Investigation of sulfur modified asphalt concrete mixes for road construction in the gulf
Bibliographic record
Abstract
ABSTRACT: A study has been funded by Saudi Aramco and conducted at KFUPM on the feasibility of using sulfur as an additive for local asphalt concrete mixtures. The research work covered many aspects of utilizing sulfur modified asphalt in road construction including the field trial at Khursaniyah and the concerns related to air pollution due to sulfur containing gases. This study on sulfur-asphalt concrete consists of testing local sulfur, Shell Canada sulfur-extended asphalt modifier (SEAMTM), with local asphalt-concrete mixes to assess the effect of sulfur and modified sulfur materials by comparing the performance of these paving mixes. Results from laboratory and field trials indicated that SEAMTM and sulfur modified asphalt concrete can be produced, hauled, placed and compacted easily with conventional methods and equipment. There will be no constructability problem with the use of sulfur or SEAMTM binder. Use of SEAMTM or sulfur material at 30% replacement of asphalt could be more economical as compared to regular asphalt as the amount of required asphalt will be reduced in proportion to the SEAMTM/sulfur percentages added. The field tests on assessing the environmental impact of the sulfur-asphalt technology show that there is no long-term hazard for mixes as indicated by acceptable values of emission of hazardous gases such as H during preparation and laying of mixes at 145°C.
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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 | 0.001 | 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".