MétaCan
Menu
Back to cohort
Record W2889816929 · doi:10.1520/gtj20180024

Compaction Effort for Uniform Laboratory-Prepared Cohesionless Soil Bed

2018· article· en· W2889816929 on OpenAlexafffund
Yasir M. Alharthi, Adel Hanna

Bibliographic record

VenueGeotechnical Testing Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeotechnical engineeringCompactionGeologySoil compaction

Abstract

fetched live from OpenAlex

Abstract Laboratory testing in geotechnical engineering plays a paramount role in developing theories and empirical formulas. Soil preparation is an integral part of any experiment, which is often required to prepare uniform cohesionless soils in the testing tank. One of the oldest methods to obtain the desired unit weight throughout the testing tank is by dropping the sand from a predetermined height. This method often suffers from particle segregation, which reflects on the test results. Applying compaction effort on the surface of the soil is another method for soil preparation that is widely used because of its simplicity and the repeatability of its results. The procedure is followed by trial and error by adjusting the thickness of the layer and the appropriate energy level until the desired relative density is achieved. However, this technique often produces overconsolidated sand in the testing tank, which is usually ignored. Using compaction effort, uniform sand in the testing tank is achieved by placing the sand in layers; each receives a predetermined compaction effort. In this procedure, the majority of the energy applied is used to compact the immediate top layer, while the rest seeps through to the lower layers, increasing their compaction level. Accordingly, by adjusting the level of the energy applied to each layer, it is possible to produce uniform homogeneous sand in the testing tank. This article presents a laboratory procedure to produce a uniform sand bed and to measure the level of the overconsolidation in the testing tanks, namely, by controlling the thickness of the sublayer and the level of energy applied on each layer.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.499
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.246
Teacher spread0.225 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueGeotechnical Testing JournalSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207