A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
An edge-weighted graph triumvirate to represent modular building layouts
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.
Highlights 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.
An edge-weighted graph triumvirate to represent modular building layouts
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.
Highlights 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.
An edge-weighted graph triumvirate to represent modular building layouts
Lin, Xiao (author) / Chen, Junjie (author) / Lu, Weisheng (author) / Guo, Hongling (author)
2023-10-16
Article (Journal)
Electronic Resource
English
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