Dynamic analysis of beams vibrating on nonlinear poroelastic multi‐layered continuum
Bibliographic record
Abstract
Abstract The paper presents a framework for analysis of beams interacting with nonlinear‐poroelastic, layered continuums (e.g., clayey soils) when subjected to time‐dependent loads. The poroelastic layered continuum is characterized by a nonlinear‐elastic constitutive relationship that relates the secant shear modulus to the induced shear strain. The Biot's theory of consolidation is combined with a dynamic beam‐continuum interaction model to develop the analysis. The vertical consolidation settlement of the beam and the excess pore pressure in the porous continuum are assumed to be products of separable functions, and the extended Hamilton's principle of least action is applied to obtain the differential equations governing the inertial consolidation motion of the beam‐continuum system and the dissipation of excess pore pressure. An iterative numerical algorithm is used to solve these coupled differential equations following one‐dimensional finite element analysis in which the implicit Wilson‐Θ time integration scheme is used to obtain the time history of beam and continuum responses. The novelty of the framework is that it rigorously takes into account the nonlinear poroelastic soil‐structure interaction within a dynamic time‐integration framework with minimal computational resources. The characteristics of this newly developed model are illustrated through examples.
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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) | 0.000 | 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 | 0.001 |
| 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".