Partial safety factors for designing and assessing flexible pavement performance
Bibliographic record
Abstract
In designing and assessing pavement performance, the uncertainty in material properties and geometrical variables of pavement and in traffic and environmental actions should be considered. A single factor is employed to deal with these uncertainties in the current American Association of State Highway and Transportation Officials (AASHTO) guide for design of pavements. However, use of this single factor may not ensure reliability-consistent pavement design and assessment because different random variables that may have different degrees of uncertainty affect the safety and performance of pavement differently. Similar problems associated with structural design have been recognized by code writers and dealt with using partial safety factors or load resistance factors. The present study is focused on evaluating a set of partial safety factors to be used in conjunction with the flexible pavement deterioration model in the Ontario pavement analysis of cost and the model in the AASHTO guide for evaluating the flexible pavement performance or serviceability. Evaluation and probabilistic analyses are carried out using the first-order reliability method and simple simulation technique. The results of the analysis were used to suggest factors that could be used, in a partial safety factor format, for designing or assessing flexible pavement conditions to achieve a specified target safety level.Key words: deterioration, reliability, pavement, serviceability, stochastic process, performance, partial safety factor.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".