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Investigation into Mechanical Behavior of Air-Hardening Organic Polymer-Stabilized Silty Sand

2022· article· en· W4292998048 on OpenAlexaff
Ying Wang, Jin Liu, Cheng Lin, Changqing Qi, Zhihao Chen, Wenyue Che, Ke Ma

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMaterials scienceCohesion (chemistry)Ultimate tensile strengthComposite materialPolymerFriction angleHardening (computing)ToughnessShear (geology)Strain hardening exponentCompressive strengthGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

Air-hardening organic polymer (AHOP), a new soil stabilizer, can form a cross-link among soil particles with high strength and toughness. In this study, the effect of the polymer content and density on the mechanical behavior of stabilized silty sand was investigated. The results indicate that the unconfined compressive strength and tensile strength of stabilized silty sand keep a prominent linear relationship with AHOP content that increases with the increment in density; the increment in AHOP content also leads to a higher elastic modulus of stabilized silty sand. The cohesion was also significantly enhanced by the increase in AHOP content and density; the internal friction angle of loose specimens (ρ≤1.50 g/cm3) keeps increasing with AHOP content; however, for dense specimens (ρ≥1.55 g/cm3), 3% polymer content is a turning point, at which the internal friction angle changes nonmonotonically. AHOP film can connect loose silty sand particles and adhere to the surface of particles, leading to silty sand with good strength properties. Based on failure modes, the failure of polymer–particle interactions can be divided into four modes, including (1) tensile fracture at membrane, (2) tensile fracture at polymer–particle interface, (3) shear at membrane, and (4) shear at polymer–particle interface.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.199
Teacher spread0.190 · 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 designBench or experimental
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

Citations7
Published2022
Admission routes1
Has abstractyes

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