Advances in service life prediction - an overview of durability and methods of service life prediction for non-structural building components
Bibliographic record
Abstract
An overview is provided of durability, service life (SL) and service life prediction (SLP) research in the construction domain over the past decades with emphasis on the activities of the CIB W080 working commission and related RILEM technical committees working in this area. The information serves as a primer on the topic and offers useful references on SL and SLP methods for the principal building components such as wood, sealants, coatings, roofing, and rendered cladding. As well, the SL methods developed for more complex construction components, such as insulated glass units and solar collectors, are summarised and serve to illustrate the approaches taken when estimating the SL of multifaceted building assemblies. A key component to SLP research within the CIB W080 has been interest in making the outgrowth of the research accessible to the practitioner. As such the dissemination of SL information in the form of building codes and standards are addressed and the prominence of information technologies, such as the Internet, in facilitating the dissemination process is also touched upon. Finally, examples reflecting current trends in SLP are presented and expectations for future research focus are offered.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".