Studies On Partial Model Extraction And Model Merging For BIM Based On File Instances

Partial model extraction and model merging are fundamental problems in the research field of BIM(Building Information Models),which is widely used in collaborative design,version management,data exchanging and rule checking.There are mainly three methods for partial model extraction,including database-based methods,ontology-based methods and IFC(Industry Foundation Classes)files based methods.The former two methods often lose information in file conversion.As for IFC files based methods,the existing implementations are sensitive to redundant data instances,and the obtained results include many syntax errors and irrelevant data instances.The previous method of model merging for BIM is mainly based on GUID(Global Unique Identifier)of data instances.However,this method is not stable since GUID is often changed when importing and exporting from different design platforms.To address the above issues,this paper proposes the algorithms of partial model extraction and model merging for BIM based on file instances.The main work of this paper is as follow.(1)An algorithm about instances construction based partial model extraction for IFC files.It begins with a classification for the extraction building elements in terms of user intent and a comprehensive analysis of the structure and content for the IFC file.Then establishes a hierarchical structure with bottom-up traversing.Elimates all the redundant instances and constructs a partial model with physical and nonphysical instances.In addition,a performance benchmark is provided by 292 IFC files in the expreriment part.In contrast with previous method,the presented algorithm takes the Ifc Owner History,the redundant and the syntax error instances into account which can get a more accurate,more compact and smaller partial model.(2)Another algorithm is proposed for instances matching based on the model merging for IFC files.This algorithm aims to match the physical instances in two IFC files.To detail,the non-geometrical information is first compared using content-based automatic comparison algorithm.Then,the geometrical information is matched based on the ananlysis of 3D shape representations,where the Hausdorff distance is used to measure the distance between the discretized point sets.Finally,based on the matched physical instances,all the repeated physical and irrelevant data instances are recursively deleted to form the merged file.The proposed approach ismore robust and insensitive to unstable GUID.Our method is superior to the GUID-matched approaches with 54% less instances on average.