Study of environmental impact from geosynthetic reinforced soil walls
Bibliographic record
Abstract
Reinforced soil walls (RSWs) have proven to be a reliable and resilient solution in many geotechnical applications (e.g., bridge abutments, highway and railway embankments, soil retaining walls, dikes, among others). Moreover, the reduced impact of these types of structures over traditional solutions has been compared using life cycle analysis (LCA) and sustainability assessment methodologies. Nowadays, RSWs are often constructed with geosynthetic materials as reinforcement elements due to their ease of use, cost, and technical viability. The use of geosynthetic materials can assist to meet the global challenges of the United Nations global sustainability goals and to adapt to the effects of climate change. The LCA methodology allows designers to determine the environmental impact of different candidate solutions or structures for a given design life. By providing comparable score-based results, a LCA permits objective decision making. The present work describes the environmental impact assessment of idealized polymer strap geosynthetic RSWs using the LCA methodology. Case studies are focused on the use of different backfill material (granular soil from a quarry or riverbed, recycled construction aggregate, and low quality locally available soil). Analysis boundaries include cradle-to-gate considerations and a 100-year design life. Results indicate the reduced environmental impact of using on-site backfill.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".