Life Cycle Assessment of Waste Glass Powder Incorporation on Concrete: A Bridge Retrofit Study Case
Bibliographic record
Abstract
The construction sector is responsible for some of the highest energy and natural resources consumption. In this context, new materials and solutions are created aimed at developing sustainable alternatives. While the literature presents papers that evaluate the mechanical and durability properties of concrete with glass waste powder and account for its environmental impact, no papers have executed the evaluation considering the retrofit of bridges. Furthermore, no papers evaluating the materials, construction, and maintenance could be found. Hence, this study proposes a technical and sustainable solution for the retrofit of the Third Bridge of Vitoria, an important intercity urban connector. This study evaluates both the technical and the environmental performance of structural concrete elements, considering the partial substitution of cement with glass waste powder and a baseline scenario with conventional concrete. The environmental impacts were evaluated through the life cycle assessment tool. The results indicate that incorporating waste glass powder in the prestressed hollow-core slabs as a partial cement replacement can improve the durability-related properties and mitigate environmental impact. It also shows that the manufacturing phase is the most impactful and that glass powder can significantly reduce the impact of maintenance.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".