Class-A prediction of three-sided reinforced concrete culverts and numerical investigation of the supporting strip footing geometry effect
Bibliographic record
Abstract
This paper presents three-dimensional finite element analyses conducted to optimise the instrumentation plan for monitoring the structural performance of reinforced concrete three-sided culverts (TSCs). Numerical models were developed for three TSCs with spans of 7.3, 10.4, and 13.5 m to establish the anticipated range of measurements. The TSCs were instrumented with 30 pressure cells and 56 strain gauges. Comparing the numerical predictions with field measurements of the 10.4-m-span TSC verified the adequacy of simulating the concrete behaviour as linear elastic for estimating the applied earth pressures. However, such simplification would lead to underestimating the induced strains in the culvert body in case cracks develop. In addition, Class-A predictions of soil pressures on the culvert were compared with the field measurements of a 7.3-m-span TSC. The calculated soil pressures agreed well with the field measurements. The measured and calculated earth pressures suggest a vertical arching factor of 1.05 at the final backfill height. The influence of the shallow foundation geometry was investigated employing the validated numerical model. It was found that the footing geometry has no influence on the applied earth pressures. However, the calculated stresses below the footing edge on the backfill side decreased as the footing flexibility increased.
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 | 0.000 | 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 | 0.000 | 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".