Damping in masonry arch railway bridges under service loads: An experimental and numerical investigation
Bibliographic record
Abstract
This article investigates the damping behavior of masonry arch bridges under service loads extracted from experimental data and provides guidelines on how to emulate this behavior in numerical analysis, particularly in discrete element model applications. First, an experimental campaign is undertaken and vibrations on three masonry arch railway bridges under train loads were monitored. The modal damping ratios from several sensors on each bridge were extracted by isolating the modal component of free decay vibrations which commence immediately after the train leaves the bridge. The modal damping ratios identified under service loads were compared with their counterparts identified under ambient vibrations. The suitability of mass-proportional, stiffness-proportional and Rayleigh damping models in emulating damping in masonry arch bridges was evaluated. In the numerical phase of the study, a single-arch masonry bridge was modeled using mixed discrete continuum approach and a moving load analysis was conducted without applying any additional viscous damping. The results of the numerical analysis indicate that the inherent damping in discrete element models provided by their nonlinear nature can be sufficient to emulate the damping behavior of masonry arch bridges under service loads. The research provided in this article is unique in the sense that it combines an experimental study and a numerical study on damping of masonry arch bridges under service loads. Unlike its counterparts in literature, which use either ambient vibrations or seismic action, damping values are computed under appropriate levels of vibration amplitudes for service loads, which is critical to estimate the modal damping ratios accurately under these loads.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".