Exploring the use of BIM to support space planning and asset information management in a large public organization
Bibliographic record
Abstract
Building information modeling (BIM) enables the exchange of structured geometric and non-geometric information across design, construction, and operations, allowing owners and project teams to access consistent data throughout the asset lifecycle. Despite its demonstrated value during design and construction, BIM adoption for operational use remains limited. In particular, empirical research on how large public-sector owners, who manage extensive and complex facility portfolios, can derive value from BIM during operations is scarce. Existing studies are largely confined to pilot projects or private-sector contexts, leaving a gap in understanding how BIM can be practically applied in large public-sector environments. This research addresses this gap by investigating how BIM can support two key operational functions, i.e., space planning and asset information management, within a public-sector owner organization in British Columbia, Canada. A design science research approach combined with a case study method was adopted through direct collaboration with the organization on two real-world projects. Each case study followed three phases: benchmarking, prototyping, and validation. A mixed-method strategy was employed, including document and model analyses, data reviews, tool assessments, and semi-structured stakeholder interviews to examine existing workflows, tools, and data practices. The benchmarking phase identified challenges such as inefficient handovers, disconnected systems, and inconsistent and inaccurate information. The prototyping phase developed BIM-enabled prototypes, including (1) an interactive Power BI dashboard integrating spatial and occupancy data to support space utilization analysis, and (2) an asset information model (AIM) based on open standards to centralize and standardize operational data for facilities and asset management. Validation interviews with planning, facilities, and asset management stakeholders confirmed the identified challenges and assessed the usability, perceived value, and adoption conditions of the prototypes. Findings show that BIM-enabled visual analytics improved the granularity and timeliness of utilization insights, supporting data-driven decision-making for hybrid workplace strategies. The standardized BIM-based AIM enhanced data accessibility, reduced redundant manual work, and provided a foundation for integration with maintenance and capital-planning systems. Overall, this research contributes empirical evidence on BIM for operations in a real public-sector context and provides insights for owner organizations seeking to better connect project delivery with facility operations through data-centric information management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".