Hybrid computational approach for nonlinear bending of bio-inspired helicoid composite plates using MITC3i and ANN
Bibliographic record
Abstract
This paper explores the nonlinear static response of bio-inspired helicoidal laminated composite (BiHLC) plates supported by a Pasternak medium. A combined analytical framework is established by integrating the mixed interpolation of tensorial components (MITC3i) approach with the first order shear deformation plate theory (FSDT). The Pasternak foundation is characterized by its spring stiffness k1 and shear stiffness k2. Based on the Lagrangian energy principle and von Kármán nonlinear theory, the governing equations are formulated and numerically solved through the Newton–Raphson iterative procedure. The effectiveness of the novel method is verified through comparisons with published documents. Additionally, the effects of helicoidal stacking sequences, geometric configurations, boundary constraints, and foundation rigidity on the large deflection behavior are analyzed. An artificial neural network (ANN) model is also proposed to estimate displacements efficiently, eliminating the dependence on finite element computations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".