Predicted and measured loads using the coherent gravity method
Bibliographic record
Abstract
The paper investigates the accuracy of the coherent gravity method by using measurements reported in a large database of full-scale instrumented walls that was not available at the time the original method was developed. The new database includes data for bar mat, welded wire and steel strip soil reinforced walls. Measured reinforcement loads under operational conditions are compared with predicted values for bar mat and steel strip reinforced walls. The accuracy of the coherent gravity method as presented in the BS 8006 design standard is quantified by computing the mean and coefficient of variation of the ratio (bias) of measured to predicted loads. The paper shows that for steel strip walls the coherent gravity method is reasonably accurate for soils with friction angles less than 45°. For granular soils with higher friction angles and bar mat walls, the current coherent gravity method is shown to be less accurate and, on average, non-conservative for design. Modifications to the method as currently described in BS 8006 are proposed to improve the accuracy of the method for bar mat reinforced soil walls and steel strip reinforced soil walls with high friction angle backfill soils.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".