Effects of Temperature Variations on the In-Plane Stability of Steel Arch Bridges
Bibliographic record
Abstract
The in-plane stability of shallow parabolic arches subjected to a central concentrated load and temperature variations was investigated in this paper. The virtual work principle method was used to establish the non-linear equilibrium and buckling equations. Analytical solutions for the non-linear in-plane symmetric snap-through and antisymmetric bifurcation buckling loads were obtained. Then, the effects of temperature changes on the in-plane stability for arches with supports that stiffen under compression were studied. The results show that the influence of temperature variations on the critical loads for both buckling modes (symmetric snap-through and anti symmetric bifurcation) is significant. The critical loads for the two buckling modes are more than those only under external loads without thermal loading. Moreover, the critical loads increase with an increase of the thermal loadings. It can also be found that the effects of applying a temperature field increase when either initial stiffness coefficient α or the stiffening rate β is raised. Furthermore, the effect of thermal loading on the critical load increases with the span-rise ratio m for arches with any initial stiffness coefficient α and the stiffening rate β.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".