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An edge-weighted graph triumvirate to represent modular building layouts.
- Source :
-
Automation in Construction . Jan2024, Vol. 157, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- Representing building layouts as graphs can extract critical design patterns that would facilitate space syntax analyses as well as design mining and automation but traditional approaches (e.g., non-weighted adjacent graphs) encountered problems in modular buildings, as they are largely shaped under the principle of 'modularity' rather than freeform cast in-situ elements. This paper attempts to develop a novel analytical tool called ModularGraph to represent modular building layouts (MBLs) as graphs considering their unique adjacency, connectivity, and conjoint relationships in a triumvirate. It does so by developing a prototype then applying it to 36 modular buildings for iteration, finetuning, and finalizing. It is found that ModularGraph can effectively translate heterogeneous forms of MBLs into unified graph-based representations with rich graphic and semantic information. This study not only contributes an innovative graph analytic tool for design pattern mining, but also lays a stepping stone towards generative AI for modular building design. • Propose a graph triumvirate structure for modular building layout representation. • Incorporate a weight mechanism to indicate the strength of spatial relationships for better semantic interpretations. • Perform a quantitative analysis on the layouts of 36 modular buildings in Hong Kong using the proposed framework. • Uncover patterns in modular building layout designs exemplified by ModularGraph. • Develop a machine learning model to learn knowledge from layout designs represented by ModularGraph. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09265805
- Volume :
- 157
- Database :
- Academic Search Index
- Journal :
- Automation in Construction
- Publication Type :
- Academic Journal
- Accession number :
- 173693574
- Full Text :
- https://doi.org/10.1016/j.autcon.2023.105140