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Record W4402226784 · doi:10.1061/jmcee7.mteng-17700

Evaluating Mechanical Properties of Lightweight Cellular Concrete Backfill with Dynamic Cone Penetration Tests

2024· article· en· W4402226784 on OpenAlexaff
Yu-Qiu Ye, Jie Han, Brad Dolton, Hao Liu, Robert L. Parsons

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsBGC Engineering (Canada)
Fundersnot available
KeywordsMaterials sciencePenetration (warfare)Geotechnical engineeringPenetration testComposite materialDurabilityStructural engineeringEngineeringAsphalt

Abstract

fetched live from OpenAlex

Dynamic cone penetrometer (DCP) has been commonly used for the evaluation and quality control/assurance of soils before, during, and after construction in civil engineering projects. This test equipment has been increasingly used for geotechnical engineering applications but not yet been used to evaluate lightweight cellular concrete (LCC) as a backfill material. This technical note reports laboratory and field DCP tests to evaluate the properties of LCC. To establish the relationship between DCP data and other material properties, unconfined compressive strength and California bearing ratio (CBR) tests were conducted on this material. The test results showed that the measured unconfined compressive strength and CBR of LCC increased as its density increased. The DCP indices (DCPI) for the LCC specimens at different densities were almost constant with the penetration depth. This technical note proposes the correlations between the DCPI and the unconfined compressive strength and CBR, which may be used for future applications.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.101
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.225
Teacher spread0.212 · 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 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
Published2024
Admission routes1
Has abstractyes

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