Evaluation of impact factors of straight and horizontally curved composite concrete deck-steel cellular bridges.
Bibliographic record
Abstract
A theoretical investigation of the dynamic impact factors for straight and curved composite cellular bridges is performed in this thesis. The bridges are modelled as three-dimensional solid structures using commercially available software "ABAQUS" to simulate the bridge geometry and vehicle loading. The vehicle loads are modelled as a pair of two concentrated forces moving along in circumferential paths. Extensive parametric study is conducted, in which 120 composite multi-cell bridge prototypes are analyzed to: (1) evaluate their first natural frequencies; (2) evaluate their impact factors for moment, reaction, and deflection under truck loading conditions. The key parameters considered in this study are: number and area of cross-bracing and top-chord systems, number of cells, number of lanes, degree of curvature, span length, and loading conditions. Based on the data generated from the parametric study, expressions for dynamic impact factors for moment, reaction, and deflection are proposed.* (Abstract shortened by UMI.) *This dissertation includes a CD that is compound (contains both a paper copy and a CD as part of the dissertation). The CD requires the following application: Microsoft Office. Source: Masters Abstracts International, Volume: 41-01, page: 0279. Adviser: John B. Kennedy. Thesis (M.A.Sc.)--University of Windsor (Canada), 2001.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".