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Record W2974359094 · doi:10.5267/j.esm.2019.9.001

Experimental and numerical simulation of corrosion induced expansive pressure on concrete cover

2019· article· en· W2974359094 on OpenAlexvenueno aff
Sheikh Shakib, Abu Zakir Morshed, Md. Hasan Ali

Bibliographic record

VenueEngineering Solid Mechanics · 2019
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
Fundersnot available
KeywordsExpansiveCorrosionMaterials scienceCover (algebra)Computer simulationConcrete coverStructural engineeringGeotechnical engineeringComposite materialForensic engineeringEngineeringMechanical engineeringCompressive strengthSimulation

Abstract

fetched live from OpenAlex

In this research, an investigation was carried out through experimentation and numerical modeling to evaluate the cracking pressure induced due to corrosion of reinforcement for different cover thicknesses, grades of concrete and bar locations. The model was also used to determine the patterns of crack for a different number of bars and the effect of bar diameter on cracking pressure. The critical pressure was measured experimentally by applying hydraulic pressure through a hole in concrete cubes of 150 mm. Abaqus 6.14 was used as a modeling platform. From the experimentation and numerical modeling, it was found that with the increase of cover thickness the critical pressure was increased irrespective of bar locations. A lower cracking pressure was observed for corner bar with respect to the sidebar. On the other hand, with the increase in bar diameter, a decrease in cracking pressure was observed. The critical pressure observed was also increased with higher grades of concrete. For corner bars with cover thickness 37.5 mm, the critical pressure was found as 6-10 MPa and it increased up to 17 MPa for the cover thickness of 64 mm for different grades of concrete. On the other hand, for other bar location with the cover thickness of 37.5 mm and 64 mm, the pressure required to initiate crack was about 7.6 MPa and 14.8 MPa, respectively, for C20 grade concrete.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.771

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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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 designSimulation or modeling
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

Citations3
Published2019
Admission routes1
Has abstractyes

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