Deformation monitoring with off-the-shelf digital cameras for civil engineering fatigue testing
Bibliographic record
Abstract
Abstract. Deformation monitoring of civil infrastructure systems is important in terms of both their safety and serviceability. The former refers to estimating the maximum loading capacity during the design stages of a building project, and the latter means performing regularly scheduled maintenance of an already existing structure. Traditionally, large structures have been monitored using surveying techniques, while fine-scale monitoring of structural components such as beams and trusses has been done with strain gauge instrumentation. In the past decade, digital photogrammetric systems coupled with image processing techniques have also been used for deformation monitoring. The major advantage of this remote sensing method for performing deformation monitoring is that there is no need to access the object of interest while testing is in progress. The paper is a result of an experiment where concrete beams with polymer support sheets are subjected to dynamic loading conditions by a hydraulic actuator in a structures laboratory. This type of loading is also known as fatigue testing, and is used to simulate the typical use of concrete beams over a long period of time. From a photogrammetric point of view, the challenge for this type of experiment is to avoid motion artifacts by maximizing the sensor frame rate, and at the same time to have a good enough image quality in order to achieve satisfactory reconstruction precision. This research effort will investigate the optimal camera settings (e.g., aperture, shutter speed, sensor sensitivity, and file size resolution) in order to have a balance between high sensor frame rate and good image quality. The results will be first evaluated in terms of their repeatability, and then also in terms of their accuracy. The accuracy of the results will be checked against another set of results coming from high quality laser transducers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".