Liquefaction and Deformation Analyses Using a Total Stress Approach
Bibliographic record
Abstract
Estimating deformations due to seismically induced liquefaction is often accomplished with a series of simplified uncoupled analyses. An alternative approach is presented in this paper that builds upon this common practice while making significant improvements to the modeling quality. A two-dimensional finite-difference analysis is performed in the time domain using a simple plasticity-based constitutive model. The triggering of liquefaction is assessed in each element by continuously weighting the cyclic shear stress history. Liquefaction is initially predicted in the most susceptible elements and then progressively spreads as the earthquake continues. The properties of liquefied elements are adjusted at the instant of liquefaction to reflect the anticipated loss of strength and stiffness. Dynamic equilibrium is always maintained so that computed deformations are rationally affected by the structural response, progressing liquefaction, and gravity forces. The method is demonstrated through application to the Upper San Fernando Dam and its response to the San Fernando earthquake of 1971. The objective of this approach is to achieve a practical balance between a rigorous numerical and theoretical analysis and currently accepted practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".