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Record W4394976469 · doi:10.1007/s10706-024-02781-w

Statistical Reappraisal of the Wax and Mercury Methods for Shrinkage Limit Determinations of Fine-Grained Soils

2024· article· en· W4394976469 on OpenAlexaff
Amin Soltani, Mahdieh Azimi, Brendan C. O’Kelly, Abolfazl Baghbani, Abbas Taheri

Bibliographic record

VenueGeotechnical and Geological Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsQueen's University
FundersFederation University Australia
KeywordsShrinkageMercury (programming language)Soil waterHydrogeologyEnvironmental scienceWaxGeologySoil scienceStatisticsGeotechnical engineeringMathematicsMaterials scienceComputer scienceComposite material

Abstract

fetched live from OpenAlex

Abstract Because of the hazards associated with handling mercury, most standards organizations have withdrawn the conventional mercury (displacement) method (MM) for shrinkage limit (SL) determination of fine-grained soils. Despite attempts to substantiate the wax (coating) method (WM), which is presently the only standardized MM-testing alternative, the geotechnical community remains somewhat hesitant of its adoption in routine practice. To encourage more widespread use of WM-testing, this study re-examines the level of agreement between the MM- and WM-deduced SL parameters (i.e., SLMM and SLWM, respectively). This was achieved by performing comprehensive statistical analyses on the largest and most diverse database of its kind, to date, entailing SLMM:SLWM measurements for 168 different fine-grained soils having wide ranges of plasticity characteristics (i.e., liquid limit = 31.6–362.0%, plasticity index = 8.2–318.0% and SLMM = 7.1–42.0%). Furthermore, an attempt was made to evaluate the SLWM (in lieu of the SLMM) parameter for performing preliminary soil expansivity assessments using existing SLMM-based classification approaches. It was demonstrated that the MM and WM methods do not produce identical SL values for a given fine-grained soil under similar testing conditions, with their discrepancy being systematic and hence likely arising from the differences between the materials (mercury versus wax) and methodologies involved in performing these tests. New SLWM → SLMM conversion relationships were established, allowing SLMM to be deduced as a function of SLWM with high accuracy. Hence, when inputting SLWM in SLMM-based empirical correlations to predict other geoengineering design parameters, the newly proposed conversion relationships can be employed to minimize systematic prediction errors. It was also demonstrated that plasticity-based correlations, at best, can only provide a rough approximation of SLMM. Hence, when the SL is desired, WM-testing or any other alternative method that directly and reliably measures the soil shrinkage factors should be retained. Finally, the same soil-expansivity rankings, as obtained for existing classification systems employing SLMM results, are achieved using SLWM measurements (i.e., without the need of applying SLWM → SLMM conversion equations).

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)0.035
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.066
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.274
Teacher spread0.262 · 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 designObservational
Domainnot available
GenreMethods

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

Citations3
Published2024
Admission routes1
Has abstractyes

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