A Novel Approach for Classification of Structural Elements in a 3D Model by Supervised Learning

Abstract

Development of Computer Aided Design (CAD) has made a transition from 2D to 3D architectural representation and today, designers directly work with 3D digital models for the initial design process. While these digital models are being developed, layering and labelling of 3D geometries in a model become very crucial for a detailed design phase. However, when the number of geometries increases, the process of labelling and layering becomes simple labor. Hence, this paper proposes automation for labelling and layering of segmented 3D digital models based on architectural elements. In various parametric design environments (Rhinoceros, Grasshopper, Grasshopper Python and Grasshopper Python Remote), a training set is generated and applied to supervised learning algorithms to label architectural elements. Automation of the labelling and layering 3D geometries not only advances the workflow performance of design process but also introduces wider range of classification with simple features. Additionally, this research discovers advantages and disadvantages of alternative classification algorithms for such an architectural problem.