Environmental Design Considerations Using an Equivalency Index between Granular Drainage and Geosynthetic Alternatives
Bibliographic record
Abstract
Effective drainage is essential in civil engineering projects, and the design is influenced by the required capacity, inflow rates, and the geometric configuration of the structure. Reduction factors are applied based on material and application. Drainage layers can be granular or geosynthetic, with granular layers using free-draining aggregates and geocomposites comprising non-woven geotextiles and drainage cores or pipes. This study aims to establish equivalency between granular layers and geocomposites in water drainage and gas transport, assuming equal long-term capacity under identical conditions. Despite reduction factors and safety margins, geocomposites significantly reduce drainage layer thickness compared to granular layers. The methodology adopted in this project is to present an equivalency design between granular drainage aggregates and drainage geocomposite and environmental benefit of using geosynthetics as a drainage design solution. This is supported by practical examples and environmental benefits such as reduced aggregate extraction, transportation, greenhouse gas emissions, and construction time for similar or better drainage capacity.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Civil engineering study of drainage material equivalency; the object is geotechnical design, not research practice.
The study examines drainage design and geosynthetic materials, not research practice.
Civil engineering study of drainage-layer equivalency and environmental benefits of geosynthetics; object is design practice, not research itself.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".