Bibliographic record
Abstract
Structural health 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, fine-scale monitoring of structural components such as beams and trusses has been done with geotechnical gauge instrumentation, which only measures deflections in one direction, and do not allow for a threedimensional deformation estimation. Moreover, monitoring the appearance of cracks specifically, for example in support columns, foundations or walls, has been done mostly manually with a permanent marker directly on the specimen. This paper will present the advantages of using vision techniques for the purposes of flagging the appearance and tracking the propagation of cracks in structural materials. Basically, using a remote sensing method for imagebased measurements allows for detecting cracks without having the need of accessing the tested elements, and also a permanent visual record is established for each observed epoch in time. The paper shows some of the data and preliminary results from an experiment where a concrete beam with a polymer support sheet was subjected to both static and dynamic loading conditions by a hydraulic actuator in a structures lab. The static loading was used to simulate the maximum loading capacity at a particular instance, while the dynamic loading (also known as fatigue testing) was used to simulate the typical use of the beams over longer periods of time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".