Critical Issues, Condition Assessment and Monitoring of Movable Bridges: Image Processing for Open Gear Monitoring
Bibliographic record
Abstract
Movable bridges are one of the least studied bridge types. In this paper, examples from a movable bridge evaluation study are presented based on the research conducted on a movable bridge in Florida over the last several years. Movable bridges face operational and maintenance challenges mainly due to complex structural, mechanical and electrical systems which, at the same time, provide their versatility. Although there are a few studies focusing on movable bridges, none of these studies provide a complete list of the problems related to the condition of movable bridge populations in conjunction with possible monitoring applications specific to these bridges. This study summarizes these issues related to movable bridges considering both the structural and mechanical components. After presenting the design and implementation of a monitoring system to a representative bascule bridge, analysis of image data for evaluating the lubrication levels in an open gear is presented. The findings from this analysis are compared with the maintenance logs. It is shown that continuous monitoring may provide invaluable information about safe, reliable and cost-effective operation and maintenance of movable bridges.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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".