Comparison between major repair and replacement options for a bridge deck life cycle assessment: A case study
Bibliographic record
Abstract
Material production, manufacturing, transportation, usage, and end of lifeprocessing are usually the main contributors defining the life cycle assessment (LCA). Bridge infrastructure is important to the economy and the society. Over their life cycle, highway bridges experience several stressors that can significantly affect their structural performance and therefore require rehabilitation. This paper discusses the life cycle analysis of bridge rehabilitation decisions and demonstrates the analysis with a case study of a bridge located in Ontario, Canada. The LCA of the bridge deck is analyzed for two rehabilitation strategies: major repair and replacement. The study focuses on evaluating the different life cycle phases of the bridge deck by assessing their carbon dioxide emission, energy consumption and cost. Also, the paper presents the impact of the different elements within each phase to identify the most contributing elements. The LCA of the bridge deck is analyzed and estimated with the aid of CES EduPack 2016 software that includes a database of more than 4000 different materials and more than 200 manufacturing processes. Analysis of the case study shows that material phase causes significant life cycle impact. The study concluded that the deck replacement yields higher environmental impact and life cycle cost compared to repairing and strengthening the deck.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".