Knowledge-Driven Inference of Hidden Structural Parameters in Ming–Qing Large Woodwork by Integrating Point Clouds and Traditional Construction Rules

To address the limited automation of 3D reconstruction caused by the inability of point clouds to represent the hidden structures and construction logic of Ming–Qing large timber buildings, this study proposes a hidden structural parameter inference method that integrates point clouds with traditional construction rules. First, a unified parameter space is established to provide a structured representation of building components and their associated parameters. Second, traditional construction knowledge is formalized into computable proportional, relational, and spatial constraints. Finally, hidden structural parameters are inferred through hierarchical constraint propagation, and the inferred results are used to generate HBIM components. The proposed method was validated using the sub-eave columns and their associated components of the Dabei Hall of Chongshan Temple in Taiyuan. Among 1848 hidden structural parameters, 1800 were successfully inferred, corresponding to a solvability rate of 97.4%. The results demonstrate that the proposed method can effectively infer hidden structural parameters under the available observations and construction-rule constraints and use the rule-consistent inference results to generate parametric HBIM components. This study extends HBIM beyond geometric representation toward knowledge-driven model representation, providing a knowledge-enhanced modeling approach for the digital documentation and structural understanding of traditional timber architecture.