Behavior of Shear-Critical Concrete Deep Beams Monitored with Digital Image Correlation Equipment
Bibliographic record
Abstract
This paper discusses the behavior of large-scale reinforced concrete deep beams that failed in shear and were monitored with full field-of-view, digital image correlation (DIC) equipment.Six shear-critical deep beams, measuring 4.88 x 1.11 m, were tested to failure.The specimens were point-loaded and simply supported, with three members examining the influence of asymmetrical loading conditions.The members were tested with various loading plate sizes and shear span-depth ratios.High-resolution displacement and strain field data obtained throughout loading are used to examine the member response.Principal compressive strain field diagrams of the deep beams at peak load are discussed.The paper presents crack patterns and crack kinematics, including crack widths and crack slips along critical shear cracks, determined from the DIC data throughout loading.The paper discusses crack dilatancy-that is, crack slips versus crack widths-along critical shear cracks, throughout loading, and at multiple locations.The results improve understanding of the detailed displacement field response of large shear-critical reinforced concrete deep beams, which can be used to improve kinematic and constitutive models, such as aggregate interlock models, for large-scale members.
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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 | 0.000 |
| 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) | 0.004 | 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".