Fragility analysis of existing RCC frame structure
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 In recent years, number of studies have been carried out for the evaluation of vulnerability of structure during seismic events. Fragility analysis is one of the important probabilistic approach to estimate the damage data at different damage state during seismic events. The G+7 Reinforced Concrete frame structure is considered for analysis. The structure is analyzed in ETABS by Non-linear Pushover Analysis. Fragility curve developed for Non-linear Pushover Analysis from results obtained by capacity spectrum method for different damage states. The fragility curves are derived from analytical method. The fragility points for different damage states are derived from analytical formula. Fragility curve is plotted for probability in Y-axis and spectral displacement in X-axis for 4 different damage states from Non-linear Pushover Analysis. From Incremental Dynamic Analysis fragility curve plotted for probability in Y-axis and peak ground acceleration in X-axis for five different damage state. The behavior of fragility curve after achieving the 100% probability for different damage state is constant.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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