Work

A generative artificial intelligence approach to modular skeletal framework modeling: Bamboo stilt houses as a case study

Year: 2025

Type: article

Abstract: This paper presents a new generative artificial intelligence (AI) approach for creating modular skeletal frameworks, using vernacular bamboo stilt houses as examples to investigate an innovative methodological perspective. By transforming building skeletons to connected graphs, our method uses Variational Graph Autoencoders (VGAE) and Graph Sample and Aggregate (GraphSAGE) to generate 3D modular components based on spatial constraints set by users, such as axis grids and chosen room areas. The graph representation encodes structural elements as edges and their connections as nodes, maintaining critical dimensional constraints and spatial relationships. Using data from bamboo stilt houses built without architects, we make a specialized dataset of geometric skeletons for model training. Experimental results demonstrate the effectiveness of our approach in capturing the distribution of featured elements in building frameworks and in generating structurally sound designs, with GraphSAGE showing better performance compared to alternative methods. The probabilistic edge prediction approach allows for a collaborative human-AI design process, empowering designers while utilizing computational capabilities. The inherent flexibility of the graph-based representation makes it adaptable to a wide range of materials and scales.

Source: Frontiers of Architectural Research

DOI: 10.1016/j.foar.2025.06.004

Authors: Xianchuan Meng, Jiadong Liang, Ximing Zhong

Institutions: Nanjing University, Architectural Association School of Architecture, Aalto University

Language: English

Topic: Architecture and Computational Design

Subfield: Architecture

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,500