Lateral load resistance of reinforced concrete columns with brittle details
Bibliographic record
Abstract
Abstract Analytical models for shear strength and deformation capacity estimation of reinforced concrete members are essential for the practical implementation of performance‐based seismic evaluation of existing structures and form a key part of current seismic assessment codes (e.g., EN 1998‐3 (2005), (2022), ASCE/SEI 41‐17). Although these models have been derived from regression or calibration of large datasets collected from experimental literature, discrepancy persists between experimental and analytical estimates, especially in cases of members with old‐type detailing. The evident uncertainty about parametric dependencies of the underlying mechanistic problem motivated the present work, where a set of benchmark columns that represent older structural detailing are studied parametrically through advanced finite element simulation. Parameters considered were, the axial load ratio, transverse reinforcement amount and spacing, longitudinal reinforcement ratio, and anchorage/lap splice detailing; results were gauged in terms of the resulting drift ratio (deformation capacity) at the performance limit states and were compared with the Code estimates to evaluate their limits and conservatism. The simulation results were used to vet the assumptions that underlie the simple mechanistic models used in deriving seismic design expressions for yield and ultimate deformation capacities, shear strength, and the rate of its degradation with increasing ductility.
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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) | 0.001 | 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 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".