Effect of Bituminous Material Rheology on Adhesion
Bibliographic record
Abstract
Bituminous materials are used in many civil engineering applications in which adhesion to a substrate is essential for good performance. Yet it is not possible to predict the adhesion of these materials. The particular case of bituminous crack sealants is of interest; the effect of sealant viscosity, aging, test temperature, and loading rates was investigated by means of a blister test. This test provided the bonding characteristics to a model aggregate in relation to interfacial fracture energy (IFE). From testing of several sealants, it was found that pouring viscosity affects adhesion and that higher viscosities help to attain higher IFEs. Temperature was found to play a key role on bonding characteristics and failure mechanism because it affected the viscoelastic properties of the sealant. The glass transition temperature (T g ) was found to have a governing role on bonding characteristics. At temperatures above T g , bond strength was found to be affected by sealant flow such that failure was flow related; that is, cohesive failure prevailed. At temperatures below the T g , at which sealants were stiff and bulk deformation was low, stress was directed toward the interface so that failure tended to be adhesive. In taking into account temperature and test rates, an IFE master curve was obtained for a sealant. Such a curve may be used in predicting and comparing sealant IFE.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".