Bibliographic record
Abstract
Purpose This paper aims to respond to the high cost of facility management-enabled building information model (FM-BIM) creation and maintenance, a significant and under-researched barrier to adoption for existing buildings. The resultant approach focuses on only value-adding content (“Lean”) developed flexibly and iteratively in collaboration with end-users (“Agile”). Design/methodology/approach Five case studies were developed for university and hospital buildings in collaboration with end-users, guided by the process presented. These informed the refinement of a robust and flexible approach to increase BIM functionality with minimal geometry, focusing instead on the development of specific parameters to map semantic information necessary for each desired FM use. Findings The resulting BIM provided a breadth of model functionality with minimal modeling effort: 15 hours average implementation time per supported FM use. This low level of effort was achieved by limiting geometry to where it is necessary for the FM use implementation. Instead, the model incorporated the majority of geometry by reference and focused on semantic and topological parameters to house FM information. Research limitations/implications This study provides the basis for a new ontology structure focused on defining the rules for hosting asset management data (host entity, parameter type and characteristics) to reduce the reliance on complex geometric model development. Practical implications By prioritizing highly beneficial applications, early investment is minimized, providing quick returns at low risk, demonstrating the value of FM-BIM to end-users. Originality/value The Lean-Agile approach addresses the known research gap of low-effort, flexible approaches to FM-BIM model creation and maintenance and its effectiveness is analyzed through five case studies.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 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".