Non-Linear and Non-Stationary Random Responses of Discretized Plate Structures by Stochastic Direct Integration With Correction Factor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it