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Record W4415254620 · doi:10.1139/cgj-2025-0104

Simple-shear and direct-shear behaviours of grass-rooted soils

2025· article· en· W4415254620 on OpenAlexvenueno aff
Ali Akbar Karimzadeh, Anthony Kwan Leung, Jun Zhu, Peng Han

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsnot available
Fundersnot available
KeywordsShearing (physics)Soil waterCohesion (chemistry)DilatantPrincipal stressReinforcementFriction angleDirect shear testShear (geology)

Abstract

fetched live from OpenAlex

Root reinforcement to soil has been commonly quantified by direct-shear tests (DSTs). Despite the simplicity and popularity, this testing method is often criticised for imposing a predefined shear plane, unrealistically representing the in situ stress conditions. Suitability of using DST to characterise the mechanical properties of rooted soils has never been discussed before. This study aims to investigate the differences in the shearing behaviour and stress–dilatancy relationships of rooted soils obtained from simple-shear tests (SSTs) and DSTs. A new stress–dilatancy relationship for rooted soils was derived to explain the additional soil dilatancy contributed by roots upon shearing. The measurements and prediction consistently revealed that the DSTs substantially overestimated the cohesion (by 100%) and peak friction angle (by 15%) of rooted soils, compared to SSTs. This phenomenon is due to the much greater principal stress rotation in the DSTs due to the undesirable stress concentration near the forced horizontal shear plane, causing virtual increases in root reinforcement. Cautions should be taken when using DST to quantity root reinforcement to prevent unsafe engineering design. Predictions made by the validated model showed that the effects of root-induced dilatancy and the associated increase in peak friction angle were more prominent for shallow rooted soils.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.231
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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