Ultimate Limit State of Deep-Corrugated Large-Span Box Culvert
Bibliographic record
Abstract
Limit state design requires independent assessment of both load and resistance. Although much is known about the live and dead loads that may act on box culverts, there is no known measurement of the resistance (or capacity) at the ultimate limit state. The objective of this study is to present results from a full-scale experiment conducted on a buried, deep-corrugated, large-span box culvert under controlled laboratory conditions—the first conducted to its ultimate limit state. The box culvert had a 2.4-m rise and 10-m span (7.9 × 32.8 ft) and was fabricated from steel plate 6 mm thick with corrugations 150 mm deep at a 400-mm pitch (0.24 × 6 × 16 in.). The box culvert was backfilled to a minimum cover depth of 0.45 m (1.5 ft) with densely compacted well-graded sand and gravel. Tandem-axle loading was then applied by an actuator until an ultimate limit state was attained. The ultimate limit state of the box culvert involved the formation of plastic hinges at the crown and shoulders at a total applied force of 1,100 kN (250 kips). The force required to reach the ultimate limit state was 1.8 times larger than the factored design tandem-axle load from the AASHTO bridge design specifications. Similarly, the factored resistance at the ultimate limit state was 1.7 times larger than the factored CL-625-ONT tandem-axle load from the Canadian highway bridge design code.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".