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Record W2120807808 · doi:10.1061/9780784412350.0026

A Case Study: Unreinforced Soil Mixing for Excavation Support and Bearing Capacity Improvement

2012· article· en· W2120807808 on OpenAlexaff
Daniel Ruffing, M. J. Sheleheda, Rebecca Schindler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsKensington Health
Fundersnot available
KeywordsGroutExcavationGeotechnical engineeringEnvironmental scienceEnvironmental remediationCuring (chemistry)Soil waterBearing capacityMixing (physics)EngineeringSoil scienceMaterials scienceContamination

Abstract

fetched live from OpenAlex

Soil mixing is widely used for environmental site remediation and ground improvement. The main objectives of soil mixing are to increase the strength and decrease the permeability of the soils. Conventionally, unreinforced soil mixing is an uncommon choice for an excavation support system, but a recent case study highlights the potential advantages of using a single technology to accomplish multiple site objectives. The case study provides an overview of the site history, of the design methodology, and of the installation methods used in Lexington, VA. The work was performed in April and May of 2010. Wet "grab" soil-grout samples were collected immediately following installation. All of the soil-grout quality control samples achieved the project design minimum of 100 lbs/in2 (~690 kPa) in less than 28 days of curing.

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 score0.169
Threshold uncertainty score0.414

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)0.0000.000

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 head0.025
GPT teacher head0.227
Teacher spread0.202 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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