Corrugated Steel Ellipse Culvert Response: Experimental Results Compared to Design Approaches
Bibliographic record
Abstract
Design methods for corrugated steel culverts in current design standards consider the circumferential force in culvert walls (i.e., hoop thrust) to be the dominant load-carrying mechanism in these structures. However, recent studies have shown that bending moment, rather than thrust, is often the more dominant response for corrugated steel culverts under shallow burial conditions and vehicle loading. In addition, 2D finite element analyses have historically been unable to effectively capture the effects of discrete surface loads, such as wheel loads, on the response of buried metal culverts. To investigate these issues, the bending moment and thrust responses from an experiment involving an elliptical corrugated steel culvert under shallow burial conditions and simulated vehicle loading are compared with the bending moment and thrust estimates from the Canadian bridge design code and CANDE-2019 (a commonly used public domain finite element software package) and the thrust estimates from the American AASHTO LRFD bridge design code (which does not consider moment directly). The comparisons show that the Canadian code and CANDE-2019 models with a fine mesh and the continuous load scaling elasticity-based method appear to be the most effective for the investigated culvert and loading, while there is a need to modify the American AASHTO LRFD code to consider moment more directly. In addition, the results suggest that, under these conditions, the current approaches for estimating the peak bending moment response are more effective compared with the approaches for estimating the peak thrust response.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".