Influence of reinforcement stiffness and compaction on the performance of four geosynthetic-reinforced soil walls
Bibliographic record
Abstract
The paper describes measurements taken from a series of four full-scale modular block walls that were constructed with reinforcement layers having different stiffness. The walls were 3.6 m high and were reinforced with two different polypropylene geogrid reinforcement materials, a polyester geogrid and a welded wire mesh. Each wall was constructed with the same modular block facing and reinforcement spacing of 0.6 m. The influence of compaction effort on wall displacements and horizontal toe load measurements at the end of construction was detectable in this investigation. These values were adjusted to account for the influence of different compaction methods on end-of-construction wall response. However, during subsequent surcharging the effects of initial compaction effort were erased. Reinforcement loads are computed from strain readings and results of in-isolation constant-load (creep) tests. Computed maximum reinforcement loads are compared with values predicted using the current AASHTO Simplified Method and the K-stiffness Method. The predicted magnitude and distribution of reinforcement loads are shown to be more accurate using the K-stiffness Method for polymeric reinforcement materials. For the relatively stiff welded wire mesh product, the measured reinforcement loads fell between values predicted using both methods.
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 | 0.000 | 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) | 0.001 | 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".