Evaluating the suitability of CRMB for high performance asphalt applications
Bibliographic record
Abstract
High modulus asphalt concrete is widely used in regions with hot/mild weathers and ultra-heavy traffic due to its superior stiffness and durability. However, its use in cold climates is limited, as the stiff binders typically employed become brittle at low temperatures. To address this, high performance asphalt concrete (HPAC) tailored for cold regions is needed. This study explores the optimization of crumb rubber modifier (CRM) content for HPAC applications, targeting a high-temperature performance grade (PG) of 82 as a performance benchmark. A PG 64-22 binder was modified with 30 mesh (600 µm) CRM at 3%–15% concentrations and blended for 30–90 min at 180 °C. The dynamic modulus curve confirmed that 12% CRM achieves the PG 82 target, striking a balance between stiffness and flexibility. Blending time optimization showed no significant difference in PG across durations, allowing for shorter blending times, supporting the feasibility of in-field blending. Although storage stability due to phase separation remains a concern, in-field blending effectively addresses this issue and enhances practical applicability.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".