Experimental Assessment of Mineral Filler on the Volumetric Properties and Mechanical Performance of HMA Mixtures
Bibliographic record
Abstract
This research is conducted to evaluate the influence of mineral filler on the volumetric properties, mechanical and field performance of Hot Mix Asphalt (HMA). Two mineral filler types, namely, Hydrated Lime (HL) and Dust Plant (DPt) were used. Three filler proportions were utilized greater than 1% which represents the most applicable percentage, especially for HL, used by the Ministry of Transportation Ontario (MTO). The effect of filler on various volumetric properties including Voids In Mineral Aggregates (VMA), Voids Filled With Asphalt (VFA), dust to binder ratio (Dp) is examined. Mechanical and predicted field performance of HMA to the best filler proportion that meets all the MTO limitations is also investigated. The obtained results indicated that the Optimum Asphalt Content (OAC), VMA, and VFA decrease as the filler content is increased. HMA mixtures that includes DPt filler had the higher values of VMA, VFA, and OAC compared to the hydrated lime. The addition of filler with 2.5% percentage is very successful for both filler types due to satisfying all MTO requirements for volumetric properties of HMA. Based on MTO specifications, the addition of 2.0% filler seems to be unsuccessful for both filler types due to lowering the Dp ratio. Mix design with 3.0% filler was also unsuccessful because of the lower value of OAC meaning that the mix is dry and there is insufficient asphalt binder to coat the aggregate particles. Besides, filler type has a significant effect on the mechanical properties of the HMA mixtures. As a filler in HMA mixtures, the utilization of HL as a portion of 2.5 % leads to a significant improvement in mixture resistance to water and freezing and thawing. The mixtures that included HL have a higher cracking resistance, greater stiffness, and a higher fracture stress than the mixtures that included DPt. Furthermore, predicted field performance indicated better outcomes for mixes with HL compared to DPt mixes. Doi: 10.28991/cej-2020-03091619 Full Text: PDF
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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) | 0.001 | 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".