An optimal procedure for fragility analysis of nuclear containment structures under internal pressure
Bibliographic record
Abstract
This study aims to develop a novel efficient procedure for determining the fragility function of nuclear containment structures under internal pressure. This procedure can substantially reduce the computational cost by optimizing the required number of nonlinear structural analyses, which is initially unclear in the pressure fragility analysis. The core principle of the proposed procedure is to monitor the changes of the mean ( P m ) and standard deviation ( β S ) of ultimate pressure at failure while gradually increasing the number of structural analyses. The process is terminated when the changes in P m and β S between two consecutive steps drop below a prescribed threshold. The proposed procedure was applied to the pressure fragility analysis of an existing type of containment structure. Since the proposed procedure involves the random selection of additional value sets of the uncertainty parameters at each step, it was repeated 10 times to ensure a fair evaluation. The fragility curve obtained from as small as 40 analyses was nearly identical to that from 100 analyses. On average, over the 10 repeated cases, the computation time was reduced by approximately 47 % compared to the case of 100 analyses. The results confirm that the proposed procedure not only significantly reduced the computational demand but also ensured the reliable generation of the pressure fragility function.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".