Information commissioning: bridging the gap between digital and physical built assets
Bibliographic record
Abstract
Purpose As the use of building information modeling (BIM) for facilities management (FM) continues to grow, questions remain around the quality and completeness of digital assets to support FM practices. This paper aims to examine the current gap between digital and physical assets in the absence of formal information requirements and its impact on the handover process. Design/methodology/approach An action-research was carried out with a large public organization to understand the challenges of their current FM processes and the steps required in developing an asset information model (AIM) from a project information model (PIM). A mixed method approach was employed with interviews, document analysis and an exploratory pilot case study. Findings This paper investigates the process, the challenges and the level of effort of the information commissioning process to create a fit-for-use AIM. Four distinct steps were identified in the process as follows: analyzing the handover PIM and documents, extracting FM-specific information, populating the model with the information and attaching operations and maintenance (O&M) documents. The research highlights the significant amount of effort that is required when no specific asset information requirements are formulated at the project onset. Practical implications The paper presents an information commissioning process that helps to develop an AIM from a PIM. Understanding the impact of the lack of requirements on the information commissioning process can help asset owners understand the importance of defining and articulating their information requirements up front. Originality/value This paper provides empirical evidence of the impact of the absence of formal information requirements on the development of a fit-for-use AIM.
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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 | 0.031 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".