Design of long life concrete structures using high performance reinforcing steels
Bibliographic record
Abstract
The need to upgrade the large number of aging reinforced concrete (RC) structures that are exposed to aggressive environments, such as de-icing salts in cold regions and sea water requires the development of innovative solutions that will lead to the construction of long life RC structures with low life cycle costs. In this paper, the impacts of using high performance reinforcing steels (HPS), such as 316 LN, 304 and 2205 duplex stainless steels and ASTM - 1035 (or chromium) steel on the service life and structural behaviour of RC structures are investigated. In terms of resistance to chloride attack, 316 LN stainless steel provided the highest value, followed by 2205 duplex steel, chromium steel and then carbon steel. In terms of yield and ultimate strengths, chromium steel exhibited the highest values followed by 316 LN and 22065 duplex steels then carbon steel. In terms of ductility, the RC beams reinforced with 316 LN steel exhibited the highest capacity to deform before fracture, followed by duplex 2205 steel, carbon steel, and then chromium steel. In terms of flexural design of RC beams reinforced with HPS, the same flexural capacity is achieved by using much lower areas of reinforcement for chromium steel, followed by stainless steel and then carbon steel. This suggests that greater savings in material, labour, and maintenance costs are possible when using chromium steel and stainless steel as a reinforcement for RC structures built in aggressive environments, such as highway bridge decks, parking structures, marine and offshore structures.
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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 | 0.001 | 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".