Practical application of structural risk assessment with SMART DT
Bibliographic record
Abstract
Structural risk assessments are highly relevant to assure the integrity of the structure over the whole life-cycle of an aircraft. The Aircraft Structural Integrity Program (ASIP) introduces design guidance based on deterministic crack growth prediction for safety critical components. Today, with the increase of computational power, probabilistic methods instead of deterministic analysis can be deployed. Probabilistic Risk Assessment (PRA) offers the benefit of taking uncertainty into account and leads to less conservative estimates while meeting safety requirements. Facilities such as the National Research Council Canada (NRC) or the Defence Science and Technology Organization in Australia (DSTO) are already assessing safety critical elements with PRA. RUAG AG is interested in the application of this method for a military system. Based on the research performed by these facilities, the application of PRA is assessed. The probabilistic calculations are performed with SMART|DT. Structural fatigue data is needed for the assessment, which includes equivalent crack growth data, pre-crack size distributions (EPS), loading distributions and POD. The possibilities of the tool are assessed with four case studies with different data situations. Based on the case studies, existing methods are explored and implemented for the data acquisition. For better understanding PRAs and their application for the system, evaluations are performed including comparisons between spectra, different material data and the influence of different distributions on the probability of failure. With the applied methodologies, the case studies show very conservative results compared to fleet crack findings. The application of the tool must be assessed on a case-by-case basis depending on the available data. The application of PRA is challenging and it is difficult to establish the reliability of the data and their results with the lack of guidelines regarding PRA. The paper shows a possible application of PRAs using the available data with limited effort in the data gathering process and includes simple ways for the data preparation and application in SMART|DT based on known PRA methodologies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".