A Simplified Approach to Estimating the Collapsible Behavior of Loess
Bibliographic record
Abstract
Due to the particularity and complexity of loess, it is challenging to estimate its collapsible behavior numerically at present. This paper aims to propose a simplified approach, which is named as the modulus reduction method, to estimate the collapsible behavior of loess. For loess upon wetting, the modulus reduction method assumes that loess collapses as a result of strength reduction due to the additional stress induced by increasing bulk density. Thus, special attention is given to the confirmation and determination approaches of bulk density and deformation modulus of loess upon wetting. Subsequently, a comparative numerical analysis based on the modulus reduction method and the force‐water equivalent method, which is commonly used for the analysis of negative skin friction on piles in collapsible soil, is investigated. It turns out that the result obtained by the modulus reduction method is more consistent with the collapse mechanism of loess compared with that derived by the force‐water equivalent method. Finally, a case history concerning a published field test of loess upon wetting is studied, and the result shows that the simulated deformation characteristics by adopting the modulus reduction method agree excellently well with the measured data. The case study validates that the modulus reduction method is feasible to analyze the collapse of loess and suitable for the numerical simulation involving collapsible loess.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".