Environmental benefits of life cycle design of concrete bridges
Bibliographic record
Abstract
This paper presents a life cycle-based approach for the design of concrete highway bridges that takes into account their physical, economic and environmental performances over their life cycles, with emphasis on the reduction of greenhouse gas emissions, construction waste production and life cycle cost. The analysis considers all the key stages in the life cycle, which include extraction of raw materials, construction, maintenance, repair, rehabilitation, replacement, and disposal. The proposed life cycle design approach is illustrated on the cases of two concrete highway bridge decks that are built in corrosive environments using high performance concrete containing industrial by-products and normal concrete. It is found that that use of high performance concrete leads to highway bridge decks with: (i) longer service lives (3 to 10 times longer than decks built with normal concrete; (ii) 65 % reductions in CO2 emissions associated with construction and rehabilitation compared to normal concrete decks; and (iii) 40 % to 45 % reductions in life cycle cost compared to normal concrete decks. This paper illustrates the multiple environmental benefits of using high performance concrete to build durable bridges with minimum maintenance that lead to a reduction in the consumption of raw materials and greenhouse gas emissions, and reclaiming industrial waste products and using them as effective construction materials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".