Analytical Vehicle–Bridge Interaction Simulation Using Estimated Modal Parameters from Ambient Vibration Tests
Bibliographic record
Abstract
Previous research shows that an accurate simulation of vehicle–bridge systems is not feasible using beam models because they cannot adequately represent the torsional and transverse behavior of a bridge. Three-dimensional dynamic vehicle–bridge analytical solutions and FE models can also be cumbersome to develop and are prone to errors from idealization and modeling assumptions. To address these limitations, this paper presents a novel analytical vehicle–bridge simulation method that utilizes the experimentally estimated modal parameters of a bridge structure. The estimated modes from ambient vibration tests inherently enable the simulation to be valid for any generalized structural system and boundary condition because they truly represent the actual structure. In this paper, the mathematical derivation of the analytical model using plate vibration is presented in detail, the framework for the application of the model is outlined, and the proposed model is validated using a full-scale case study arterial highway bridge in the Canadian Province of New Brunswick. The proposed model offers a valuable solution applicable to real-time structural health monitoring and diagnostics, bridge weight in motion, and drive-by vehicle monitoring fields.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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".