Failure Surface Frontier for Reliability Assessment on Expensive Performance Function
Bibliographic record
Abstract
This work proposes a novel concept of failure surface frontier (FSF), which is a hyper-surface consisting of the set of non-dominated failure points on the limit states of a failure region. Assumptions, definitions, and benefits of FSF are described first in detail. It is believed that FSF better represents the limit states for reliability assessment (RA) than conventional linear or quadratic approximations on the most probable point. Then, a discriminative sampling based algorithm is proposed to identify FSF, based on which the reliability can be directly assessed for expensive performance functions. Though an approximation model is employed to approximate the limit states, it is only used as a guide for sampling and a supplementary tool for RA. Test results on well-known problems show that FSF-based RA on expensive performance functions achieves high accuracy and efficiency, when compared with the state-of-the-art results archived in literature. Moreover, the concept of FSF and proposed RA algorithm are proved to be applicable to problems of multiple failure regions, multiple most probable points, or failure regions of extremely small probability.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".