Performance and Accuracy of Fibre Optic Sensors and the Digital Image Correlation in Measuring the Strains and Crack Widths of Concrete Structures
Bibliographic record
Abstract
A significant proportion of North America’s aging infrastructure has surpassed its intended design life. This includes a large number of concrete structures that are located in Canada or northern parts of the US with prolonged freezing seasons and high temperature fluctuations. One potential solution to assess the condition and performance of a structure and ensure its resilience is to use structural health monitoring (SHM) techniques. Fibre optic strain sensors (FOS) and digital image correlation (DIC) are two SHM breakthrough techniques providing more comprehensive performance data than conventional techniques. Although these SHM techniques are reasonably well developed, there is still a gap between the monitoring data and serviceability and reliability indicators due to uncertainty of measurements caused by parameters varying with time, e.g. temperature. In this work, FOS and DIC were used to measure strains and crack widths for eight large-scale reinforced concrete beams tested under static and fatigue loading at 15°C and -25°C. In addition, to evaluate the accuracy and precision of these technologies with temperature variations, calibration tests were conducted to measure temperature related strain errors that are induced in these systems. The results showed that both FOS and DIC are affected by temperature changes, and their measurements need to be corrected for temperature when they are used for measuring strains. This study also showed that DIC technique is capable of measuring crack widths with a very high accuracy, and external fibres can measure the strains in the concrete in compression with a reasonable accuracy, and can give an indication of the strains in the tensile reinforcement prior to reaching the cracking load of the reinforced concrete.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".