Health monitoring of fluid dampers for vibration control of structures: experimental investigation
Bibliographic record
Abstract
SUMMARY This paper presents results from a comprehensive experimental program on medium‐size and large‐size fluid dampers in an effort to extract their force output during cyclic loading by simply measuring the strain on the damper housing and the end‐spacer of the damper. The paper first discusses the stress path within the damper and, subsequently via the use of linear elasticity, shows that the experimental data obtained with commercially available strain gauges yield a force output of the damper that is in good agreement with the readings from the load cell. This comparison is achieved via the use of a position and velocity transducer, which combines good accuracy together with robust performance in a marine environment. The paper then examines the performance of a portable data acquisition system that can be used to collect and transmit data from a damper installed on a bridge to a nearby location (order of a km) where data are collected via either a wired or a wireless Local Area Network (LAN). Alternatively, the data may be transmitted to any remote location via mobile telecommunication networks; however, this requires leased telephone lines. The data show that the proposed arrangement is promising for monitoring in situ the force output of fluid dampers and detecting possible loss of their energy dissipation capability. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".