Advanced Weather Monitoring for a Cable Stayed Bridge
Bibliographic record
Abstract
In the northern United States, Canada, and many northern European countries, snow and ice pose serious hazards to motorists.Potential traffic disruptions caused by ice and snow are challenges faced by transportation agencies.Successful winter maintenance involves the selection and application of the most optimum strategy, over optimum time intervals.The risk associated with operating the bridges during winter emergencies varies depending on the size of the structure, the material of the stays, volume of average daily traffic, geographical location, nature of terrain and surroundings etc.The 'Dashboard', a monitoring system designed to help the bridge maintenance and operation personnel was developed at University of Cincinnati Infrastructure Institute.This was implemented at the Veterans Glass City Skyway Bridge in Toledo, Ohio.This system was also extended to the Port Mann Bridge in Vancouver, Canada.The aim of this research is to come up with an advanced monitoring system which will help the bridge management team make control actions during winter emergencies on the VGCS and Port Mann bridges.The current monitoring system gives information on the status of ice accumulation/ snow accretion or shedding based on last one hour's weather data.This dissertation focuses on adding intelligence to the existing system through addition of sensors, identifying patterns in events, adding cost-benefit analysis and incorporating forecast parameters, while also extending the system to other bridges and structures.In essence a new, more intelligent monitor designed to make the control decisions easier and have all necessary information to make such decisions in one place will be invaluable to the officials in the transportation departments.iii
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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 | 0.000 | 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".