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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	caadria2024_318
id	caadria2024_318
authors	Krncevic, Monika, Arjaghi, Niousha, Makki, Mohammed and Jordan, Mathers
year	2024
title	Re-imagining The Urban Development of Western Sydney: The Case Study of Oran Park
doi	https://doi.org/10.52842/conf.caadria.2024.1.353
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 353–362
summary	This paper addresses the challenges of rapid urban expansion in Western Sydney, Australia, using the suburb of Oran Park as a case study. With the region's population projected to more than double by 2041, and an expected influx of an additional 400,000 people by 2030, there is a pressing need for sustainable and environmentally responsive urban development. Current approaches have prioritised space utilisation over environmental and social considerations, leading to homogeneity and poor urban quality. In response to these challenges, this study proposes a four-stage generative model for Oran Park, emphasising environmental restoration, agricultural integration, and housing diversification. This model aims to balance economic growth with environmental sustainability, contrasting with the density-focused development prevalent in the area. By implementing multi-objective optimisation, this research presents an algorithm-driven approach to urban planning, catering to the diverse needs of the expanding population.
keywords	Sydney, Oran Park, Evolutionary Computation, Generative Algorithm, Urban
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	ecaade2024_215
id	ecaade2024_215
authors	Park, Hyejin; Gu, Hyeongmo; Hong, Soonmin; Choo, Seungyeon
year	2024
title	Comparison of GAN-based Spatial Layout Generation Research Focusing on AIBIM-Spacemaker and GAN-based Prior Research
doi	https://doi.org/10.52842/conf.ecaade.2024.1.539
source	Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 539–548
summary	Recent advancements in Large Language Models (LLM) and the emergence of ChatGPT are rapidly progressing Generative AI models, suggesting the possibility of AI replacing human creative activities. In architecture, where outcomes depend on human creative thinking, the pre-planning stage is crucial. Architectural planning involves decisions on mass, space layout, and space program, aiming for optimal design with a significant impact on subsequent stages. Creating a client-centric design within a given time prompts architects to search for diverse reference materials. However, finding comparable spatial layouts is challenging due to the predominant focus on materials, construction methods, and details. This study introduces AIBIM-Spacemaker, a Generative Adversarial Network (GAN)-based program we developed for generating spatial layouts through graphical composition of space programs. Focusing on a house with limited space usage but versatile layouts, the study collected 10,000 raster-based floor plan images, creating a training dataset annotated for spatial elements. Training this dataset using the YOLO model enabled automatic extraction of vector-based data representing spatial relationships from raster-based images. A GAN trained on this data resulted in AIBIM-Spacemaker, allowing users to create diverse spatial layouts. Executing a graph with nodes representing spaces and edges denoting relationships between doors and windows using the trained GAN produced varied spatial layouts. Verification, comparing actual ground truth values, GAN-generated outcomes, and architect-provided values confirmed the program's effectiveness in the planning stage. Performance was verified by comparing the program, learning method, dataset, and results developed in this study with previous studies on GAN-based spatial layout generation. This study identifies the potential for AI-based spatial layout generation, enhancing planning efficiency and contributing to intelligent design automation, with anticipated positive impacts on planning task efficiency.
keywords	Space Layout Generation, Space Program, Generative Adversarial Networks(GNN), You Only Look Once(YOLO), Pre-design stage
series	eCAADe
email
last changed	2024/11/17 22:05

_id	architectural_intelligence2024_31
id	architectural_intelligence2024_31
authors	Sofia Colabella, Alberto Pugnale, Jack Halls, Michael Minghi Park, László Mangliár & Markus Hudert
year	2024
title	Making the Hypar Up pavilion: (in)efficiencies of upcycling surplus timber products
doi	https://doi.org/https://doi.org/10.1007/s44223-024-00074-z
source	Architectural Intelligence Journal
summary	This paper illustrates the design and fabrication processes of the Hypar Up pavilion, which served as a proof-of-concept to demonstrate the viability of a design-to-fabrication workflow for complex yet modular architectural geometries that utilise small and planar timber offcuts geometries discretised as Planar Quadrilateral (PQ) meshes. By integrating computational design and optimisation with efficient manufacturing processes, this research highlights the technical challenges of repurposing materials with unknown characteristics, notably detailing solutions, and evaluates the efficiency of design-to-manufacturing workflows with surplus timber products, using a quantitative cost analysis of the fabrication and assembly phases. While exploring the potential of repurposing scrap wood into hypar-shaped modular construction components, this work expands on existing research on segmented shells and investigates methods and means to move beyond the use of shell structures as monolithic and static artefacts. The pavilion is intended as a 1:1 modular prototype that can be resized to accommodate different dimensions of the timber panel offcuts and potential applications to be tested in future applications, such as load-bearing walls and facade retrofitting.
series	Architectural Intelligence
email
last changed	2025/01/09 15:05

_id	caadria2024_31
id	caadria2024_31
authors	Wong, Nichol Long Hin, Crolla, Kristof, Hou, June-Hao, Hsu, Pei-Hsien and Cheng, Yu-Tung
year	2024
title	Curved Glulam Architecture Design Optimisation For Low-Tech Construction: The Fabrication and Construction of KATENARA
doi	https://doi.org/10.52842/conf.caadria.2024.3.181
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 3, pp. 181–190
summary	This paper reports on the research findings from the fabrication and construction of "KATENARA", a prototypical, hyper-lightweight, wooden pavilion built in the Dongshi Forestry Cultural Park, Dongshi, Taiwan in November 2023. KATENARA uses a suspended roof structure system optimised for low-tech production from glue-laminated (glulam) timber. The pavilion’s geometry is based on near-catenary-shaped glulam beams that are evolutionary algorithmically optimised for manufacture from a single mould. Structures based on suspended beam geometries substantially reduce material needs when compared with those relying on straight beams, as catenary beams operate in pure tension throughout, avoiding inefficient neutral fibres along the centreline and removing risk of buckling. Yet, their manufacture from glulam typically requires costly bespoke individual hardware setups. Shape optimisation for fabrication efficiencies substantially increases the tectonic system's applicability, as it facilitates more affordable implementation in low-tec fabrication environment.
keywords	Catenary, Timber Shell, Evolutionary Algorithm, Glue-laminated Timber, Low-tech, Affordable Construction.
series	CAADRIA
email
last changed	2024/11/17 22:05

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