Contribution of Post-Rolling Surface Treatment to Corrosion Performance of Duplex and Austenitic Stainless Steel Reinforcing Bar
Bibliographic record
Abstract
This study elucidates the complex relationship between stainless steel (SS) rebar surface treatment and corrosion resistance in high chloride environments using microscopy, analytical, and conventional electrochemical and novel corrosion microscopy techniques in a short- and long-term assessment. Previous studies on carbon steel reported that millscale defects (cracks and porosity) compromise corrosion resistance by allowing chloride ions to reach the base steel. This study examining three SS grades with four different surface type shows that their high Cr content improves compactness and reduces defectiveness of the inner (Fe-Cr) millscale layer, resulting in better corrosion resistance which varied with surface treatment methods, alloy composition, concrete composition, and chloride exposure method. Despite its defective outer (Fe) millscale layer, as-rolled (AR) bars showed comparable or better corrosion resistance than bars subjected to post-rolling treatment. Treating SS surface with mechanical blasting or acid pickling alone deteriorates corrosion performance due to embedment of blasting particles into rebar surface and/or the presence of loose residual millscale. Employing both shotblasting and acid pickling (SBPK) causes micropits to develop on the bar surface, which deteriorates corrosion resistance. Pore solution tests do not adequately reflect corrosion performance of millscales, as they exhibit high corrosion rate upon uniform exposure to chloride but show better performance in long-term mortar/concrete tests due to longer equilibration of the millscale and passive film with concrete’s high pH environment and non-uniform chloride exposure to rebar surface. Overall, this work shows that achieving consistent post-roll treatment or pristine SBPK surface between manufacturers and batches is a bigger challenge than achieving homogeneous millscale, making AR SS rebar a cost-effective corrosion resistant option for construction.
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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 | 0.000 |
| 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) | 0.001 | 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".