Soil Reinforcement Loads in Geosynthetic Walls at Working Stress Conditions
Bibliographic record
Abstract
Knowing the load in geosynthetic reinforcement layers in full-scale walls is an important step to improving internal stability design methods. Interpretation of empirical reinforcement load data enables analytical models to be properly calibrated. High-quality empirical data also provides a baseline against which new design methods can be validated. In this paper, loads in soil reinforcement layers from 16 full-scale geosynthetic wall case histories were estimated from strain measurements and converted to load through the stiffness of the reinforcement material. The paper summarizes these estimated peak loads, describes general trends in the data, and compares these reinforcement loads to predictions using current design practice applied to the wall case histories. It was found that reinforcement loads derived from strain measurements are, in general, much lower than would be predicted based on current limit equilibrium design methods that use classical earth pressure theory. The low reinforcement strains and loads measured to date in geosynthetic walls point to the desirability of using peak soil shear strength rather than constant volume shear strength for design. This approach will help to reduce design conservatism and will be consistent with the philosophy of preventing failure of a major component of the reinforced soil system, the soil. Once the soil has failed, for all practical purposes, the wall has failed as well.
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 | 0.001 | 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) | 0.001 | 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".