Radius Requirements for Trucks on Three-Dimensional Reverse Horizontal Curves with Intermediate Tangents
Bibliographic record
Abstract
Minimum radius requirements for reverse horizontal curves are currently based on the design of each arc as a separate simple horizontal curve on flat terrain, with the effects of reverse horizontal curvature and vertical alignment being ignored. This paper establishes minimum radius requirements for trucks on three-dimensional (3-D) reverse horizontal curves with intermediate tangents on the basis of vehicle stability. With vehicle simulation software, vehicle dynamics were recorded for the base case of two-dimensional simple curves and for reverse curves superimposed with different vertical alignments (upgrade, downgrade, crest curve, and sag curve). The simulation was run for two maximum superelevation rates, three design vehicles, and different vertical grades. Two mathematical models were developed for flat and 3-D reverse curves. The models provided the minimum radius of the sharper arc of the reverse curve as a function of design speed, maximum superelevation, ratio of flatter to sharper curve radius, design vehicle, and intermediate tangent length. The results showed that an increase in the minimum radius of existing design guides (ranging from 5% to 27%) was required to compensate for the effects of reverse curvature and vertical alignment. The required increase can be reduced by using longer intermediate tangents. Design requirements for the spiral length of reverse curves are also presented.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".