A Coupled Approach to Developing Damage Prognosis Solutions
Bibliographic record
Abstract
An approach to developing damage prognosis (DP) solution that is being developed at \nLos Alamos National Laboratory (LANL) is summarized in this paper. This approach integrates \naadvanced sensing technology, data interrogation procedures for state awareness, novel model \nvalidation and uncertainty quantification techniques, and reliability-based decision-making \nalgorithms in an effort to transition the concept of damage prognosis to actual practice. In parallel \nwith this development, experimental efforts are underway to deliver a proof-of-principle technology \ndemonstration. This demonstration will assess impact damage and predict the subsequent fatigue \ndamage accumulation in a composite plate. Although the project focus will be DP for composite \nmaterials, most of this technology can generalize to many other applications. The unique aspects of \nthis approach discussed herein include: 1) multi-length scale damage models analyzed on tera-scale \ncomputer platforms that discretize composites on an individual lamina level, 2) integration of \nadvanced sensors with Los Alamos’s flight-hardened data acquisition system, 3) damage detection \nbased on a statistical pattern recognition approach, and 4) reliability-based metamodels with \nquantified uncertainty that can be deployed on microprocessors integrated with the sensing system \nfor autonomous damage prognosis.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".