MOST‐FIT: Support Techniques for Inspection and Life Cycle Optimization in Building Asset Management
Bibliographic record
Abstract
Abstract: Among the various asset management functions that support capital renewal decisions, both inspection and fund-allocation are very challenging in terms of time, cost, and technology. To support these functions, this article introduces two unique techniques that can be implemented, individually or combined, into any asset management system: (1) Focused-Inspection Technique (FIT); and (2) Multiple Optimization and Segmentation Technique (MOST). FIT improves inspection by incorporating an analysis of reactive-maintenance data to predict components’ conditions, thus saving the time and cost of indiscriminate inspections. The MOST technique, on the other hand, has a unique formulation for large-scale optimization involving thousands of assets simultaneously, thus maximizes the return of renewal dollars. The article provides a description of the MOST-FIT techniques and discusses their implementation in a prototype system that suits a large school board in North America. The proposed techniques are innovative and help organizations with large building assets improve the overall condition of their inventory with highest return on the limited renewal budget.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".