A Framework for Generic Semantic Enrichment of BIM Models
Publication: Journal of Computing in Civil Engineering
Volume 38, Issue 1
Abstract
The design intent and many meaningful semantics in building information modeling (BIM) models are often implicit, and some explicit semantics are lost during model exchanges. The missing information can be artificially supplemented through a process called semantic enrichment. However, previous research on semantic enrichment has primarily focused on specific tasks, leading to a limited scope of predictions, and lacks comprehensive, seamless approaches. In this study, we aim to infer BIM semantics from fundamental model data, pure object geometries, and organize the predicted results into a graph-based common data environment (CDE) to support intelligent applications. Consequently, we propose a framework of generic semantic enrichment that includes four fundamental tasks in the context of graphs and a process control mechanism to execute a set of tools in a proper sequence. To validate its feasibility, we selected a real-world apartment model and developed six tools to generate the graph-based CDE from its object geometries. Additionally, applications were implemented on the graph-based CDE, such as (1) enriching a pure geometry model from SketchUp to a BIM model in Revit; and (2) easing interoperability problems by reconstructing ArchiCAD models in Revit or downgrading Revit models to earlier versions. The experiment’s success demonstrates the feasibility of constructing BIM graphs from object geometries through semantic enrichment to enable intelligent applications. This study establishes a theoretical foundation for graph semantic enrichment and opens a door to further explore intelligent applications on BIM graphs.
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Data Availability Statement
Some data related to Exp 1 Object type classification are available from the corresponding author upon reasonable request.
Acknowledgments
The work is part of the Cloud-based Building Information Modelling (CBIM) project, a European Training Network. The CBIM project receives funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant with agreement No. 860555.
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Journal of Computing in Civil Engineering
Volume 38 • Issue 1 • January 2024
Copyright
© 2023 American Society of Civil Engineers.
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Received: May 3, 2023
Accepted: Jul 30, 2023
Published online: Oct 3, 2023
Published in print: Jan 1, 2024
Discussion open until: Mar 3, 2024
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