Dynamic System for Prioritizing and Accelerating Inspections to Support Capital Renewal of Buildings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Maintaining the serviceability of a large number of buildings is a huge challenge that requires a perpetual cycle of inspections and capital renewal actions. Inspection, however, is subjective, costly, and time-consuming. To substantially reduce inspection effort, this research proposes a dynamic inspection-priority system (DIPS) that analyzes recent records of reactive-maintenance data, which is often not utilized for capital renewal, to establish a condition prediction mechanism for most building components without inspection. DIPS also incorporates procedures to prioritize and schedule key inspection tasks in addition to a tablet application designed to allow all-on-site visual inspection by marking on two-dimensional (2D) digital plans. A prototype system of DIPS has been developed and tested using the data of 88 schools in Canada and proved its usefulness as an efficient approach to improve the internal inspection practices of organizations that own a large number of buildings. This research contributes to integrating the maintenance and capital renewal functions, introducing a simple visual approach to inspection, saving inspection time and cost, and ultimately improving the economics of the multibillion-dollar business of capital renewal.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it