Field Test of a Shear Force Measurement Technique Using Fiber Optic Sensing under Variable Speed Truck Loading
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Bibliographic record
Abstract
The measurement of reaction forces at bridge bearings would enable engineers tasked with maintaining bridges to detect potential damage to the bearing and bridge by detecting changes in the load distribution at the supports with time. Currently, measuring the load in the bearing requires sensors built into the bearing, which means that they are hard to repair when damaged and cannot be installed after the bridge is built (unless the bearings are replaced). A potential alternative is the use of distributed fiber optic sensors (DFOS) that could be used to measure curvature in the beams of a bridge, which can then be used to calculate the moment, shear, and ultimately reaction force due to live loading. To investigate this, a DFOS system was installed on a newly built steel girder bridge on a single beam near one of the piers. A series of load tests were undertaken using a truck with a known load and driving along the bridge directly over the instrumented beam at speeds ranging from pseudo-static up to 30 km/h. The maximum measured strain in the bridge beam was 15 microstrain, which was lower than can be measured with certain DFOS systems, and highlighted the need to select a system with appropriate accuracy and precision. The measured strains were used to calculate the beam shear at the pier as the truck moved across the bridge. These results were compared with a continuous beam and two grillage analyses, and it was found that, based on the continuous beam model, about 25% of the total truck load was being carried by the beam, which was lower than the code live load distribution factor suggested. The grillage models provided better estimates of load spreading but were still conservative and dependent on the choice of transverse stiffness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it