Free-vibration of Composite Beam-columns with Stochastic Material and Geometric Properties Subjected to Random Axial Loads
Bibliographic record
Abstract
Beam-columns made of polymer-matrix fiber-reinforced composite materials are increasingly being used in automotive, aerospace, structural, and mechanical engineering industries. The composite materials display significant variability in their material properties. The composite laminates also display significant variability in their geometric and structural properties. Randomness exists in the axial loads and in the support conditions. As a result, the natural frequencies of composite beam-columns become random variables. The present work considers such composite beam-columns with the objective of determining the mean values and variances of natural frequencies. The randomness in the material and geometric properties of the laminated beam-columns are modeled using stationary stochastic fields in space. Each natural frequency is expressed as a perturbation series. The corresponding normal mode is also expressed as a compatible perturbation series. The perturbation method is employed in the context of stochastic analysis. The equations for sample realizations of natural frequencies and normal modes are derived. Using the sample realizations and the first-order second-moment probabilistic analysis the statistics of natural frequencies are determined. A parametric study on beam-columns made of NCT-301 graphite-epoxy composite material is conducted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".