Numerical Investigation of the Structural Behavior of Corrugated Steel Culverts under Surface Load Tests Using Three-Dimensional Finite-Element Analyses
Bibliographic record
Abstract
Corrugated steel pipe (CSP) culverts have been widely used for decades; however, their structural behavior under surface loading may not be correctly captured by design codes based on results from recent experimental studies. To address this, the data from nine full-scale experiments investigating the structural behavior of corrugated steel culverts with different burial depths and surface loading configurations, instrumented with distributed fiber optic strain sensors, were compared with three-dimensional finite-element analyses. Parametric studies were undertaken that included different models for the corrugation properties (i.e., explicit modeling of the corrugated geometry and orthotropic, and isotropic shell), contact between soil and pipes, and the soil properties (soil moduli and elastic and elastoplastic behavior). It was found that the orthotropic model was a good substitute for explicit modeling of the corrugated geometry, which saves computation time and still provides an accurate estimation of the thrusts and moments. The moments in the culvert were found to be sensitive to the distribution of soil moduli, whereas thrusts were not. Based on this investigation, three-dimensional analyses using orthotropic shell models, elastic soil properties with moduli varying with depth, and tie constraints between the soil and the culvert are recommended for future investigations of corrugated steel pipe responses to surface 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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".