Nondestructive Assessment of Elastomeric Bridge Bearings Using 3D Digital Image Correlation
Bibliographic record
Abstract
Elastomeric bridge bearings are installed between the bridge superstructure and substructure to accommodate translational and rotational deformations. The manufacturing quality of elastomeric bridge bearings is of high significance because manufacturing defects (such as variations in rubber layer thickness and nonparallel steel laminates) may jeopardize their short- and long-term structural behavior and integrity. Current quality control procedures involve destructive testing of samples of bearings from a lot. This type of testing is time consuming and costly, and thus limited to a relatively small sample size, which may undermine confidence in the quality of remaining bearings in the lot. This paper presents an alternative, vision-based assessment methodology for the nondestructive identification of the internal structure of elastomeric bridge bearings. The methodology capitalizes on the high deformability of rubber and the near inextensibility of the steel laminates, which together result in a unique deformation pattern on the vertical surfaces of a bearing when it is subjected to axial load. This deformation pattern features local extrema in in-plane strain and horizontal displacement fields on the vertical surfaces. These local extrema are analyzed to deduce the thicknesses of the rubber layers and rubber side covers. The methodology is developed based on three-dimensional finite-element analyses (3D-FEA). Then, three-dimensional digital image correlation (3D-DIC) is used in experimental tests to evaluate its capability to identify manufacturing defects in elastomeric bridge bearings. Finally, the methodology is validated against destructive tests. The nondestructive method presented in this study is conducted in conjunction with compressive tests that departments of transportation carry out routinely and is therefore expected to facilitate rapid and cost-effective qualification of elastomeric bridge bearings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".