Automated Life Cycle Analysis Tool for Bridge Design: A User-Friendly Model for Sustainability
Bibliographic record
Abstract
Large-scale infrastructures including bridges are designed for long lifespans and yet require substantial materials such as concrete and steel, which have high impacts on the environment.Therefore, considering the sustainability requirements early during the design stage is crucial.Life cycle assessment (LCA), which is a systematic and standardized approach for quantifying these environmental impacts, provides designers a better insight to develop eco-friendly solutions that reduce these long-term impacts.It also helps decision-makers to compare different design alternatives and select the one that meets the set sustainability goals.This paper introduces an innovative computer model to minimize the gaps between designing bridges and analyzing their environmental impacts to help engineers investigate and evaluate the design's impact early in the conceiving process.The developed model is a standalone graphical user interface (GUI) that provides a platform to input, process and analyze necessary data for evaluating the environmental impacts across various LCA stages.The development methodology used in this study starts by collecting and storing essential data in a database created for that purpose in Excel and MySQL and thereafter mapping the data with the Life Cycle Inventory (LCI), specifically the Ecoinvent database.Afterwards, the model's implementation and creation of the user interface are fulfilled by using C# programming language and openLCA platform.Finally, the model's generated outcomes are provided to users in graphical and tabulated formats.The said model will offer a practical and efficient solution to integrate sustainability requirements during the early stages of designing bridges and will facilitate making informed decisions to minimize their environmental impacts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".