The Effect of Surface and Loading Conditions on the Corrosion Performance of Stainless Steel Rebar
Bibliographic record
Abstract
Deterioration of reinforced concrete structures due primarily to chloride induced corrosion of plain carbon-steel reinforcement is a widespread problem, particularly in areas close to marine environments and where de-icing salts are used to keep roadways clear of ice. Replacing plain carbon-steel rebar with highly corrosion resistant stainless steel rebar has been shown to greatly increase the lifespan of concrete structures in harsh environments, and yields favourable life-cycle costs despite high initial costs. In attempt to lower stainless steel rebar’s initial cost of processing, this research compared its corrosion resistance in the pickled (mill scale removed) and as-rolled (mill scale intact) surface conditions. Rebar was embedded in highly-chloride contaminated concrete, and corrosion performance between the two surface types was compared in order to determine if conventional pickling of stainless steel rebar is necessary. A second part of this research addressed possible concern of reduced corrosion resistance of pickled stainless steel rebar in concrete exposed to chlorides when subjected to dynamic loading due to micro-motion at the concrete/crack interface. \nIt was concluded that as-rolled stainless steel rebar in aggressive environments would provide sufficient corrosion resistance for the 75 year lifespan currently specified by the Canadian Bridge Code (CAN/CSA-S6-06, 2006), however it is recommended that monitoring of these specimens be continued to ensure high corrosion rates and/or concrete cracking do not develop. As well, investigation into the effects crevice corrosion cells found in typical concrete structures could have on as-rolled stainless steel rebar’s corrosion resistance should be undertaken. With regard to loading conditions, no significant evidence was found suggesting that pickled stainless steel rebar has reduced corrosion resistance when loaded dynamically versus statically. Therefore pickled stainless steel rebar is recommended for use in dynamically loaded concrete structures if others factors permit. However, the higher electrochemical noise measured during cyclic loading suggests that corrosion behaviour could be influenced largely by frequency of loading, and so further study should be undertaken for applications involving more extreme cyclic loading conditions than those used in this experiment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".