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Record W2279125380

Effects of Surface Condition on the Corrosion Performance of Stainless Steel Rebar

2009· dissertation· en· W2279125380 on OpenAlexaboutno aff
Bradley P. Bergsma

Bibliographic record

VenueUWSpace (University of Waterloo) · 2009
Typedissertation
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
Fundersnot available
KeywordsRebarCorrosionMaterials scienceMetallurgyComposite material
DOInot available

Abstract

fetched live from OpenAlex

Corrosion of carbon (black) steel reinforcing bars (rebar) is the major cause of damage and deterioration of reinforced concrete structures in maritime regions and in climates where de-icing salts are used. The cause of the corrosion is diffusion of chloride ions to the steel surface through the concrete in which it is placed. The bars are naturally passivated by the high pH of concrete interstitial pore fluid, and will not corrode in chloride-free concrete. Chloride ions break down the passive film, allowing dissolution of the steel. Corrosion of reinforcing steel drastically reduces the service lives of concrete structures. \nWhere chlorides can not be avoided, stainless steel is becoming increasingly popular as an alternative reinforcing material. Stainless steel is able to withstand greater concentrations of chlorides, extending the service lives of structure in which they are placed. Due to high initial cost, stainless steel is often avoided in the design of new structures. In order to reduce the cost of stainless rebar, it has been proposed that the standard process of abrasive blasting and pickling of the steels not be performed, as these steps are mainly used to restore a bright and shiny surface, a quality not required for steels embedded in concrete. \nAISI 304LN, AISI 316LN and 2205 duplex stainless steels were tested with pickled surfaces as well as with mill-scale intact (as-rolled) in order to determine the affect of pickling vs. not pickling on the corrosion behaviour of the steels. Steels were tested in solutions simulating concrete interstitial pore fluid containing from 0 to 16% Cl- by mass of solution, simulating cement paste with 0 to 7.5% Cl- by mass of cement, which is near the solubility limit of Cl- in pore fluid. Steels were also tested in thin mortar shells, with Cl- ions being rapidly diffused to the surface due to an applied potential gradient. \nThe microcell corrosion performance of the as-rolled steels was slightly worse than that of pickled steels; however, the corrosion rates of the as-rolled steels at 16% Cl- in pore fluid are near 3 µm/year, while black steel is normally observed to be actively corroding at 10 µm/year in cement containing as low as 0.1% Cl- by mass of cement, or 0.2% Cl- by mass of solution. \nNo significant difference was observed between different grades of stainless steel in either the as-rolled or pickled conditions. \nAs-rolled stainless steels exhibited poor pitting resistance when an anodic potential is applied, but the corrosion occurs at potentials much higher than experienced in service and at Cl- concentrations far greater than that needed to initiate corrosion on black steel; the time required to reach these higher Cl- levels would allow for maintenance free service long enough to justify the cost of as-rolled stainless steel over black steel. \nThe Canadian Highway Bridge Design Code, CSA S6-06, specifies that reinforced concrete bridges should meet a service life of 75 years. It is concluded that, given the time required for concentrated chlorides to accumulate at the steel, the stainless steel rebar in the as-rolled condition would allow reinforced concrete structures to reach the specified service life, as long as care is taken to avoid contamination of the steel/surface by black steel from handling, or by secondary phases within the steel, Cr23C6 and MnS in particular.

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 score0.011

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.005
GPT teacher head0.176
Teacher spread0.171 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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