![]() Geometric Deep Learning (GDL) is introduced in two steps a discussion about the benefits of GCN (Graph Convolutional Networks) is given before a set of experiments on BIM element datasets are conducted. In this work, we leverage the benefits of a 3D light-weight, geometric algorithm to autonomously generate meaningful geometric features assisting shape classification in erroneous design models and pre-segmented point clouds. At the same time, Scan-to-BIM processes still require considerable manual effort to identify subclass element geometry. Mis-or unclassified building elements are a common issue and can lead to tedious manual reworks. The high complexity and low rigidity in BIM model exchange standards such as IFC result in considerable differences in data quality and impede the direct data availability for analytics-based decision support. Automated parsing of design data will increasingly be a prerequisite for efficient data- and analytics-driven management of building portfolios.
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