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Record W4408080320 · doi:10.1080/1064119x.2025.2456660

Testing and modeling of chemically induced changes in mechanical characteristics of cement-stabilized sensitive marine clays

2025· article· en· W4408080320 on OpenAlexaff
Yang An, Yubin Li, Can Yang, Zhuoma Yangjin, Lin Yuan

Bibliographic record

VenueMarine Georesources and Geotechnology · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Ottawa
FundersChina Scholarship Council
KeywordsCementMaterials scienceComposite materialGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

There is a significant variability of salinity level in sensitive marine clays (SMC), which will produce an important impact on the development of mechanical characteristics in stabilized SMC. The influences of salt content (NaCl salt: 3, 10, and 20 g/L) on mechanical properties evolution of cement-stabilized SMC under different curing time (1, 7, 28, 60, and 90 days) have been experimentally investigated and modeled. The results indicate that the strength and modulus increase constantly with time but the time rates decrease. Meanwhile, the apparent improvement of strength and modulus at early age (up to 7 days) is observed. Higher NaCl content can bring a larger strength gain to stabilized SMC after same curing time and the enhancing effect of high salt contents (10 and 20 g/L) becomes more obvious with the extension of curing time. Whereas, the enhancing effect of high NaCl content on modulus is limited compared with strength. Further improvement provided by excessive NaCl salt (20 g/L) is not as effective. In addition, the predictive models have been established to quantitatively evaluate the evolution of mechanical properties in stabilized SMC with different NaCl contents. The capability of developed models has been demonstrated through the good agreement between simulated and experimental results.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.528

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 science0.0000.001
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 head0.015
GPT teacher head0.229
Teacher spread0.214 · 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
Published2025
Admission routes1
Has abstractyes

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