Predicting Bridge Bearing Demands Through a Probabilistic Framework
Bibliographic record
Abstract
The life expectancy of bridge bearings is not well understood or predicted, and bearing replacement is primarily determined through field inspection and engineering judgement. An improved estimate of the timeline for bearing replacement would aid in maintenance scheduling and budgets. As a first step in determining the age for replacement, the annual and lifetime cyclic demands must be estimated. The bearing displacements are largely caused by temperature fluctuation, seismic events, and traffic cycles. In this dissertation a probabilistic framework is presented to quantify the annual cyclic displacement demands on a bearing from these diverse loadings using an archetype continuous concrete girder bridge. The likelihood of occurrence per annum of cycles of increasing amplitudes are presented. In addition, the mean and cycle periods which are related to the loading scenario and cycle amplitude are discussed, and a suggested loading protocol is presented. Three locations across Canada are considered, Quebec City, Toronto and Vancouver, to investigate the influence of location on the bearing demands. The annual expected demands can be used for future work on fatigue testing of bearings to better relate to replacement schedule projections for bridge bearings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".