Bibliographic record
Abstract
As a natural occurrence, rutting develops during the using stage of flexible, pavement When the rutting becomes worse, users will experience an uncomfortable feeling. Moreover, this may eventually influence the life span of the pavement. It is of researcher's interest to find out an effective way to mitigate this phenomenon. The application of geotextile/geogrid is a good method. The objective of this research is to study the function of geotextile/geogrid, during gradual stiffening stage. In doing so, this research was processed in two steps. The first step was triaxial tests on the soil sample (base material) reinforced by different layers of geotextiles under different confining pressure. One-dimensional analysis was performed upon the test results. The second step was numerical analysis on the published data dealing with the permanent deformation. Finite Element Analysis and Sensitivity Analysis were exercised. The FEA was undertaken to identify the permanent resilient modulus (PRM) by assuming that the inclusion of geogrid influenced the PRM of all the layers, while the sensitivity analysis was done by assuming that the inclusion of geogrid merely affected the properties of base material. From the experiments, it was observed that the effect of adding more layers of geotextiles was more pronounced than the increase of confining pressure. From the numerical analysis, the conclusion can be drawn that the variation of permanent deformation was very sensitive to the variation of thickness coupled with permanent resilient modulus (PRM) of base layer. Source: Masters Abstracts International, Volume: 39-02, page: 0557. Adviser: B. B. Budkowska. Thesis (M.A.Sc.)--University of Windsor (Canada), 2000.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".