Galvanic corrosion assessment of ASTM A1010 bridge steel and bolts and effect of temperature and acidity
Bibliographic record
Abstract
This study has compared the galvanic corrosion risk of two types of bolt for ASTM A1010 steel that is used in construction of a new generation of corrosion-resistant stele bridges. The high corrosion resistance of A1010 steel with a more positive corrosion potential imposes a risk of galvanic corrosion with the connection bolts in direct electrical contact. The galvanic corrosion was studied between A1010 steel and galvanized ASTM A325 Type I bolt and ASTM A193 B8 class 2 bolt, The result was compared to that of A325 bolt and weathering steel. Galvanic corrosion risk was evaluated through experimental investigation in the cells of aerated salt solution and was further validated by visual examinations of bolted steel plates in salt spray testing chamber. The effect of temperature and acidity of environments was also investigated to illustrate the impact of climate change. It shows that not only the galvanic corrosion risk of using B8 bolt in A1010 steel bridges is much smaller than that of A325 bolt, but also it is affected much less by temperature rise and increase in acidity. The study suggests that climate change may have very different effects on different components of a steel bridge, and effect on galvanic corrosion could be much worse than the individual components. Furthermore, the corrosivity of atmosphere for bridge design such as pollutants in an atmosphere and temperature rise needs to be reviewed and re-evaluated under climate change.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".