Comparative study of integral abutment bridge structural analysis methods
Bibliographic record
Abstract
The primary goal is to accurately predict long-term integral abutment bridge (IAB) responses under thermal loads by applying available numerical modeling techniques developed on the basis of a long-term monitoring of in-service IABs. Considered methodologies are: (1) free expansion; (2) empirical approximate; (3) two-dimensional (2D) static analysis; (4) 2D time-history; (5) three-dimensional (3D) static analysis; and (6) 3D time-history. Specific IAB responses evaluated for the comparison are: girder axial force and moment, pile shear, moment, and displacement. The results indicate that the substructure responses predicted by all six analyses are reasonably comparable. However, the superstructure responses predicted by a 2D analysis are significantly different than predictions by a 3D analysis. Both 2D and 3D static analysis predictions tended to form boundaries for 2D and 3D time-history analysis. Therefore, this study concludes that a 3D time-history analysis is preferred for long-term, superstructure response predictions; all 2D and 3D static and time-history analyses are acceptable for substructure response predictions.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".