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	caadria2022_464
id	caadria2022_464
authors	Liu, Xinyu and van Ameijde, Jeroen
year	2022
title	Data-driven Research on Street Environmental Qualities and Vitality Using GIS Mapping and Machine Learning, a Case Study of Ma On Shan, Hong Kong
doi	https://doi.org/10.52842/conf.caadria.2022.1.485
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. 485-494
summary	In a post-carbon framework, data-driven methods can be used to assess the environmental quality and sustainability of urban streetscape. Streets are an important part of people's daily lives and provide places for social interaction. Therefore, in this study, the relationship between street quality and street vibrancy is measured using the new town of Ma On Shan, Hong Kong as a study area. Firstly, machine learning was used to identify the physical features of streets through geographic information collection and streetscape image acquisition. Secondly, previous measurement algorithms are combined to calculate the greenness, walkability, safety, imageability, enclosure, and complexity of streets. Thirdly, secondary calculations and visualisations were carried out on a Geographic Information System (GIS) platform to observe the current distribution of street qualities. Finally, the relationship between street quality and vibrancy was analysed using SPSS statistical analysis software. The results show that walkability has a positive effect on street vitality, whereas safety and complexity have a negative effect on street vitality. This study demonstrates how the quantitative assessment of urban street environments can be used as a reference for building a green, low-carbon, healthy, and walkable city.
keywords	Street Quality, Geographic Information Systems, Machine Learning, Image Segmentation, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ijac202220107
id	ijac202220107
authors	Yu Ma, Chun; Jeroen van Ameijde
year	2022
title	Adaptable modular construction systems and multi-objective optimisation strategies for mass-customised housing: A new user-driven paradigm for high-rise living in Hong Kong
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 1, pp. 96–113
summary	There has been a recent increase in the exploration of ‘the discrete’ in architecture, speculating on how an integrated approach to design, fabrication, assembly and inhabitation can disrupt the traditional investment- and decision-making models in the housing industry. Strategically designed part-to-whole systems allow for differentiation and reconfiguration, and the incorporation of different end-user’ requirements. This potential of ‘democratising’ housing production requires further research into how the negotiation between multiple stakeholders’ preferences can be guided through digital methods. This paper presents a research-by-design project that applies a digital and discrete material system to high-rise housing in Hong Kong, a typology which often features high degrees of standardisation. Through the development of an adaptable modular con- struction system and a multi-objective optimisation workflow, a system is explored that addresses the challenges of high-rise construction, and of customising high-density housing. The case study project demonstrates the ability of the workflow’s evolutionary algorithm to balance complex requirements including maximising views, daylight access and internal connectivity according to diverse user requirements.
keywords	Participatory Design, Generative Design, Multi-Objective Optimisation, Adaptable Architecture, High-rise Housing, Hong Kong
series	journal
last changed	2024/04/17 14:29

_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	sigradi2022_298
id	sigradi2022_298
authors	Perry, Isha N.; Xue, Zhouyi; Huang, Hui-Ling; Crispe, Nikita; Vegas, Gonzalo; Swarts, Matthew; Gomez Z., Paula
year	2022
title	Human Behavior Simulations to Determine Best Strategies for Reducing COVID-19 Risk in Schools
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 39–50
summary	The dynamics of COVID-19 spread have been studied from an epidemiological perspective, at city, country, and global scales (Rabajante, 2020, Ma, 2020, and Giuliani et al., 2020), although after two years of the pandemic we know that viruses spread mostly through built environments. This study is part of the Spatiotemporal Modeling of COVID-19 spread in buildings research (Gomez, Hadi, and Kemenova et al., 2020 and 2021), which proposes a multidimensional model that integrates spatial configurations, temporal use of spaces, and virus characteristics into one multidimensional model. This paper presents a specific branch of this model that analyzes the behavioral parameters, such as vaccination, masking, and mRNA booster rates, and compares them to reducing room occupancy. We focused on human behavior, specifically human interactions within six feet. We utilized the multipurpose simulation software, AnyLogic, to quantify individual exposure to the virus, in the high school building by Perkins and Will. The results show how the most effective solution, reducing the occupancy rates or redesigning layouts, being the most impractical one, is as effective as 80% of the population getting a third boost.
keywords	Spatiotemporal Modeling, Behavior Analytics, COVID-19 Spread, Agent-Based Simulation, COVID-19 Prevention
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	cdrf2022_78
id	cdrf2022_78
authors	Sharif Anouar, Adam Anouar, and Ayoub Lharchi
title	Heritage Information Modeling: The Case of Chellah’s Gate
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_7
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	This paper aims to propose an integrated workflow for the digitization of the built cultural heritage. To this end, we leverage the power of computational tools and the relevancy of Building Information Modeling (BIM) process to overcome the limitations and challenges faced by Scan-to-BIM. We describe the automatic generation of an as-built BIM model of a heritage building in a three-step procedure. Firstly, we outline the data acquisition method of the point cloud. Secondly, we describe the automatic processing and segmentation of the point cloud according to architectural elements using Machine Learning. Then, we tested and compared various meshing algorithms and utilized a combination depending on the desired level of details. Lastly, the resulting geometry is converted into a BIM object that will be subsequently semantically labeled. We used a UNESCO world heritage in Morocco—Chellah, as a case study to test the robustness of our protocol.
email
last changed	2024/05/29 14:02

_id	acadia22_392
id	acadia22_392
authors	Soana, Valentina; Shi, Yichao; Lin, Tongyao; Ma, Yiting; Dai, Ling
year	2022
title	LOOPS; A Mobile, Shape-Changing Architectural System: Robotically-Actuated Bending-Active Tensile Hybrid Modules
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. 392-405.
summary	LOOPS is a mobile and shape-changing architectural system that achieves multiple states through robotically controlled elastic material deformations. LOOPS is part of a wider research agenda on elastic robotic structures (ERS). ERS are lightweight, adaptive and can perform multiple behaviors with material and actuation efficiency, leveraging the capability of elastic materials to undertake large deformations.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:04

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