Enabling AI-driven modular building design: an auto-decoder approach for IFC 3D geometry representation

Modular building design requires numerous context-dependent component variants that traditional constraint-based methods cannot exhaustively enumerate. Industry Foundation Classes (IFC) models encode rich spatial and semantic context from completed modular projects. This context could enable Artificial Intelligence (AI) models to generate component variants and complement constraint-based methods. However, IFC 3D geometry that carries spatial context is not directly usable by AI models. This stems from IFC’s complex data structure. To address this limitation, this paper proposes a readily deployable auto-decoder method that produces AI-compatible vectors from IFC geometry. First, an IFC export strategy that retains component spatial context is employed. Second, a sampling method that pairs 3D points with their distances to the nearest surface is applied. Third, an auto-decoder neural network that jointly optimises per-component vectors and the model weights is presented, yielding context-aware representation vectors for modular components. Finally, an octree-based decoder for accurate geometry recovery from vectors is employed. Experiments on real-world modular project data demonstrate that the resulting vectors preserve geometric fidelity and support component variant generation. Geometric fidelity is confirmed by the mean and maximum surface reconstruction errors of 14.57 mm and 51.94 mm, sufficient for modular building design analysis. Support for component variant generation is evidenced by geometric interpolation linearity exceeding 0.98 out of 1, showing excellent variant generation suitability. This method makes IFC spatial context accessible to AI-driven modular design methods, transforming Design for Manufacture and Assembly (DfMA) data into actionable knowledge. Codes available on GitHub.