Precise Photogrammetric Reconstruction Using Model-Based Image Fitting for 3D Beam Deformation Monitoring
Bibliographic record
Abstract
Periodic structural health monitoring of infrastructure systems is important to avoid economic losses and human casualties. Traditionally, deformation monitoring has been done through surveying techniques. Recently, with the increased availability of inexpensive off-the-shelf cameras, photogrammetry has become a viable noncontact alternative for complete three-dimensional reconstruction of the object or surface of interest. This paper aims at combining two methodologies of photogrammetric reconstruction—image-matching-based reconstruction and model-based image fitting—to achieve submillimeter precision for the estimation of both vertical deflections and horizontal displacements. The proposed methodology was tested with data collected using a photogrammetric system at a structures laboratory where a concrete beam was subjected to different loading conditions by a hydraulic actuator. The experimental results showed that the photogrammetric system was capable of monitoring both static and dynamic deformations. The methodology used exhibited a high level of automation and the final results yielded a root-mean-square error (RMSE) of half a millimeter.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".