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Record W2786266575

Finite Element Modelling of Corrosion Damaged Reinforced Concrete Structures

2017· dissertation· en· W2786266575 on OpenAlexfundaboutno aff
Siavash Habibi

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typedissertation
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsFinite element methodCorrosionReinforced concreteStructural engineeringMaterials scienceEngineeringComposite material
DOInot available

Abstract

fetched live from OpenAlex

Corrosion of steel is the predominant deterioration mechanism of reinforced concrete structures throughout the world. This thesis presents the work performed on VecTor2, a nonlinear finite element analysis program developed at the University of Toronto, for analysis of corrosion damaged reinforced concrete structures. Two well-known types of corrosion, namely uniform and pitting, were considered for modelling. Corrosion damage was incorporated in the algorithms of VecTor2 through reduction of the sectional area of reinforcing steel, the bond strength between the reinforcement and concrete, and the mechanical properties such as yield strength of a corroded reinforcing bar.\nThe employed techniques for incorporating corrosion damage in VecTor2 successfully reproduced the load-deflection response of published experiments. Stochastic simulation of the same experiments, performed by employing the stochastic tools of VecTor2, demonstrated the sensitivity of response quantities to changes in various input parameters forming the basis for further research.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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)0.0080.000

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 head0.019
GPT teacher head0.232
Teacher spread0.213 · 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 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

Citations4
Published2017
Admission routes2
Has abstractyes

Explore more

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