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Record W4313496897 · doi:10.32047/cwb.2022.27.2.4

Effect of different cement content and water cement ratio on carbonation depth and probability of carbonation induced corrosion for concrete

2022· article· en· W4313496897 on OpenAlexafffund
Mostafa Hasan, Lamya Amleh, Hesham Othman

Bibliographic record

VenueCement Wapno Beton · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsToronto Metropolitan University
FundersNational Research Council Canada
KeywordsCarbonationCementCorrosionMaterials scienceWater–cement ratioEnvironmental scienceComposite material

Abstract

fetched live from OpenAlex

Nowadays transportation infrastructure is subjected to a high percentage of carbon dioxide emissions. CO 2 greatly affects the carbonation depth of concrete, which can affect the deck for reinforced concrete bridges causing corrosion to steel reinforcement. Cement content and water to cement ratio greatly influence the carbonation depth of concrete. This study aims to investigate the effect of climate change on carbonation depth by considering different Representative Concentration Pathways [RCPs]. In addition, the effect of different compressive strengths on the carbonation depth was investigated in this research. Additionally, the effect of different cement contents on the probability of carbonation-induced corrosion has been investigated. Two parameters are considered, namely, the cement content 400 kg/m3, 350 kg/m3, and 250 kg/m3 and, the water to cement ratio [0.45 and 0.55]. This study RCPs for CO2 concentrations. The RCP [2.6, 4.5, 6, and 8.5] trajectory was used by the Intergovernmental Panel on Climate Change [IPCC], which represents low emission pathways, intermediate emission pathways, and high emission pathways, respectively. Carbonation depth has been estimated using Yoon’s and Stewart’s equations. Furthermore, the probability of carbonation-induced corrosion has been investigated using Monte Carlo simulation and the first-order reliability method at different cement contents for RCP 8.5. The percentage increase in the carbonation depth using Yoon’s compared to Stewart’s equations for concrete mixes which consist of different water to cement ratios and cement content for the years 2025 and 2100 for both RCP 2.6 and RCP 8.5 were calculated. Finally, the probability of carbonation-induced corrosion conducted by FORM for cement content of 250 kg/m3 has been increased by 18% compared to the probability of carbonation including cement content equal to 400 kg/m 3 for the year 2100.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.028
GPT teacher head0.234
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2022
Admission routes2
Has abstractyes

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