Damage Identification in Large-scale Double-layer Truss Structures Via a Two-stage Approach
Bibliographic record
Abstract
In this study, a two-stage damage identification approach based on modal flexibility differences and whale optimization algorithm (WOA) was applied to localize and quantify damages in large-scale double-layer truss structures. In first stage, damage locating vector (DLV) method using EDS (exponential decreased stress) was employed to find the real damaged elements of structure; then, WOA algorithm was used to determine the severity of suspected damaged elements obtained from the first stage. To evaluate the reliability of the proposed approach, two large-scale double-layer truss structures were studied. Furthermore, to assess the effect of noise on the accuracy of damage detection, the article compares the results of EDS with NCE. Calculation results demonstrate that the combination of DLV method using EDS and WOA algorithm provides an effective tool to carefully determine the location and the severity of structural damages in noisy condition directly. Moreover, the approach determines damages even though there are the low number of used mode shapes and a high number of structural elements.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".