What We Can and Cannot Learn from a Single Shear Test of a Very Large RC Beam
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Bibliographic record
Abstract
In the existing database on shear load capacity, tests of very large beams are scarce. Valuable additions to the database have recently been made in 2021 at the University of California, Berkeley (UCB), and in 2015 at the University of Toronto. These two tests were the largest ever among the standard three-point-bend type tests conducted so far. They verified the effects of beam size and of steel stirrups on the ultimate load, Vu, provided that the same concrete and steel are used. The present analysis, which deals in detail only with the UCB test, shows that the subsequent public blind competitions to predict the Vu measured in both tests were meritless and potentially misleading. The reason is that, similar to design codes, the only information provided to the competitors (besides the E modulus) was the required concrete compression strength, fc′, whereas the mean compressive and tensile strengths, fracture energy, initial creep data, and so on, were not provided. The fault of a competition of this kind is evidenced by (1) finite-element fracture simulations, (2) analysis of the huge statistical scatter of a database of 784 tests and a previous database in which fc′ was also the only concrete property used, like in the design code, and (3) estimation of the statistical error due to anchoring code provisions to the classical shear strength approximation 2fc′ (psi), which was set at about 65% below the mean of the data cloud in the database. The winning prediction of the UCB competition had an error of only 2.7% of the measured failure load, even though the probability of success is here shown to have been between 0.14% and 8.46%, with 0.90% being the best estimate. Hence, competitions of this type are, in essence, a lottery. Furthermore, the fact that the winning predictions in both competitions happened to be obtained by cross-section strain analysis based on beam mechanics, and no fracture mechanics, is potentially misleading. This, of course, does not detract from the value of the UCB and Toronto experiments as important and unique additions to the database and as verifications of the load capacity for the particular concrete used.
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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 it