Classification and Seismic Safety Evaluation of Existing Reinforced Concrete Columns
Bibliographic record
Abstract
This study contributes to the critical need for safety assessment tools for existing reinforced concrete structures. Of particular concern is the possibility of collapse due to shear failure followed by axial failure of columns supporting gravity loads. This is a potential threat to a number of existing buildings in seismically active regions. Due to unavoidable uncertainties, drift capacity predictions can only be made in a probabilistic manner. This is addressed by the development of probabilistic drift capacity models at two performance levels: lateral strength degradation and axial load failure. First, a classification method is proposed to approximately distinguish between shear-dominated columns and flexure-dominated columns. Second, for each type of column, a probabilistic shear capacity model is developed by applying an existing Bayesian methodology to an experimental database. The focus of the presentation is on the physical insight gained from the model development. Third, a probabilistic model is developed for the drift capacity at axial load failure. Finally, the probabilistic drift capacity models are employed to develop fragility curves—with confidence bounds—that are utilized to assess the probability of failure implied by current seismic rehabilitation guidelines.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".