Simulation‐Based Tracking Test and Optimization of Large‐Tonnage Box Girder Transport with Trolley on an Erected Bridge
Bibliographic record
Abstract
In the construction of large‐tonnage box girder, the construction load of box girder transport is generally greater than the operating load in highway industry; therefore, it is a crucial task to carry out accurate simulation and optimization on bearing box girder. For this purpose, the refined modeling method of 40 m/1270t box girder is studied first in this paper, followed by detailed stress analysis by considering the impact coefficient of vehicles and the most unfavorable conditions. Tracking tests on dead load, prestressed load, and the transport load have shown that the calculated stress values obtained by the refined models are very close to the measured stress values. Based on dynamic strain test of the vehicles at the speed of 4 km/h, the impact coefficient of four vehicles is estimated to be 1.08 and its value meets the requirements of no more than 1.1 provided by the vehicle manufacturer. Aimed at no tensile stress in the midspan section, the optimized geometry of 40 m box girder is obtained with less concrete and longitudinal prestressed tendons. These results demonstrate the plausibility and validity of the proposed research methods and optimization schemes for large‐tonnage box girder transport.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".