Comportamiento mecánico de mezclas asfálticas tipo superpave y sma
Bibliographic record
Abstract
In U.S. Asphalt mixtures have typically been designed with empirical laboratory design procedures, meaning that field experience is required to determine if the laboratory analysis correlates with pavement performance. However, even with proper adherence to these procedures and the development of mix design criteria, good performance could not be assured.\nThe Superpave is a product of the Strategic Highway Research Program, SHRP. The SHRP was established by U.S. Congress in 1987, $500 million research program to improve the performance, durability of road and the development of performance based asphalt specifications to directly relate laboratory analysis with field performance.\nThe Superpave is being implemented by Americans agencies to replace the Marshall and Hveem design methods. The Superpave system optimizes mixture resistance to permanent deformation, fatigue cracking and low temperature cracking.\nIn the early 1960, the European asphalt industry recognized a critical need for pavements that would be resistant to permanent deformation and the various pavement distresses associated with heavy traffic and low temperature. In response to the need, contractors developed Stone Mastic Asphalt, SMA a gap graded mix containing increased amounts of gravel, mineral filler and asphalt cement as well as decreased amounts of sand. This mixture proved so successful in Germany that its use was continued throughout Europe and currently in USA and Canada.\nThe thesis presents the results of investigations at National University of Engineering for the application of the Superpave and SMA in Peru.
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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 | 0.000 | 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) | 0.002 | 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 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".