Moisture Resistance of Olive Husk Ash Modified Asphalt Mixtures
Bibliographic record
Abstract
In agricultural nations such as Jordan, olive oil production generates substantial quantities of a byproduct known as olive husk.Traditionally utilized for heating purposes, olive husk is now being employed in the production of cleaning solutions and cosmetic products.This study investigates the potential use of olive husk ash (OHA) as a filler in bitumen, when combined with aggregate to produce asphalt mixtures typically employed in road pavement construction.The impact of moisture on the properties of asphalt mixtures containing OHA filler was examined using the Marshall Stability test method.OHA was incorporated as a replacement for bitumen at varying volumes (0, 5, 10, 15, 20%).The optimum bitumen content was added to limestone aggregate to prepare asphalt mixture specimens.Furthermore, the effect of Dryback on the properties of asphalt mixtures was assessed.Findings indicate that the inclusion of OHA in bitumen (by volume) reduced the moisture resistance of specimens, with an optimal performance observed at 10-15% replacement.Additionally, it was found that the moisture effect on asphalt mixtures was reversible, and the observed degradations in strength and stiffness after wet conditioning were not predominantly due to the adverse effects of water.Consequently, the utilization of OHA as a pavement material in the field could potentially reduce production costs and enhance performance, leading to notable environmental benefits.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".