Distributed Fibre Optic Sensing to Assess Support Reactions and Behaviour of Composite Steel Bridges
Bibliographic record
Abstract
The method proposed in this investigation involves using distributed fibre optic sensing (DFOS) strain measurements to estimate support reactions in beams through an experimental investigation that involved beam, model bridge, and field load tests. Five steel beams were instrumented with fibre optic sensors and loaded in simply supported and continuous beam configurations. Disturbed regions in DFOS strain measurements were found to extend a distance equal to the depth of the section from supports and loading points. An estimate of flexural stiffness was used to calculate support reactions, which were found to be inconsistent with results obtained from load cells. The error between DFOS calculated support reactions and load cell support reactions was then minimized to determine calibrated stiffness values for each beam. The five slender steel beams were then assembled into a two-span bridge configuration and connected by a series of steel deck plates. Stiffness values determined from the individual beam tests were used to calculate support reactions for each beam at each support. The sum of the support reactions was compared with load cell measurements at the loading points and showed good overall agreement in tests involving consistent loading between beams. Non-linear strain profiles were observed in tests involving concentrated loading in the centre of the bridge, which decreased the accuracy of the DFOS calculated support reactions. DFOS strain derived neutral axis measurements were used to estimate the level of composite action in the bridge and adjust the flexural stiffness of each beam. Part of a girder in a steel beam with concrete deck bridge in Kingston, Ontario, was instrumented with fibre optic sensors and loaded by a truck driving across the bridge deck at a range of speeds. DFOS calculated shears were compared with the results of a numerical analysis to determine that the girder carried approximately 25% of the truck load when the truck was near the instrumented region, compared to 79% of the truck load calculated using live load distribution factors from a design code. Load sharing between girders was found to have a greater effect when the truck was positioned further from the pier.
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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 | 0.001 | 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) | 0.001 | 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".