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Record W3138361780 · doi:10.3390/ma14061363

Performance of Repaired Concrete under Cyclic Flexural Loading

2021· article· en· W3138361780 on OpenAlexafffund
Boyu Wang, Rishi Gupta

Bibliographic record

VenueMaterials · 2021
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural engineeringCementitiousMaterials scienceMortarFatigue limitFlexural strengthCyclic stressCementComposite materialFatigue testingEngineering

Abstract

fetched live from OpenAlex

There is limited research reported on the effect of cyclic loading on cement-based repair materials as conducting such tests is time consuming. To overcome this issue, this study utilized a novel loading regime consisting of cycle groups with increasing stress amplitude to accelerate the test process. The Palmgren-Minder rule was used to estimate the fatigue life of repaired specimens. Specimens repaired with Mix M (cementitious repair mortar), which was estimated to have the highest 2-million-cycle fatigue endurance limit (77.4%), showed the longest fatigue life (95,991 cycles) during the cyclic loading test, the highest slant, and splitting bond strength among all repair mixes. The estimated two-million cycle fatigue endurance limit of Mix S (70.8%) was very similar to that was reported in literature (71%) using the traditional loading method. This study confirms the usefulness of Palmgren-Minder rule on estimating the fatigue life of repaired specimens. Additionally, the use of the novel loading regime showed the benefit of shortening the test process while producing results similar to those from using traditional loading methods. To improve the prediction accuracy, future research is required to modify the failure criteria to accommodate specimens that may not fail even when the average flexural strength is met.

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 categoriesInsufficient payload (model declined to judge)
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 score0.007
Threshold uncertainty score0.999

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)0.0020.000

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 head0.017
GPT teacher head0.224
Teacher spread0.207 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

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