Environmental life cycle assessment (LCA) for design of climate-resilient bridges – a comprehensive review and a case study
Bibliographic record
Abstract
The life cycle assessment (LCA) study of a bridge and bridge network would provide the environmental profile and hotspot information including the GHG emission of different life stages, among the components. Compared with a rapid adoption of Road LCA into the procurement process in the developed countries, Bridge LCA however remains a nascent area where a few studies conducted in North America. The critical issues of environmental profile, hotspots and benchmarks of bridges remain a challenge due to the complexity of bridge structures, data collection and unfamiliarity of LCA in the bridge community. To address the challenge, this study presents a comprehensive bibliometric analysis and review regarding life cycle environmental impacts assessment of bridge projects around the world to identify the research pattern in order to capture the areas of research needed inside this theme. As a proof of concept, this study continues with conducting an LCA case study of a Bridge Replacement Project on a Canadian signature highway, demonstrating the adoption of the LCA methodology and a framework to streamline the collection of data, to develop, present, and interpret the environmental impacts, in terms of the durability and service life of the bridge asset. The study found that stainless steel rebar decks outperformed black steel decks in terms of CO2 reduction by over 10%, with transport fleet impacts being a significant part of the bridge’s overall environmental impact, highlighting the need for diverse functional units in bridge life cycle assessment studies.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".