A Normalized Hyperbolic Approach for Predicting Peak Shear Strength in Multistage Direct Shear Tests
Bibliographic record
Abstract
This study presents a predictive framework for estimating peak shear strength and the corresponding shear displacement in direct shear tests, with a particular focus on applications to multistage testing.A normalized hyperbolic function, originally developed for triaxial tests, is adapted to represent the shear stress-displacement curve up to failure.Based on a dataset of 484 direct shear tests performed on 175 different soils, the parameters of the model were derived through regression and empirically linked to the normalized secant elastic modulus.In multistage direct shear tests, early termination of the initial shearing phases often prevents the direct measurement of peak values.To address this, a prediction algorithm was developed that estimates the unknown peak shear strength and displacement based on the initial portion of the shear curve.This algorithm combines empirical relationships with a stochastic search method based on differential evolution to minimize the prediction error.The model was validated across the full dataset, and simulations showed that peak values could be predicted with high accuracy even when only 60% of the displacement at failure was used as input.The results highlight the potential of this approach to improve the reliability and efficiency of multistage shear testing in fine-grained, coarse-grained, and mixed soils.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".