Optimizing the effective particle diameter of crushed rock materials to mitigate the effect of convection in a pavement structure
Bibliographic record
Abstract
It is common practice in Norway to use crushed rock in road structures. Recent regulations allowed a large variation of the particle size distribution in this layer. This paper focuses on the influence of effective particle size of crushed rock materials on heat transfer in pavements based on pavement structure thermal modelling using a partial differential equation solver. The normal temperature for the 1980 – 2010 period in various regions of Norway, as well as the coldest year (extreme conditions) were used together with the factor N as surface conditions of a typical pavement structure. The crushed rock was modelled using various values of effective particle diameter (d 10 ), a material characteristic that significantly affects thermal properties of coarse materials. Along with temperature distributions, maximum frost depths were computed and expressed according to the climatic data of each site studied and as a function of the effective particle diameter. The results showed that d 10 had a significant effect on frost depth mainly during the coldest winters as a result of increased heat transfer owed to natural convection. The results obtained also made it possible to establish a critical d 10 value to minimize convection as a function of the mean annual air temperature, providing a simple tool to road designers for selecting the materials for subbase and frost protection layer (FPL).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".