Examination of Strength Modeling Reliability of Physical Tests on Structural Concrete Columns
Bibliographic record
Abstract
Comparisons and analyses of strengths from selected computational procedures were undertaken of more than 500 physical tests of rectangular, tied, structural concrete columns reported in the published literature. The computational procedures compared with the physical tests and with each other include a commercially available nonlinear finite element modeling software and the Canadian Standards Association (CSA) Standard A23.3-04. The requirements of the American Concrete Institute (ACI) 318-08 are very similar to those of the CSA A23.3-04, and hence, strength comparisons and analyses reported here are also applicable to ACI 318-08. The physical tests used for comparison were conducted on columns that were braced and pinned at both ends and were constructed using normal-density concrete with a compressive strength between approximately 17 and 57 MPa. The columns were subjected to short-term loads producing pure axial force, axial force combined with symmetrical single-curvature bending, or pure bending. Major variables included the concrete strength, the end eccentricity ratio, the slenderness ratio, the longitudinal reinforcing steel index for reinforced concrete or the structural steel index for composite columns, and the transverse reinforcement (tie/hoop) volumetric ratio. The study provides an examination of the reliability of the computational methods examined.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".