Steel Bridge-Coating Systems and Their Environmental Impacts: Current Practices and Future Trends
Bibliographic record
Abstract
Coatings are essential for protecting structural steel bridges from corrosion in harsh environmental conditions. The selection of a suitable coating system can significantly impact the performance and longevity of a bridge as well as its environmental footprint. This study investigates the U.S. and Canada’s environmental ISO corrosivity map and the general environments to which bridges are exposed. Additionally, environmental data and road maintenance practices of transportation departments and the use of de-icing salts were investigated to examine the impact of micro-corrosive environments on bridge elements. The study reviews commonly examined coating systems and their expected service life in moderate and highly corrosive environments. This sheds light on factors influencing coating system selection, such as life-cycle cost analysis and maintenance practices for bridge elements. For the first time to our knowledge, an environmental life-cycle evaluation of one of the most commonly used coating systems with theoretical maintenance scheduling for a bridge project’s expected service life is presented to encourage the use of a quantitative tool for environmental impact assessment of coatings in terms of global warming potential (GWP). Additionally, perspectives on patented state-of-the-art and future steel-protective technologies and their potential role in bridge engineering are reviewed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".