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Record W7071503866

STRUCTURE, GEOLOGY, AND ENGINEERING PROPERTIES OF TWO CARBONATIC FINE-GRAINED SOILS

2016· article· en· W7071503866 on OpenAlexaboutno aff

Bibliographic record

VenuePurdue e-Pubs (Purdue University System) · 2016
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsSoil waterCompressibilityShear (geology)CarbonateOedometer test
DOInot available

Abstract

fetched live from OpenAlex

Soft, carbonate-rich, fine-grained soils are commonly found in the glaciated regions of the northern United States and throughout Canada. In addition to the high compressibility potential and low shear strength, these sediments are typically characterized by alternating layers of silts and clays as well as high calcium carbonate content. The unique properties of these deposits make them challenging soils for geotechnical engineers. Despite the prevalence of soft carbonatic soils in Indiana and the concerns associated with their behavior, very limited work has been done to study their engineering properties. This was the motivation for the research, which is founded on an in-depth characterization of a glaciolacustrine carbonatic fine-grained soil deposit formed about 22,000 calendar years ago in the southwestern part of the State of Indiana, USA. The aim of the investigation was the developing of improved knowledge of the behavior of carbonatic fine-grained soils. The project involved field tests (seismic cone penetration tests, standard penetration tests, field vane shear tests), and laboratory experiments (index tests, incremental and constant rate of strain consolidation tests, and K 0-consolidated undrained triaxial tests) conducted on high quality Shelby tube samples. Additionally, the mineralogy and microstructure of the soil was studied in detail. The laboratory tests revealed that the deposit was not homogeneous, as was initially anticipated, but was, instead, formed by two types of soils that repeated in horizontal thin layers. These two soils, referred to as ‘soil M’ and ‘soil C’, both had very high calcium carbonate content, but show distinct index and engineering properties that were ascribed to differences in mineralogy and composition. This stratification was not detected by the field tests. A detailed study of the local geology combined with the observations of the differences between the morphology of pyrite and the clay mineral composition between the two soils, as well as the presence of biological intrusions in only one of the two soils, suggest that different source materials and sedimentary environments alternated during the formation process of the deposit. The microstructural investigation showed that the soil consisted of clay platelets that were covered by a thin layer of a carbonatic coating and interconnected by carbonatic bridges to form aggregates. The laboratory results showed that these interparticle bonds altered the macroscopic behavior of the soil (i.e. index and engineering properties). The consolidation tests showed that the deposit had an overconsolidation ratio (OCR) less than 2 and compressibility parameters markedly dependent on stress. K0-consolidated undrained compression triaxial tests showed that both soils exhibited normalized behavior and that the relationship between strength and stress history was well described by the SHANSEP equation (although the SHANSEP parameters differed for the two soils). Comparison of the field data and laboratory results provided the means to validate published correlations for interpretation of the geotechnical properties of carbonatic soils from field results. For the site examined, correlations to estimate shear wave velocity, stress history, and undrained strength from cone penetration tests (CPT) results were identified.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.006
GPT teacher head0.145
Teacher spread0.139 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2016
Admission routes1
Has abstractyes

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