Prediction of Coarse Aggregate Performance by Micro-Deval and Other Soundness, Strength, and Intrinsic Particle Property Tests
Bibliographic record
Abstract
Aggregate samples were collected from the majority of the U.S. states, as well as several Canadian provinces, and subjected to micro-Deval, Los Angeles abrasion, magnesium sulfate soundness, Canadian freeze-thaw, aggregate crushing value, absorption, specific gravity, and particle shape characterization testing to determine whether a correlation exists between laboratory aggregate tests and observed aggregate field performance. Performance ratings were assigned to each aggregate on the basis of the type of distress observed and years of service in the field in hot-mix asphalt and portland cement concrete applications. Numerical and qualitative analyses were performed to evaluate the success of separating good performers from fair and poor performers, with the micro-Deval test alone as well as the micro-Deval test combined with other tests. Furthermore, attempts were made to determine whether a correlation exists between any two tests. The tests that most consistently correlated well with field performance, either alone or in combination with other tests, were micro-Deval, Canadian freeze-thaw, absorption, and specific gravity. Several correlations indicated specific loss limits, which appeared to correctly isolate good performers from the rest. The limits found from correlation of test results and field performance are believed to be effective in identifying good performing aggregates; how-ever, care should be taken in using the limits to exclude aggregates without further consideration because good performers with higher losses were identified. Each agency should develop criteria, including a performance history versus micro-Deval loss, for each aggregate to develop a database that provides accurate performance forecasting.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".