Evaluation of Moving Vehicle Dynamic Effects on Short- and Medium-Span Highway Bridges Using Weigh-in-Motion Data
Bibliographic record
Abstract
This paper highlights the limitations of current bridge design guidelines in accounting for dynamic loads caused by moving vehicles. The impact of bridge and vehicle parameters on the coupled vehicle–bridge system dynamic response was examined using a comprehensive set of 96,768 numerical simulations. The studied parameters include bridge geometry, span length, material type, pavement condition, vehicle gross weight, axle count, suspension system, and traveling speed. The ranges for vehicle attributes and bridge geometries were determined using traffic data from weigh-in-motion stations and structural types from the bridge inventory in New Brunswick, Canada. The scope of parametric studies related to the bridge structural parameters and vehicle suspension properties was established using published data. It is demonstrated that the bridge pavement condition significantly impacts the dynamic response; an increase in vehicle speed may either increase or decrease its dynamic impact; the bridge’s fundamental frequency cannot solely represent vehicle–bridge parameters; and the vehicle parameters, particularly the suspension system, have significant impacts on the dynamic amplification factor. Finally, recommendations were made to enhance existing practices for accurately accounting for the dynamic effects of vehicle–bridge interaction in short and medium-span highway bridges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".