Effect of five versus two axle moving trucks on structural dynamic performance of frame bridges
Bibliographic record
Abstract
Abstract Recent advances in analysis, and design approaches led to a considerable reduction of the structural elements’ size and weight. Heavy multi-axle trucks are now standardized in North America, and elsewhere, the traffic speed and the average number of trucks passing bridges have dramatically increased. For the purpose of the design and/or assessment of bridge structures, it is imperative to evaluate the static deformations, frequency, vibration amplitudes, and dynamic deformation patterns of new and aged bridges due to the new five-axle versus the old two-axle design trucks. This study investigated the effects of using a recent standard multi-axle design truck on the dynamic performance of a frame bridge. It presents a comparison of the bridge dynamic performance under 2-axle and 5-axle moving trucks. A two-dimensional nonlinear finite element model is used to model the frame, and trucks are modelled as a multi-degree of freedom dynamic system integrated with the bridge model. It is found that the model is able to capture the local dynamic excitation and oscillations results from the high variation of the stiffness and mass of the bridge components.
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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 | 0.001 | 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".