Risk-based framework for accommodating uncertainty in highway geometric design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The development of highway standard design models involves various assumptions regarding design inputs and the road environment. This paper suggests an improvement to the treatment of uncertainty in design inputs by replacing the current deterministic approach with a reliability-based framework. Reliability theory deals with the propagation of quantified variability in design inputs throughout the design process. In such a framework, each design output corresponds to a theoretical probability of noncompliance to design requirements. These probabilities can be used to assess and compare the a priori safety level associated with various design scenarios. This paper proposes that such a priori safety level of standard design outputs should be consistent and close to a prespecified target level. A set of methods is proposed to determine a target value for design safety. A general framework for calibrating standard design models is presented. To demonstrate the concept, the paper presents an application of the calibration framework to the standard design model of crest vertical curves. Calibrated design charts are constructed to yield a consistent design safety level.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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