Comparison of Different Methods for Conducting Multistage Direct Shear Tests
Bibliographic record
Abstract
Large-scale direct shear tests are frequently used to determine the shear strength of coarse-grained or mixed-grained soils.Standard practice requires carrying out at least three singlestage tests, each at a different normal stress on a new specimen.Although this ensures reliable shear parameters, it demands considerable material volume and lengthy testing times.As an alternative, multistage direct shear testing applies multiple shear phases to a single specimen, significantly reducing sample volume and laboratory time.However, each additional shear phase may alter the soil structure and affect subsequent peak shear strengths, especially in dense or overconsolidated soils.This study systematically compares singlestage and multistage direct shear tests on a mixed-grained soil with high gravel content.Five distinct multistage methods (MSA-MSE) were evaluated, varying in shear displacement and normal stress reset conditions.Specimens were compacted to medium-dense to dense conditions with water contents close to the optimum value determined by the standard Proctor test.Comparisons of the defined secant slope 𝑆 10-50 (calculated between 10% and 50% of the peak shear stress), the dilation angle, peak shear strength, and shear parameters (friction angle and cohesion) highlight how methodological differences influence the choice of testing method.The results reveal that methods involving full shear displacement reset between stages (MSB and MSC) provide shear strength parameters closely matching those from singlestage tests.In contrast, methods without full reset (MSA and MSD) or with reversed loading sequences (MSE) produced lower peak shear strengths and distorted shear parameters due to cumulative disturbance or induced overconsolidation.These findings highlight the essential role of controlling both displacement history and loading sequence to ensure reliable parameter interpretation in multistage testing.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".