Modelling discrete soil reinforcement in numerical limit analysis
Bibliographic record
Abstract
Soil reinforcement is widely used in geotechnical engineering. While there are various means of accounting for the presence of soil reinforcement in limit analysis and limit equilibrium type calculations, these are often highly problem-specific. In this paper, a general means of incorporating soil reinforcement within numerical limit analysis calculations is presented. A key feature of this implementation is that the reinforcement is modelled “in parallel” with the soil model, which allows the soil to flow past the reinforcement as might occur in soil nailing. To illustrate this, the “discontinuity layout optimization” (DLO) numerical limit analysis procedure is used, and the efficacy of the approach is evaluated via application to reinforced slope problems involving rigid soil nails under plane strain conditions. The analyses are calibrated against a two-part wedge analysis method, as presented in British Standard BS 8006:1995 or AASHTO’s LRFD bridge design specifications. It is shown that the DLO-based procedure produces identical results only when the two-part wedge collapse mechanism is prescribed in advance (achieved by artificially strengthening the soil except along pre-defined failure planes). A more critical mechanism is otherwise predicted, with the soil strength at collapse required to be approximately 10% higher than predicted by the two-part wedge method (or alternatively, soil nail lengths required to be approximately 20% greater).
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".