IMAGE-BASED DEFORMATION MONITORING OF STATICALLY AND DYNAMICALLY LOADED BEAMS
Bibliographic record
Abstract
Abstract. Structural health monitoring of civil infrastructure systems is an important procedure 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 instrumentation for civil engineering purposes. As a remote sensing technique, photogrammetry does not need any contact with the object being monitored, and this can be a great advantage when it comes to the deformation monitoring of inaccessible structures. The paper shows a low-cost setup of multiple off-the-shelf digital cameras and projectors used for three-dimensional photogrammetric reconstruction for the purpose of deformation monitoring of structural elements. This photogrammetric system setup was used in an experiment, where a concrete beam was being deformed by a hydraulic actuator. Both static and dynamic loading conditions were tested. The system did not require any physical targets other than to establish the relative orientation between the involved cameras. The experiment proved that it was possible to detect sub-millimetre level deflections given the used equipment and the geometry of the setup.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".