Distributed strain monitoring for bridges: temperature effects
Bibliographic record
Abstract
To better manage infrastructure assets as they reach the end of their service lives, quantitative data is required to better assess structural behavior and allow for more informed decision making. Distributed fiber optic strain sensors are one sensing technology that could provide comprehensive data for use in structural assessments as these systems potentially allow for strain to be measured with the same accuracy and gage lengths as conventional strain sensors. However, as with many sensor technologies, temperature can play an important role in terms of both the structure’s and sensor’s performance. To investigate this issue a fiber optic distributed strain sensor system was installed on a section of a two span reinforced concrete bridge on the TransCanada Highway. Strain data was acquired several times a day as well as over the course of several months to explore the effects of changing temperature on the data. The results show that the strain measurements are affected by the bridge behavior as a whole. The strain measurements due to temperature are compared to strain measurements that were taken during a load test on the bridge. The results show that even a small change in temperature can produce crack width and strain changes similar to those due to a fully loaded transport truck. Future directions for research in this area are outlined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".