An approach for extracting BIM component instance information integrating IFC semantic data and geometric similarity
Building information modeling (BIM)accurately captures architectural structures,component compositions,and semantic attributes. It plays a crucial role in the digital management of smart cities' spatiotemporal frameworks and the entire building lifecycle,including planning,construction,maintenance,and operation. However,the massive data scale,complex reference relationships,and hierarchical structures of BIM models present challenges in extracting component instance information,hindering lightweight data transmission,seamless visualization,and BIM analysis within current city information modeling (CIM)platforms. To address this issue,we introduce an innovative approach that combines IFC semantics with geometric similarity. Utilizing ICP and the Hausdorff distance metric,we attain a high level of precision in extracting BIM component instances. Furthermore,we present a specialized method tailored to the extraction of common extruded components. Our comprehensive evaluation,encompassing five diverse BIM disciplines,demonstrates exceptional results,including a 29. 79％ reduction in file sizes,a 79. 41％ component instantiation rate,and a 22. 47％solid compression rate. Impressively,each instantiated component encompasses an average of 49. 24 sub-components. Our method excels in extracting IFC-based BIM component instances,facilitating the efficient transformation of extensive models.
万方数据
维普
