ESTIMATION OF VERTICAL DEFLECTIONS IN CONCRETE BEAMS THROUGH DIGITAL CLOSE RANGE PHOTOGRAMMETRY
Bibliographic record
Abstract
Abstract. Deformation monitoring, and in general structural health monitoring, of civil infrastructure systems is important in terms of both safety and serviceability. Traditionally, large structures have been monitored using surveying techniques, while fine-scale monitoring of structural components has been done with geotechnical instrumentation. This paper reviews the advantages and disadvantages of using remote sensing methods, such as terrestrial laser scanning and digital close range photogrammetry, for the purposes of precise 3D reconstruction and the estimation of deflections in structural materials. It is also shown how a low-cost setup of multiple digital cameras and projectors can be used for the monitoring of concrete beams subjected to different loading conditions by a hydraulic actuator. The photogrammetric system used does not require any physical targets other than for the purposes of establishing the relative orientation between the involved cameras. The setup was tested in two experiments, and the beam deflections resulted from the photogrammetric system were compared to the ones from a set of one-dimensional laser transducers and a terrestrial laser scanner. The experiments proved that it was possible to detect sub-millimetre level deformations given the used equipment and the geometry of the setup.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".