Two condition indicators for building components based on reactive‐maintenance data
Bibliographic record
Abstract
Purpose Sustaining the safety and operability of the civil infrastructure assets, including buildings, is a complex undertaking that requires a perpetual cycle involving inspection, and further decisions for renewal fund allocation. Inspection, which is the basis for all subsequent decisions, however, is subjective, costly, and time‐consuming. To circumvent inspection problems, this paper aims to develop indicators of the condition of building components, without inspection, using reactive‐maintenance data. Design/methodology/approach For that purpose, sample reactive‐maintenance data of 88 schools are obtained from the Toronto District School Board in Canada. The data are then analysed to identify two condition indicators for building components: the number of reactive‐maintenance work orders per year; and the cost of reactive‐maintenance work orders per year. The analysis then identifies threshold values that differentiate the good, fair, poor, and critical conditions of components. Accordingly, a condition prediction system has been developed and discussed in this paper. Findings The system has great potential benefits in saving the time and cost associated with indiscriminate inspections, and in providing accurate and timely data for asset renewal decisions. Originality/value The paper introduces an essential component of a comprehensive framework for building asset management: condition prediction and inspection planning.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".