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Record W2725882002 · doi:10.1061/9780784480809.029

Determining the Tensile Strength of Soil-Cement

2017· article· en· W2725882002 on OpenAlexaffabout
Brian W. Wilson, Jim Coull

Bibliographic record

VenueGrouting 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsBurnaby Hospital
Fundersnot available
KeywordsCementUltimate tensile strengthCompressive strengthAcknowledgementCompression (physics)Soil cementComputer scienceGeotechnical engineeringCivil engineeringScale (ratio)Mixing (physics)EngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Deep soil mixing was proposed as a means to improve the poor subsurface conditions at the site of a proposed new electrical substation situated in Burnaby, British Columbia, Canada. Tender documents specified minimum compressive and tensile strengths, as well as a minimum compression modulus, with no acknowledgement of the inter-related nature of these attributes, and no bench scale testing to inform prospective bidders. This paper discusses the contractual risks introduced by use of the set of inconsistent criteria adopted in the project specification, and provides details and results of the trial program utilized by the contractor to establish a mix design criteria that would achieve the specification. Further, the paper discusses two testing options employed for the determination of the tensile capacity of soil-cement, the variation in results obtained for each method, the variation in the relationship of each method to the unconfined compressive strength (UCS), and compares these to previously published relationships.

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)0.002
metaresearch head score (Gemma)0.006
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.019
GPT teacher head0.223
Teacher spread0.204 · 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
Published2017
Admission routes2
Has abstractyes

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