Geometry simplification according to semantic constraints

Abstract

The building industry and facility management is in a state of upheaval: The complexity of the real world is now represented in its digital counterpart. The established object-based file format “Industrial Foundation Classes (IFC)” developed by the International Alliance for Interoperability facilitates interoperability in the context of Building Information Modeling. Unfortunately, there is no feasible workflow for filtering energy-related information, e.g. a streamlined version of the building geometry. Simplification methods often fail on CAD data that is ignorant of domain specific semantic information (i.e. functional differences between a door and stucco are not reflected in the geometry and are therefore often ignored). With EU law now requiring energy performance certificates to be issued for all buildings, energy performance analysis becomes an increasingly important topic. Accurate, yet efficient calculation depends on simple building models. However, typical IFC models contain a lot of irrelevant data, in particular geometric representations, which are too detailed for energy performance analysis. Therefore, we propose an algorithm that extracts input models suitable for calculations directly from IFC models in a semi-automatic process. The key aspect of the algorithm is geometry simplification subject to semantic and functional groups; more specifically, the 3D representations of walls, slabs, windows, doors, etc. are reduced to a collection of surfaces describing the building’s thermal shell on one hand, and the material layers associated with it on the other hand. This simplification takes into account semantic constraints and expert knowledge. Furthermore, it works on “real-world” data; i.e. it is robust towards incomplete, imperfect and inconsistent data.