Databank of Concentric Punching Shear Tests of Two-Way Concrete Slabs without Shear Reinforcement at Interior Supports
Bibliographic record
Abstract
Hundreds of laboratory experiments have been conducted to date to investigate the punching shear behavior of two-way reinforced concrete (RC) slabs at interior supports. These experiments provide a mandatory frame of reference for the development, calibration and evaluation of punching shear design provisions. Unfortunately, because of the lack of dissemination and unavailability of some of the references together with some level of arbitrariness by researchers and code developers in selecting reference data, code provisions have been developed based on a rather limited subset of the available test results. To overcome these limitations, a task group was formed within ACI Committee 445 to gather, compile and post-process the results from laboratory tests studying the concentric punching shear behavior of two-way RC slabs without shear reinforcement at interior supports. The development of the databank involved two stages: first, the creation of a "collected" databank, where the characteristics of test specimens and test results were compiled as faithfully as possible to what was reported by researchers. Secondly, the development of a "selected" databank based on a series of Data Acceptance Criteria (DAC), with the goal of endorsing a test result into an evaluation-level databank. This paper describes the creation process and main features of the collected databank and discusses several important aspects in databank development including the selected platform being used to disseminate the information to users.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.015 |
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".