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Record W4381051896 · doi:10.1061/jsendh.steng-12242

What We Can and Cannot Learn from a Single Shear Test of a Very Large RC Beam

2023· article· en· W4381051896 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Structural Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicConcrete Properties and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsStructural engineeringCompressive strengthShear (geology)Ultimate tensile strengthBeam (structure)Test dataComputer scienceFinite element methodFlexural strengthDatabaseEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.196
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it