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Record W2101821979 · doi:10.1139/cgj-2013-0339

Effect of compaction water content on the strength of cement-stabilized rammed earth materials

2014· article· en· W2101821979 on OpenAlexvenueno aff
Chris Beckett, Daniela Ciancio

Bibliographic record

VenueCanadian Geotechnical Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsnot available
Fundersnot available
KeywordsCompactionCompressive strengthCementMaterials scienceRammed earthPortland cementGeotechnical engineeringMicrostructureCuring (chemistry)Composite materialWater contentProctor compaction testScanning electron microscopeGeology

Abstract

fetched live from OpenAlex

Current guidelines suggest that stabilized rammed earth materials be compacted at their optimum water content to achieve their maximum strength. Although this is true for traditional rammed earth, there is no evidence that this procedure should also be used for cement-stabilized rammed earth. Furthermore, the water content used at compaction is usually difficult to control on a construction site, so that material might be compacted at water contents other than the optimum. In this paper, a novel experimental programme is presented in which the effect of compaction water content on the unconfined compressive strength of crushed limestone stabilized to 5% Portland cement content is investigated for a range of curing periods. Freeze drying of specimens was used to arrest cement hydration to determine the evolution of hydrated cement content. Scanning electron microscope (SEM) analysis was used to identify differences between the final material microstructures. Results are discussed demonstrating the intimate link between the amount of hydrated cement, material microstructure, and compressive strength.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.017
GPT teacher head0.205
Teacher spread0.187 · 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

Citations72
Published2014
Admission routes1
Has abstractyes

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Same venueCanadian Geotechnical JournalSame topicHygrothermal properties of building materialsFrench-language works237,207