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Record W2925910099 · doi:10.1139/cgj-2018-0833

Liquefaction and post-liquefaction assessment of lightly cemented sands

2019· article· en· W2925910099 on OpenAlexvenueno aff
Habib Rasouli, Behzad Fatahi, Sanjay Nimbalkar

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsLiquefactionGeotechnical engineeringShearing (physics)Cementation (geology)ShrinkageConsolidation (business)GeologyMaterials scienceCementComposite material

Abstract

fetched live from OpenAlex

Post-liquefaction response of lightly cemented sands during an earthquake may change and become similar to uncemented sands due to bonding breakage. In the current study, the effect of degree of cementation on liquefaction and post-liquefaction behaviour of lightly cemented sands was studied through a series of cyclic and monotonic triaxial tests. Portland cement with high early strength and Sydney sand were used to reconstitute the lightly cemented specimens with unconfined compression strength ranging from 25 to 220 kPa. A series of multi-stage soil element tests including stress-controlled cyclic loading events with different amplitudes and post-cyclic undrained monotonic shearing tests were carried out on both uncemented and cemented specimens. Furthermore, a series of undrained monotonic shearing tests without cyclic loading history on different types of specimens was conducted to investigate the effect of cyclic loading history on the post-cyclic response of the specimens. The results show that residual excess pore-water pressure is correlated to the cyclic degradation of lightly cemented sands during cyclic loading. In addition, optical microstructure images of the cemented specimens after liquefaction showed that a major proportion of cementation bonds remained unbroken, which resulted in a superior post-liquefaction response with respect to initial stiffness and shear modulus in comparison to the uncemented sand.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.638

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 integrity0.0000.001
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 head0.005
GPT teacher head0.206
Teacher spread0.200 · 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 designSimulation or modeling
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

Citations43
Published2019
Admission routes1
Has abstractyes

Explore more

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