COMPLETE AND SEMI-COMPLETE EXPLICIT ALGORITHMS OF A UNIFIED CRITICAL STATE MODEL FOR OVER-CONSOLIDATED SOILS
Bibliographic record
Abstract
This paper presents a comparison of the performance of explicit algorithm and semi-complete explicit algorithm in the numerical implementations of an unconventional plastic model for soils. The new model, named CASM-S, is developed by incorporating the sub-loading surface theory into the standard unified clay and sand model (i.e., CASM), to enhance the prediction ability for the mechanical behavior of over-consolidated soils. The complete explicit algorithm of CASM-S is based on the sub-stepping method with the technique of automatic error control (SUBM), while the semi-complete explicit algorithm adopts the cutting-plane integration procedure (CPM). The complete implementation process of this model is performed, and the stability, accuracy, and efficiency of these two algorithms are compared through a series of numerical simulations, such as fluid-structure coupling problem, over-consolidation problem, and square-footing problem. These simulations demonstrate that CASM-S implemented by both the SUBM and CPM can obtain a reliable solution under appropriate size of increments. For the sub-loading surface model with highly nonlinear characteristics used in this paper, the CPM has faster local convergence rate, but the SUBM shows a higher efficiency and accuracy at global level.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".