CBIM: A Graph-based Approach to Enhance Interoperability Using Semantic Enrichment

TL;DR

This paper presents a semantic enrichment method to create BIM knowledge graphs from geometric data, improving interoperability across different BIM software versions through machine learning and rule-based techniques.

Contribution

It introduces a novel approach combining machine learning and rule-based methods to generate BIM knowledge graphs from geometries, enhancing interoperability in construction industry workflows.

Findings

Successfully generated BIM graphs from object geometries.

Enabled reconstruction of architectural models across different Revit versions.

Demonstrated feasibility of semantic enrichment for BIM interoperability.

Abstract

Interoperability remains a challenge in the construction industry. In this study, we propose a semantic enrichment approach to construct BIM knowledge graphs from pure building object geometries and demonstrate its potential to support BIM interoperability. Our approach involves machine learning and rule-based methods for object classification, relationship determination (e.g., hosting and adjacent) and attribute computation. The enriched results are compiled into a BIM graph. A case study was conducted to illustrate the approach for facilitating interoperability between different versions of the BIM authoring software Autodesk Revit. First, pure object geometries of an architectural apartment model were exported from Revit 2023 and fed into the developed tools in sequence to generate a BIM graph. Then, essential information was extracted from the graph and used to reconstruct an architectural model in the version 2022 of Revit. Upon examination, the reconstructed model was consistent with the original one. The success of this experiment demonstrates the feasibility of generating a BIM graph from object geometries and utilizing it to support interoperability.