Bibliographic record
Abstract
Reliability analysis has been widely applied in many civil engineering fields.Current design standards in structural and geotechnical engineering applications are based on reliability analysis to reflect the design' probability of failure.Several applications have been researched in transportation engineering including road design.However, road design guides have not adopted such applications.Rather, current design practices in road design are typically based on assuming near-worst conditions for most design parameters.For example, in calculating stopping sight distance, conservative values are assumed for the perception-reaction time, deceleration rate, and speed.When the stopping sight distance model is applied or vertical curve design for example, conservative values are also assumed for driver eye height and object height.In addition, while design guidelines are derived to ensure safe vehicle operation, safety is considered only implicitly.For example, the design formula for minimum horizontal curve radius is derived based on the forces acting on a vehicle during cornering to ensure that the vehicle will not skid off the road.However, the final design criteria do not provide designers with tools to assess the consequences of deviation from the design assumptions.As a result, current road design guidelines are generally considered deterministic and safety implicit.A probabilistic, safety-explicit, reliability-based road design is an alternative approach where the full distribution of each design parameter is considered designers can assess the design safety impacts using explicit safety measures.Several researchers have adopted reliability analysis to examine road design criteria methods in road design applications such as stopping sight distance, passing sight distance, intersection sight distance, horizontal curve design, and length of speed change lanes.This presentation will discuss the main reliability analysis methods, proposed safety measures, and existing research utilizing reliability analysis in road design.
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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) | 0.000 | 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 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".