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Record W2801402046 · doi:10.1039/c8ra00082d

Comparative study on the dynamic properties of lightweight porous concrete

2018· article· en· W2801402046 on OpenAlexaff
Erlei Bai, Jinyu Xu, Song Lu, Ke-Xin Lin, Yiming Zhang

Bibliographic record

VenueRSC Advances · 2018
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsAir Canada
FundersNational Natural Science Foundation of China
KeywordsPorosityPervious concreteMaterials sciencePorous mediumComposite materialCement

Abstract

fetched live from OpenAlex

100 mm split Hopkinson pressure bar (SHPB) improved by wave shaping technology. The dynamic properties, including strength properties, deformation properties, impact toughness and energy absorption properties, of lightweight porous concretes have been analyzed comparatively, and its application prospects have been discussed. The results show that the two types of lightweight porous concretes are strain rate sensitive. Dynamic compression strength increases with strain rate; the correlation between the peak strain, ultimate strain of lightweight porous concrete and strain rate can be expressed by quadratic polynomial; under the impact loading, the impact toughness of lightweight porous concretes increases with strain rate, the amount of absorbed energy increases with the average incident energy change rate, moreover, the relation between the impact toughness and strain rate, and that between the amount of absorbed energy and the average incident energy change rate can both be expressed by exponential functions; compared with EPSC, CCPM has better properties in terms of strength, deformation, impact toughness and energy absorption. Those advantages are more obvious with high strain rate. Therefore, CCPM has more vast application prospects in civil defense projects than EPSC.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.230

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 head0.024
GPT teacher head0.267
Teacher spread0.243 · 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

Citations38
Published2018
Admission routes1
Has abstractyes

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