Performance based characterization of virgin and recycled aggregate base materials
Bibliographic record
Abstract
Characterization of the effect of physical properties on the performance such as stiffness and drainage of unbound granular materials is necessary in order to incorporate them in pavement design. The stiffness, deformation and permeability behaviour of unbound granular materials are the essential design inputs for Mechanistic-Empirical Pavement Design Guide as well as empirical design methods. The performance based specifications are aimed to design, and construct a durable and cost effective material throughout the design life of a pavement. However, the specification varies among jurisdiction depending on the historical or current practice, locally available materials, landform, climate and drainage. A literature review on the current unbound granular materials virgin and recycled concrete aggregate base construction specification has been carried out in this study. Resilient modulus, permanent deformation and permeability tests have been carried out on seven gradations of materials from locally available sources. Resilient modulus stiffness of unbound granular material at two different conditioning stress level have been compared in the study. The long term deformation behaviour has also been characterized from results of the permanent deformation test using shakedown approach, dissipated energy approach and a simplified approach. The results show improvement in resilient modulus and permanent deformation for the proposed specification compared to the currently used materials as a results of reduced fines content, increased crush count and inclusion of larger maximum aggregate size into the gradation. A significant effect of particle packing on permeability of granular materials have also been found, in addition to the effect of fines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".