Classification and Seismic Safety Evaluation of Existing Reinforced Concrete Columns
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.
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.
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 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 | 0.001 | 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