Field Investigation on Complete Replacement of Granular Subbase with Flyash and Blast Furnace Slag in Flexible Pavement
Bibliographic record
Abstract
With rapid industrial and transportation-related infrastructural growth in India, the problems of environmentally injurious metal slags, flyash open dumps and depleting natural aggregate resources are ever-increasing.The focal point of this research is to substantiate the bulk utilization of fly ash and granulated blast furnace slag (GBFS) as a replacement for a granular subbase (GSB) in flexible pavement.Based on detailed laboratory study and finite element analyses, optimal combinations of two waste mixes, i.e., fly ash + 5% lime (FL) and 80% fly ash + 20% GBFS (FG) were chosen.7 test sections (each 3.5 m wide and 50 m long) with different thicknesses of waste mix subbase (200, 300 & 400 mm) layers were constructed.To compare the field performance and service life of waste mix test sections with conventional control section with GSB, structural and functional evaluation is carried out employing a falling weight deflectometer (FWD) and Bump Integrator for two years.The results showed that this technology could save up to 4200 tons of natural aggregates per lane per km of highway construction, with an overall cost saving of 4% to 10%.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".