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Geometry Discretization for BIM-based Interdisciplinary Data-exchange of Architectural Geometry to Structural Analysis Models

Topic Description

The primary focus of this research topic will be on the geometry data exchange between architecture and structural design and analysis modelling, where the data and model exchange process through the IFC data format still requires compromises in modelling techniques and large re-adaptations both on the architect’s and on the engineer’s side. The development of a method for the discretization of BIM-geometry is required to bridge the semantic differences and varying needs in the modelling of architecture and structural engineering (e.g., slab-to-slab column in architecture vs. continuous column in FEM-models.). Algorithms are to be developed to solve the geometrical intersections for gaps occurring in IFC (coordination view) geometry with the aim of producing valid and ready-to-use models for FEM simulation. A software should be developed allowing users to import IFC geometry, run automation-procedures for model repair and to graphically control the repair results before passing on the model into FEM software. This tool is meant to significantly decrease the effort of re-adapting architectural BIM geometry for structural simulation purposes and hence to enable a more effective interdisciplinary BIM-supported design process.

Qualification Profile

A suitable background in civil engineering or architecture, earned through a pertinent academic degree programme is required, with specialisation in computer science (e.g. computing in civil engineering master programme).

Supervisor

This topic is supervised by a team of 3 supervisors. Lead supervisor is Iva Kovacic (Institute of Interdisciplinary Building Process Management). Additional supervisors are Georg Suter (Institute of Architectural Sciences) and Helmut Pottmann (Institute of Discrete Mathematics and Geometry).

Geometry Discretization for BIM-based Interdisciplinary Data-exchange of Architectural Geometry to Structural Analysis Models