Corrosion Performance of Buried Corrugated Galvanized Steel under Accelerated Wetting/Drying Cyclic Corrosion Test
Bibliographic record
Abstract
Rehabilitation of corroded buried galvanized steel structures, including corrugated metal culverts (CMCs) and pipes (CMPs), requires a deep understanding of the corrosion process and the corresponding deterioration. The current paper describes an accelerated laboratory corrosion test of corrugated galvanized steel coupons exposed to sequenced wetting/drying cycles ranging from 50 and up to 1600 cycles. The analysis demonstrates the influence of applying an increased number of wetting/drying cycles on the acceleration of the developed corrosion in the buried galvanized steel coupons. The study examines changes in the steel geometry represented by thickness loss and the accompanied deterioration of the mechanical properties such as tensile strength, hardness, and ductility over relatively short periods of time. It was observed that corrosion was insignificant as long as the zinc coating of the galvanized steel lasted. However, when the zinc was almost fully depleted, the bare steel was directly subjected to the surrounding corrosive environment, causing greater corrosion damage during subsequent wetting/drying cycles. Based on four standard mathematical models, the paper also presents approximate average corrosion predictions for bare steel in the galvanized coupons, to assess the impact of potential damage due to corrosion and determine essential rehabilitation measures.
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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".