Sustainability assessment of earth-retaining wall structures
Bibliographic record
Abstract
This paper describes a sustainability assessment methodology and example to select the best sustainable option from candidate conventional gravity and cantilever wall types and steel and polymeric soil-reinforced, mechanically stabilised earth (MSE) walls of different heights. Analyses were carried out using the value integrated model for sustainable evaluations (Mives) methodology, which is based on value theory and multi-attribute assumptions. The paper identifies how indicator issues are scored, weighted and aggregated to generate final numerical scores that allow solution options to be ranked. The final scores include an adjustment based on stakeholder preferences for the relative importance of the three sustainability pillars (environmental, economic and societal/functional). The analysis results show that MSE wall solutions were most often the best option in each category compared to conventional gravity and cantilever wall solutions and, thus, most often they were the final choice when scores from each pillar were aggregated to a final score. However, when cost was weighted most highly of the three pillars, then the conventional wall solutions gave the highest (best) Mives score for walls 3 m high. If environmental issues were the most important concern of stakeholders, then the MSE solutions were the best solution, particularly for walls 5 m high and higher.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".