Analysis of the ultimate uplift resistance and progressive failure process of strip plate anchors in marine sensitive clays: a FE analysis based on micropolar continuum theory
Bibliographic record
Abstract
This study presents a Tresca-inscribed Mises micropolar continuum model for undrained marine sensitive clays, which simultaneously accounts for strength heterogeneity and nonlinear strain softening. The model incorporates the Tresca criterion to ensure an accurate representation of soil strength while leveraging the numerical convergence of the Von Mises model. Implemented within finite element analysis, the model demonstrates superior computational performance, effectively addressing severe mesh dependency issues in strain-softening soils. Validated against existing literature data, the model accurately predicts the ultimate pullout resistance of plate anchors, accounting for both strength heterogeneity and strain-softening behaviors. Parametric analyses reveal that strain-softening parameters have a significant impact on the uplift resistance factor Nc, with reductions in the strain-softening coefficient ω and increases in the shape factor η leading to a decrease in Nc. In contrast, strength heterogeneity, characterized by the gradient k, shows only a slight effect on Nc. The paper also provides a table of Nc for plate anchors in heterogeneous strain-softening soil at different depths, based on extensive calculations. This table covers common engineering scenarios and can help engineers to determine Nc. Additionally, the study elucidates the progressive failure mechanism of soils during anchor uplift, revealing the evolution of equivalent plastic strain and undrained shear strength.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".