Influence of fines on frost heave characteristics of a well-graded base-course material
Bibliographic record
Abstract
The influence of fines on the frost susceptibility of base-course crushed aggregates was established by laboratory freezing tests simulating closely the thermal conditions in the field. The frost susceptibility of the fines was varied by use of different mixtures of granitic fines and commercially available kaolinite clay. A total of 13 samples with fines content of 5%, 10%, and 15% and kaolinite fractions of 10%, 50%, 75%, and 100% were subjected to four freezethaw cycles. The frost susceptibility of well-graded crushed aggregates increases with increasing fines content and increasing kaolinite fraction. From a quantitative point of view, for a given kaolinite fraction, the segregation potential increases linearly with fines content, until the fines create a matrix in which the coarser particles are embedded. For the material studied, this occurs when the fines content is higher than 15%. For a given fines content, it was also established that the segregation potential increases linearly with kaolinite fraction, indicating the importance of mineralogy. It was also established that appropriate thermal testing conditions need to be adopted to prevent undue pore water extraction from the unfrozen soil close to the frost front during laboratory freezing of unsaturated coarse-grained soils.Key words: coarse grained, soil, frost susceptibility, pavements, laboratory, fines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.008 | 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 teacher head, 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".