Numerical modelling of enhancement of the capacity of buried metal culverts by using geogrids
Bibliographic record
Abstract
This paper presents a two-dimensional numerical modelling analysis of a flexible buried corrugated metal arch culvert in Enkoping, Sweden. The numerical results of this study are validated against field measurements of the culvert crown deformation, thrust, and bending moments recorded during backfilling. To model the backfill soil, the hardening soil with small strains (HSs) material model is used because of its efficiency in simulating the soil-structure interaction. Furthermore, in a numerical investigation of the stress distribution at the culvert invert, it is found that weakness of the foundation soil has an insignificant impact, due to stress dissipation resulting from arching actions. The numerical modelling analysis also investigates the use of geogrid layers with dead end bolts in the soil cover above the culvert crown during the application of static surface loads, as an innovative technique to improve the load capacity of the soil-culvert system. The results show a reduction in culvert crown deformation and internal forces when geogrid layers are used. This indicates the efficiency of geogrid layers in improving the load capacity of existing buried culverts or overcoming deficiencies in culvert serviceability by reducing the impact of applied loads.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".