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Record W4230013437 · doi:10.32920/ryerson.14656794

Fracture Energy, Fatigue and Creep Properties of Engineered Cementitious Composites Incorporating Fly Ash/Slag with Different Aggregates

2021· preprint· en· W4230013437 on OpenAlexafffund
Mohamed A. A. Sherir

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsToronto Metropolitan University
FundersMinistère des Transports
KeywordsMaterials scienceFly ashComposite materialCrackingSilica fumeCementitiousStrain hardening exponentDuctility (Earth science)CreepCement

Abstract

fetched live from OpenAlex

This thesis investigates the influence of microsilica sand and local crushed sand, and different supplementary cementing materials on the mechanical properties of engineered cementitious composites (ECCs). ECC is a special type of high performance fiber reinforced cementitious composite with high ductility which exhibits strain-hardening and multiple-cracking behaviours in tension. The use of local aggregates in ECC production can lower its cost to mitigate the obstacles of wider commercial use. The experimental results showed that multiple-cracking behaviour was developed under fatigue loading for fly ash ECC (FA-ECC) mixtures, and the number of cracks was lower at both lower fatigue stress level and higher fatigue number of cycles. FA-ECC mixtures with silica sand exhibited higher deflection evolution under fatigue loading than FA-ECC mixtures with crushed sand. Based on the experimental results on link slab specimens, both FA-ECC mixtures with silica and crushed sands exhibited almost the same creep behaviour.

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 categoriesMeta-epidemiology (narrow)
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 score0.081
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.194
Teacher spread0.178 · 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.

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

Citations13
Published2021
Admission routes2
Has abstractyes

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