cities. Through the machine learning of image pairs, the result shows the relationship between the site conditions (roads, green lands, and rivers) and the configuration of buildings. This automatic design tool can help release the heavy load of urban designers in the early design stage, quickly providing a preview of design solutions for urban design tasks. The analysis of different machine learning models trained by the data from different cities inspires urban designers with design strategies and features in distinct conditions.
keywords	Artificial Intelligence; Urban Design; Generative Adversarial Networks; Machine Learning
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	cdrf2019_27
id	cdrf2019_27
authors	Chuan Liu, Jiaqi Shen, Yue Ren, and Hao Zheng
year	2020
title	Developing a Digital Interactive Fabrication Process in Co-existing Environment
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_3
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	In the stage of prototype practice, the maker mainly works by himself, but it needs to test and adapt to find correct fabrication method when maker didn’t have clearly fabrication description. Therefore, rapid prototyping is very important in the prototype practice of the maker. “Design- Fabrication-Assembly” (DFA)- an integration prototyping process which helps designers in creating kinetic skin by following a holistic process. However, DFA lacks a medium for communication between design, fabrication and assembly status. This paper proposes a solution called co-existing Fabrication System (CoFabs) by combining multiple sensory components and visualization feedbacks. We combine mixed reality (MR) and the concept of digital twin (DT)–a device that uses a virtual interface to control a physical mechanism for fabrication and assembly. By integrating virtual and physical, CoFab allows designers using different methods of observation to prototype more rigorously and interactively correct design decisions in real-time.
series	cdrf
email
last changed	2022/09/29 07:51

_id	cdrf2019_17
id	cdrf2019_17
authors	Chuan Liu, Jiaqi Shen, Yue Ren, and Hao Zheng
year	2020
title	Pipes of AI – Machine Learning Assisted 3D Modeling Design
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_2
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	Style transfer is a design technique that is based on Artificial Intelligence and Machine Learning, which is an innovative way to generate new images with the intervention of style images. The output image will carry the characteristic of style image and maintain the content of the input image. However, the design technique is employed in generating 2D images, which has a limited range in practical use. Thus, the goal of the project is to utilize style transfer as a toolset for architectural design and find out the possibility for a 3D modeling design. To implement style transfer into the research, floor plans of different heights are selected from a given design boundary and set as the content images, while a framework of a truss structure is set as the style image. Transferred images are obtained after processing the style transfer neural network, then the geometric images are translated into floor plans for new structure design. After the selection of the tilt angle and the degree of density, vertical components that connecting two adjacent layers are generated to be the pillars of the structure. At this stage, 2D style transferred images are successfully transformed into 3D geometries, which can be applied to the architectural design processes. Generally speaking, style transfer is an intelligent design tool that provides architects with a variety of choices of idea-generating. It has the potential to inspire architects at an early stage of design with not only 2D but also 3D format.
series	cdrf
email
last changed	2022/09/29 07:51

No more hits.