Despite the operational phase being the most cost-intensive in a building’s lifecycle, Facility Management (FM) resource optimization continues to face challenges due to fragmented and low-structured data. Building Information Modeling (BIM) offers a centralized data environment, but interoperability gaps persist between design-oriented BIM models and operational Computerized Maintenance Management Systems (CMMSs). This paper presents a scalable, standards-based methodology for BIM-CMMS integration based on the extension of Industry Foundation Classes (IFCs) and the enrichment of FM data. The proposed Python-based application leverages the open-source IfcOpenShell library to inject custom, FM-specific Property Sets (Psets), including asset condition, criticality, and maintenance schedules, directly into IFC entities. The approach transforms standard IFC files into data-rich Asset Information Models (AIMs) without relying on proprietary middleware. The methodology was validated through two residential building case studies. IFC models were successfully checked through the buildingSMART validation service, providing full interoperability across multiple IFC-compatible platforms. Integration with OpenMAINT automatically generates a complete asset database, minimizing manual data entry and reducing inconsistencies. The results confirm the feasibility of a repeatable open-standard workflow. The future development is the definition of a functional/cognitive DT, with the scope of improving the lifecycle BIM model quality and enhancing the efficiency of facility operations.

An open standard methodology for BIM-CMMS integration: enhancing facility operations through IFC-based data enrichment / Piras, G., Rossini, F.L., Muzi, F., Sagayaraj, M.. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 16:10(2026). [10.3390/app16104642]

An open standard methodology for BIM-CMMS integration: enhancing facility operations through IFC-based data enrichment

Piras G.
Primo
;
Rossini F. L.
Secondo
;
Muzi F.
Penultimo
;
2026

Abstract

Despite the operational phase being the most cost-intensive in a building’s lifecycle, Facility Management (FM) resource optimization continues to face challenges due to fragmented and low-structured data. Building Information Modeling (BIM) offers a centralized data environment, but interoperability gaps persist between design-oriented BIM models and operational Computerized Maintenance Management Systems (CMMSs). This paper presents a scalable, standards-based methodology for BIM-CMMS integration based on the extension of Industry Foundation Classes (IFCs) and the enrichment of FM data. The proposed Python-based application leverages the open-source IfcOpenShell library to inject custom, FM-specific Property Sets (Psets), including asset condition, criticality, and maintenance schedules, directly into IFC entities. The approach transforms standard IFC files into data-rich Asset Information Models (AIMs) without relying on proprietary middleware. The methodology was validated through two residential building case studies. IFC models were successfully checked through the buildingSMART validation service, providing full interoperability across multiple IFC-compatible platforms. Integration with OpenMAINT automatically generates a complete asset database, minimizing manual data entry and reducing inconsistencies. The results confirm the feasibility of a repeatable open-standard workflow. The future development is the definition of a functional/cognitive DT, with the scope of improving the lifecycle BIM model quality and enhancing the efficiency of facility operations.
2026
asset management; building information modeling (BIM); CMMS; digital twin; facility management (FM); IfcOpenShell; industry foundation classes (IFCs); interoperability; open standards
01 Pubblicazione su rivista::01a Articolo in rivista
An open standard methodology for BIM-CMMS integration: enhancing facility operations through IFC-based data enrichment / Piras, G., Rossini, F.L., Muzi, F., Sagayaraj, M.. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 16:10(2026). [10.3390/app16104642]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1771758
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