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	caadria2025_130
id	caadria2025_130
authors	Shen, Yanting, Wang, Xiaotong, Lin, Chen-Hsiang, Li, Shuyang, Pei, Wanyu and Stouffs, Rudi
year	2025
title	Forecasting Future Construction Demands: A Deep Learning approach for material stock circularity analysis of Singapore residential buildings
source	Dagmar Reinhardt, Nicolas Rogeau, Christiane M. Herr, Anastasia Globa, Jielin Chen, Taro Narahara (eds.), ARCHITECTURAL INFORMATICS - Proceedings of the 30th CAADRIA Conference, Tokyo, 22-29 March 2025, Volume 3, pp. 499–508
summary	Exploring sustainable urban development models for building material recycling is essential to achieving net-zero emissions. This study develops a Building Material Stock (BMS) prediction model, focusing on concrete and steel, and maps embodied carbon by identifying geographic hotspots for material recycling and flow. Specifically, to predict future demolition and construction trends at Singapore’s Housing Development Board (HDB), a Generative Adversarial Network (GAN)-based model is utilised to generate distributional images of HDB developments. A categorised map of construction sites from 2019 is used as input to generate the predicted public housing layout for 2024. These predictions are then integrated with material intensity (MI) and carbon emission factors to estimate floor area requirements and potential construction activity locations for future HDB projects. This approach establishes a closed-loop system aligned with the Sustainable Development Goals (SDGs), supporting material inventory and flow management, enhancing resource reuse efficiency, and mitigating urban development’s environmental impact. Moreover, the findings provide valuable insights for the Singapore government and industry organisations to expand the adoption of PPVC technology and circular economy practices in HDB development.
keywords	Material stock modelling, Material flow analysis, Residential construction, Embodied carbon, Deep learning
series	CAADRIA
email
last changed	2025/04/18 12:27

_id	caadria2019_252
id	caadria2019_252
authors	Tung, Hong-Cing and Hsu, Pei-Hsien
year	2019
title	An Algorithm of Rigid Foldable Tessellation Origami to Adapt to Free-Form Surfaces
doi	https://doi.org/10.52842/conf.caadria.2019.1.311
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 1, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 311-320
summary	When creating new kinds of origami, people design origami creases pattern on 2D plane. Consequently, people unable to precisely envision the 3D folded shape. However, in architecture, civil engineering and industrial applications, an accurate layout is important. This research is to compile an algorithm for creating origami forms with developability and flat-foldability on the target surface, more specifically, by setting a target surface first, generating a Miura-ori tessellation from the geometric configuration of a target surface. We achieve creating origami forms on a target surface, so that we can generate architectural surfaces with folded structure and accurately layout for construction. Our approach facilitates designing a free-form origami structure upon parametric and 3D modelling software for artists, designers and architects.
keywords	origami tessellation; free-form; grasshopper3D; rigid foldability; flat-foldability
series	CAADRIA
email
last changed	2022/06/07 07:57

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