The dynamic evaluation of composite materials footbridges
Bibliographic record
Abstract
The dynamic analyses of two examples of composite footbridges are presented in this paper. The investigation focussed on the comparison of dynamic responses to different types of dynamic load – specifically, pedestrian movement, traffic loads and rail loads. For dynamic analyses, a set of 3D models of the footbridges was prepared using the ABAQUS software program. The first step of the analysis was to determine the dynamic characteristics of the structure, i.e. its mode shapes and natural frequencies. Modal analyses revealed that the lowest natural frequency of one footbridge coincides with the frequency of pedestrian steps while walking or running; therefore, an evaluation of the dynamic response to these types of human actions was performed in order to identify the possible resonance phenomena. In the next stage, the authors assessed the dynamic response of the footbridges to typical traffic loads; these types of load are transmitted to the structure through the ground and foundations. Such an assessment appears to be necessary due to potential increases in the number of vibration sources arising from changes in the types and volume of traffic over time. It should be noted that traffic loads, which are a source of vibration for footbridges that are located over highways or railways, constitute an interesting yet still under-recognised problem concerning footbridges. For the analyses, representative time histories relating to the passage of a heavy goods vehicle and a train were used. The results of the analyses were compared with acceptability limits, with regard to levels of acceleration, in order to assess levels of vibration serviceability. The analyses revealed that the dynamic responses to both road traffic and rail loads are of a lower magnitude than the responses to the movements of human users.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".