Development and evaluation of a maturity assessment tool for integrating building information modelling into operations and maintenance phase of buildings

Abstract

The emergence of advanced digital tools such as building information modelling (BIM) has transformed the concept of building management in the construction sector. Despite the promising benefits of BIM in terms of successful project deliveries and enhanced building performance, construction firms struggle to view assets from the perspective of a whole asset life cycle context, and consequently struggle to derive their full potentials via BIM, especially in the operations and maintenance (O&M) phase. One of the fundamental reasons behind this challenge is the lack of empirical understanding of what information should be collected to support efficient and effective operation and maintenance management. Various BIM capability maturity models have been proposed to support BIM integrations into O&M management, but most of these models fail to capture detailed information requirements (IRs). Furthermore, there is limited insights regarding the prioritisations of these IRs to guide construction firms and asset owners towards operational competitiveness and excellence. To address these gaps, this study aims to develop a BIM and O&M maturity assessment model (BIM and O&M MM) by drawing on the capability maturity model integration (CMMI) concept, and utilising Delphi technique and maturity grids. The developed tool enables asset owners and managers to assess BIM and O&M integration maturity of built assets, based on a five-level maturity scheme across 21 IRs. To ensure effectiveness and practicality of the tool, 22 experts assessed the tool, and the results indicate its ability to succinctly measure the maturity level of BIM and O&M integration, thereby enabling construction managers and asset owners to identify areas of strengths and deficiencies for prioritising capital investments. The BIM and O&M MM ensures stakeholder alignment and addresses real-world O&M challenges. It enhances data consistency, optimised long-term asset management, data-driven decision-making, and provides a foundation for future research and technology integration.