Reliability Analysis of Cracking and Faulting Prediction in the New Mechanistic–Empirical Pavement Design Procedure
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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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".