Reliability Analysis of Cracking and Faulting Prediction in the New Mechanistic-Empirical Pavement Design Procedure
Why this work is in the frame
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Bibliographic record
Abstract
Reliability analysis is an important part of the mechanistic–empirical pavement design guide (M-E PDG). Even though mechanistic concepts provide a more accurate and realistic methodology for pavement design, a practical method to consider the uncertainties and variations in design and construction is needed so that a new or rehabilitated pavement can be designed for a desired level of reliability (performance as designed). Several methods, ranging from closed-form approaches to simulation-based methods, can be adopted to perform reliability-based design. However, some methods may be more suitable than others, given the complexities of the design procedure. A formal definition of reliability within the context of the M-E PDG, as well as two reliability analysis approaches considered for incorporation into the design procedure for evaluating the reliability of the rigid pavement design for cracking and faulting, was evaluated. A Monte Carlo–based simulation was combined with the damage accumulation procedure for rigid pavement distress prediction. This approach is recommended for future improvements of the procedure. The development of the reliability analysis procedure implemented into the M-E PDG also was documented. It was demonstrated that although the adopted approach is not as sophisticated as a Monte Carlo–based one, it still represents a step forward compared with AASHTO-93 reliability analysis.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it