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Record W3000303609 · doi:10.1061/9780784482117.020

Nonlinear Failure Envelope for Microbial Induced Calcium Carbonate Precipitation Treated Sand

2019· article· en· W3000303609 on OpenAlexaboutno aff
Ashkan Nafisi, Brina M. Montoya

Bibliographic record

VenueGeo-Congress 2019 · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsNonlinear systemCalcium carbonatePrecipitationEnvelope (radar)CalciumCarbonateGeologyMaterials scienceComputer scienceComposite materialMetallurgyPhysicsMeteorologyTelecommunications

Abstract

fetched live from OpenAlex

Microbial induced calcium carbonate precipitation (MICP) has drawn significant attention as a more sustainable and eco-friendly ground improvement technique compared to conventional methods in recent decades. It has been shown that bio-treatment improves the shear strength of sands remarkably, and this improvement should be predictable. To predict the shear strength after bio-treatment, different types of failure envelopes with various forms can be employed. In the majority of studies on MICP-treated sands, the shear strength parameters have been calculated based on linear failure envelopes, in spite of the fact that linear failure envelopes can overestimate the shear strength at low confinements. The intercept cohesion, indeed, may be overestimated using a linear model. Therefore, this paper presents a nonlinear failure envelope which was developed based on thirteen drained triaxial tests at two levels of cementations on Ottawa 20–30 (a clean coarse sand). The obtained results indicate that nonlinear failure envelope has a higher accuracy in predicting the shear strength of MICP-treated sands at low confinements.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.257
Threshold uncertainty score0.998

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)0.0040.003

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 head0.011
GPT teacher head0.246
Teacher spread0.235 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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