Overview of Durability and Methods of Service Life Prediction for Building Adhesive and Sealant Products
Bibliographic record
Abstract
Jointing and sealant products play an important role in maintaining the weathertightness of buildings. Despite being relatively inexpensive, in respect to the overall cost of a building, they help minimize water penetration into and air movement across the building envelope. As such, sealant failures can result in undue air and moisture transfer across the building envelope, a loss of energy efficiency, and, given moisture ingress to the building envelope, the degradation of materials and components therein. To avoid serious degradation of building components and possible damage to the structural integrity of buildings as well as the potential significant and costly consequences of sealant failure, it is important to understand the underlying reasons for the degradation of adhesive and sealant products that, in turn, permit predicting their life expectancy. In this paper, a review is provided on advances in the durability, service life, and service life prediction (SLP) of sealants and adhesives in the building domain over the past decade. In this overview, emphasis is placed on work derived from relevant technical committees, together with symposia and conferences focused on the SLP of polymer materials. The information serves both as a primer on the topic and an update on previous contributions in this area. Examples of current trends in SLP are presented, along with expectations for future research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".