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Record W3048105594 · doi:10.1139/cgj-2020-0272

Development of a hybrid soil pressure sensor and its application to soil compaction

2020· article· en· W3048105594 on OpenAlexvenueno aff
Mark Talesnick, M. Ringel, Kyle M. Rollins

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersFederal Highway Administration
KeywordsCompactionGeotechnical engineeringStiffnessPressure sensorSoil compactionCalibrationLateral earth pressureOverburden pressurePressure measurementDynamic compactionEnvironmental scienceMaterials scienceGeologyEngineeringComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

A new soil pressure sensor based on a combination of the deflecting membrane and fluid-filled approaches has been developed. The advantages of this combined approach are that issues of sensor compliance are eliminated without reducing the effectiveness of the sensor to be used for dynamic measurements. Calibration and verification testing performed under controlled laboratory conditions illustrate these benefits. The new system was implemented in a full-scale field trial that involved the construction of a compacted engineered fill 1.8 m in height. As each layer of fill was placed and compacted, vertical in-soil pressure and vertical in-soil strain were continuously measured. During the vibratory compaction process both vertical soil pressure and vertical soil strain histories were captured in each layer. The data collected allowed for the determination of fill stiffness for both static and dynamic conditions. The results illustrate the effect of both confining pressure and strain level on fill stiffness. The relationship between compaction pressure and depth is clearly defined.

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 score0.444
Threshold uncertainty score0.529

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 head0.011
GPT teacher head0.193
Teacher spread0.182 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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