A systematic methodology for design of retrofit actions with longevity
Bibliographic record
Abstract
This article presents a method for the design of retrofit actions with focus on energy savings that permits a systematic and thorough assessment of potential failures, anticipated maintenance and the expected durability of the retrofit actions. The proposed method combines the use of failure mode and effect analysis (FMEA) to permit identifying likely failure modes from which maintenance actions could be planned and the limit states (LS) method to assess the durability of the given retrofit action. One case study was completed to illustrate the application of: (1) the FMEA and LS method and (2) the proposed method for a retrofit action of an internal insulated solid wall of masonry bonded with lightweight aggregate concrete and floor division of concrete. It was evident that FMEA is useful regarding failure-mode identification and maintenance planning, and the LS method has its strength in durability assessment. Combining the use of both the FMEA and LS methods allowed improved design of new energy-saving retrofit actions, given that a thorough risk assessment was possible that included a decision-making process on maintenance planning, durability assessment and decision on potential redesign of retrofit actions.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".