BIM for FM: understanding information quality issues in terms of compliance with owner’s Building Information Modeling Requirements
Bibliographic record
Abstract
The promise of Building Information Modeling (BIM) for Facilities Management (FM) is based upon building information models as reliable sources of information for decisions during a facility’s life cycle, from the planning to end of life. However, the premise of BIM as an enabler for the delivery of reliable information for FM has numerous challenges. Previous studies have shown that the quality of information provided through current design practices with BIM is inadequate for FM. These information quality (IQ) issues are mostly related to incomplete, inaccurate, inconsistent, and unintelligible facility information that ultimately reduce the usefulness of BIM-based information for FM purposes. In order to support BIM-enabled delivery of useful asset information for FM, certain IQ criteria must be met. Based on three ethnographic case studies, including the analysis of more than two thousand documented BIM for FM-related compliance issues, this research identifies ten key IQ criteria in design BIMs that must be considered to reliably support BIM use for FM, correlates these IQ criteria with key IQ dimensions identified in the literature to reflect their frequency of occurrence, and identifies sources of IQ issues in BIM for FM within design practice. A mixed-method approach for data collection from the case studies is adopted, including document analysis, semi-structured interviews, meeting observation, and a survey. The data collected are analyzed through an iterative coding process, in which the themes emerged are refined and tested as part of a grounded theory approach. This study contributes to the development of the theoretical concept of IQ in BIM for FM that is grounded in data from actual projects with stringent BIM requirements for FM and thorough compliance processes. As a practical contribution, the findings in this study should enable owners and designers to develop a more optimized asset information delivery process, increasing the value of the information in design BIMs for operations with minimal impact on current modeling practices.
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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 | 0.071 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| 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".