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	architectural_intelligence2022_14
id	architectural_intelligence2022_14
authors	Philip F. Yuan, Xinjie Zhou, Hao Wu, Liming Zhang, Lijie Guo, Yun Shi, Zhe Lin, Jinyu Bai, Youhai Yu & Shanglu Yang
year	2022
title	Robotic 3D printed lunar bionic architecture based on lunar regolith selective laser sintering technology
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00014-9
source	Architectural Intelligence Journal
summary	The lunar base is not only an experimental station for extraterrestrial space exploration but also a dwelling for humans performing this exploration. Building a lunar base presents numerous obstacles and requires environmental perception, feedback design, and construction methods. An integrated fabrication process that incorporates design, 3D printing workflow, and construction details to build a bionic, reconfigurable and high-performance lunar base prototype is presented in this paper. The research comprises the study of the lunar regolith 3D printing mechanism, the real-time control of powder laying and compaction procedure, and the development of a 3D printing tool end system. In this paper, many scientific questions regarding in situ fabrication on the lunar surface are raised and addressed with the proposal of a progressive optimization design method, the molding principle, and gradation strategy of lunar soil-polyaryletherketone (PAEK) hybrid powder, and the principle of dual-light field 3D laser printing. The feasibility of the technical strategy proposed in this paper is verified by the presented empirical samples.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	acadia22_714
id	acadia22_714
authors	Li, Yunqin; Zhang, Jiaxin; Wang, Xueqiang; Ma, Kai
year	2022
title	Measuring Street Vitality Based on Video-image Using Deep Learning
source	ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 714-725.
summary	This paper proposes a deep convolutional neural network-based framework for fine-scale studies on automatic evaluation of street-level vitality using multiple object tracking and image segmentation with video data. A deep learning model for street vitality evaluation was proposed based on the intensity and complexity of pedestrian activities.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:04

_id	caadria2022_299
id	caadria2022_299
authors	Cui, Qiang, Zhang, Huikai, Pawar, Siddharth Suhas, Yu, Chuan, Feng, Xiqiao and Qiu, Song
year	2022
title	Topology Optimization for 3D-Printable Large-Scale Metallic Hollow Structures With Self-Supporting
doi	https://doi.org/10.52842/conf.caadria.2022.2.101
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 101-110
summary	Design for Additive Manufacturing (DfAM), is a one of the most commonly used and foundational techniques used in the development of new products, and particularly those that involve large-scale metallic structures composed of hollow components. One such AM technique is Wire Arc Additive Manufacturing (WAAM), which is the application of robotic welding technology applied to Additive Manufacturing. Due to the lack of a simple method to describe the fabricating constraint of WAAM and the complex hollow morphology, which difficultly deploys topology optimization structural techniques that use WAAM. In this paper, we develop a design strategy that unifies ground-structure optimization method with generative design that considers the features of hollow components, WAAM overhang angle limits and manufacturing thickness limits. The method is unique in that the user can interact with the design results, make changes to parameters, and alter the design based on the user‚s aesthetic or specific manufacturing setup needs. We deploy the method in the design and 3D printing of an optimized Electric Vehicle Chassis and successfully test in under different loading conditions.
keywords	Topology optimization, Generative design, Self-supporting, Hollow structures, Metallic 3D printing, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_264
id	caadria2022_264
authors	Zhang, Garry Hangge, Meng, Leo Lin, Gardner, Nicole, Yu, Daniel and Haeusler, Matthias Hank
year	2022
title	Transit Oriented Development Assistive Interface (TODAI): A Machine Learning Powered Computational Urban Design Tool for TOD
doi	https://doi.org/10.52842/conf.caadria.2022.1.253
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 253-262
summary	Transit-oriented Development(TOD) is widely regarded as a sustainable development paradigm for its sensible space planning and promotion of public transit access. Research in providing decision support tools of TOD may contribute to the Sustainable Development Goals, especially towards sustainable cities and communities (SDG goal 11).While the existing Geographic Information System(GIS) approach may well inform TOD planning, computational design, simulation, and visualisation techniques can further enhance this process. The research aims to provide a data-driven, computational-aided planning support system (PSS) to enhance the TOD decision-making process. The research adopts an action research methodology, which iteratively designs experiments and inquires through situating the research question in real-world practice. A work-in-progress prototype is provided - Transit-Oriented Development Assistive Interface (TODAI), along with an experiment in a newly proposed metro station in Sydney, Australia. TODAI provides real-time visualisation of urban forms and analytical data indicators reflecting key considerations relevant to TOD performance. A regressive machine learning model (XGBoost) is used to make predictions of analytical indicators, promptly producing outcomes that may otherwise require a costly computational operation.
keywords	TransUrban Planning, Transit-Oriented Development, Planning Support System, Machine Learning, SDG 11it-Oriented Development, Urban Planning, Machine Learning, Computational Design, SDG11, Sustainable Cities and Communities
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	cdrf2022_25
id	cdrf2022_25
authors	Hao Zhang, Yuetao Wang, Yuhan Tan, and Jilong Zhao
year	2022
title	Parametric Skin Design Method Based on Plane Crystallographic Group Operation Principle
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_3
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	Under the dual constraints of industrialization and digitalization, the building skin and structure are further integrated to form standardized units to meet the requirements of architectural performance, industrial prefabrication and “complexity” aesthetic characteristics. The complex and diverse forms of today's building skin hide profound mathematical logic relations and operation rules of form generation. Crystallographic group with regular symmetry and the operation principles reflected by it is one of the most important rules and methods of form and pattern processing in skin design. The study of the mural symbols in ancient Egypt, the murals in the Alhambra, the manuscripts of Escher and the window lattice in ancient Chinese architecture profoundly reflects the basic operation principle of crystal group in shaping the skin form of architecture. Abundant and diverse architectural skin forms can be formed through the operation of symmetry group on basic graphic units. On the basis of clarifying the basic principle of crystal group action, the operation matrix of crystallographic symmetry group can be transformed into parameterized operation steps through programming language for visual operation, and then the skin form with high complexity and leap dimension can be generated by geometric algorithm, and the design method of building skin generation based on crystallographic group is constructed. In the selection of operation form, combined with the calculation of building performance and structure, the construction skin can be used in practical engineering is generated. Based on crystallographic group operation, the unifications of building skin and the classification simplification of components can meet the requirements of modular and unifications design in the process of building industrialization, and meet the requirements of current building industrialization and digitization. It has great research significance and value in the aspects of design and construction efficiency and material economic cost.
series	cdrf
email
last changed	2024/05/29 14:02

_id	caadria2022_394
id	caadria2022_394
authors	Li, Yuanyuan, Huang, Chenyu, Zhang, Gengjia and Yao, Jiawei
year	2022
title	Machine Learning Modeling and Genetic Optimization of Adaptive Building Facade Towards the Light Environment
doi	https://doi.org/10.52842/conf.caadria.2022.1.141
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 141-150
summary	For adaptive facades, the dynamic integration of architectural and environmental information is essential but complex, especially for the performance of indoor light environments. This research proposes a new approach that combines computer-aided design methods and machine learning to enhance the efficiency of this process. The first step is to clarify the design factors of adaptive facade, exploring how parameterized typology models perform in simulation. Then interpretable machine learning is used to explain the contribution of adaptive facade parameters to light criteria (DLA, UDI, DGP) and build prediction models for light simulation. Finally, Wallacei X is used for multi-objective optimization, determines the optimal skin options under the corresponding light environment, and establishes the optimal operation model of the adaptive facades against changes in the light environment. This paper provides a reference for designers to decouple the influence of various factors of adaptive facades on the indoor light environment in the early design stage and carry out more efficient adaptive facades design driven by environmental performance.
keywords	Adaptive Facades, Light Environment, Machine learning, Light Simulation, Genetic Algorithm, SDG 3, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

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