Construction, instrumentation and field performance of geogrid-reinforced unpaved roads
Bibliographic record
Abstract
A research study involving full-scale unpaved road test sections was carried out to investigate the performance of unpaved roads reinforced with geogrids. Ten test sections were constructed with two different base course thicknesses. Three biaxial geogrids of different tensile stiffness and one geogrid with triangular aperture were evaluated. Geogrids were placed at the subgrade–base course interface. The sections were instrumented for measuring road response to traffic loading. Traffic loading was provided by a single-axle dump truck. Field measurements of rut depth and surface deformation were recorded at selected traffic intervals. An analysis of the measured performance data indicated that the geogrids effectively reduced surface rutting and improved the performance of unpaved thin base layers and the benefit became more pronounced when a stiffer geogrid was used. The results suggested reductions in the thickness of the base layer by using geogrids of up to 18%. The improvement by geogrids was shown to be less for increasing base layer thickness. In addition, the ability of the design procedure by Giroud and Han to predict rutting performance using the test section parameters as design inputs was assessed. The results indicated that the method underpredicted the required base thickness to support the applied traffic loads.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".