Non-Linear and Non-Stationary Random Responses of Discretized Plate Structures by Stochastic Direct Integration With Correction Factor
Bibliographic record
Abstract
Abstract The investigation reported in this paper is to further improve the effectiveness of some stochastic direct integration schemes. These stochastic direct integration schemes were proposed to compute response statistics, such as mean squares and variances of generalized displacements, of large discretized structures undergoing large non-linear deformation and under non-stationary random excitation. First of all, the stochastic Newmark method is extended to include the stochastic as well as the deterministic excitations. Next, a correction factor that is to be applied to the discrete white noise is introduced. The stability criterion is then examined. The advantage of introducing such a correction factor is that one is not limited to those time step sizes that have been found to yield accurate response statistics in previous investigations. Instead, one can choose a time step size in the way he may in an analysis using the deterministic Newmark method. The correction factor is determined based on this chosen time step size, thus providing the flexibility in balancing the needs of accuracy and effectiveness. Subsequently, the hybrid strain based three-noded flat triangular shell element, single- or multi-layered, is employed to model selected plate structures. These numerical examples demonstrate the accuracy and effectiveness of the proposed methodology.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".