Investigation of the mechanical response of recovered geogrids under repeated loading
Bibliographic record
Abstract
This study investigates the mechanical response and performance of biaxial polypropylene geogrid specimens’ cyclic loading. This work assesses the influence of embedment depths and subgrade strengths on the geogrids. The experimental program involved subjecting the geogrid specimens to 100 repeated tensile loading cycles at four distinct load targets: 20%, 40%, 60%, and 80% of the geogrid ultimate tensile strength. The analysis focused on evaluating the effects of preloading factors such as California Bearing Ratio (CBR) values, embedment depth, and the response to cyclic testing. Results show trends in stiffness reduction and changes in damping ratio with increased number of cycles. A comparative analysis was conducted with a control specimen from the same batch, highlighting the difference in mechanical response attributed to precycling variables. The findings indicate that the overall mechanical behavior of recovered geogrids is comparably consistent with new geogrids. However, variations in strain and stiffness reduction were observed among the recovered specimens, suggesting a pattern of yielding before failure. The findings suggest a minimal effect of embedment depth on the damping ratio at lower CBR. Overall, it was found that precycling and subgrade conditions have minimal effect on the mechanical response of the recovered specimens when tested in isolation.
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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".