Estimation of subgrade soils mechanical properties and frost sensitivity through the use of simple tests
Bibliographic record
Abstract
Subgrade soils properties are one of the main inputs for pavement design. In cold climates, these subgrade properties are associated with stiffness, water sensitivity and frost susceptibility and can be obtained through reliable but complex and costly resilient modulus and segregation potential laboratory tests. Portable instruments such as light weight deflectometer (LWD) allow to rapidly and easily quantifying mechanical properties of soils to a limited extent. The mathematical models associated with these measurements are poorly adapted to take into account stress state and water content on the mechanical properties determination. Regarding the frost susceptibility of soils, a default value is often used or it is often estimated with charts or estimated from its physical properties. Therefore, the project focused on the development of simple tests using portable tools (LWD and percometer) to perform a reliable estimation of resilient modulus and segregation potential. Ten typical subgrade soils were sampled and a laboratory deflection based test (using a LWD and a 300 mm diameter mold), validated with field measurements, and was correlated with triaxial resilient modulus test results to take into account non Iinearity. Percometer measurements (dielectric value) were also performed on the laboratory samples for various water contents. The water sensitivity measured with the percometer data were correlated with laboratory segregation potential values of the tested soils. The developed resilient modulus and segregation potential estimation techniques allowed obtaining an adequate estimation of these important pavement design properties based on soils in situ characteristics.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".