Evaluation of truss bridges using distributed strain measurements
Bibliographic record
Abstract
In 2013, one in every nine steel bridges in North America was deemed to be in need of repair, retrofit or replacement. There are an estimated 200,000 steel bridges in the US alone, and an estimated $76 billion is required to return all bridges back to adequate service levels. As a result, monitoring is crucial to ensure the longevity of these structures and to avoid unnecessary replacement. To evaluate the use of distributed fiber optic strain sensors to monitor truss bridges, laboratory tests were undertaken. A scale model of the Mile 17.7 CN Rail Bridge in Jordan, Ontario, Canada was constructed and instrumented with fiber optic strain sensors prior to testing. The experimental program consisted of three point loading under three different connection conditions: i) all bolts were present and torqued, ii) one of two bolts in the connection was removed, and iii) the remaining bolts were loosened. The distributed strain measurements were used to calculate both the axial and bending strains along the full length of each member, along with the variation in truss behavior as a result of the change in connection conditions. The results indicate that the connection conditions at the truss joints can alter the behavior of the bridge.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".