Empirical transfer functions for foam glass aggregates insulation used in flexible pavement layered systems
Bibliographic record
Abstract
Pavement design in cold regions is challenging due to the difficult conditions of soils, humidity, and temperatures. Insulation layers have been identified as a suitable solution for these conditions. Due to their unique engineering properties, foam glass aggregates (FGAs) are a promising material for use as an insulating granular layer in pavement design. However, understanding their mechanical performance is critical for predicting long-term layer and pavement behavior. In this laboratory study, an empirical transfer function was developed using an environmental and heavy vehicle simulator and an experimental pavement built in an indoor test pit. The study aimed to determine the allowable number of load repetitions for an FGAs insulation layer and to develop an empirical transfer function that can be used as part of a mechanistic-empirical pavement design procedure. This article proposes a linear relationship between permanent deformation, the number of load cycles, and the equivalency factor between the effect of resilient strain, or vertical stress, and allowable damage. The proposed empirical transfer functions allow defining an allowable number of load repetitions for a characteristic resilient strain or vertical stress and an allowable damage. The allowable damage can be modulated with respect to road classification, and a damage value of 0% to FGAs layer can be considered as a safety factor. The findings of this study provide valuable insights into the use of FGAs as an insulating granular layer in pavement design in cold regions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".