Effects of Variation in Quarter-Car Simulation Speed on International Roughness Index Algorithm
Bibliographic record
Abstract
The international roughness index (IRI) is widely used throughout the world as a measure of road roughness. A quarter-car simulation at 80 km/h is performed on the longitudinal profile to compute IRI. Questions have been raised regarding the applicability of IRI for roads that are used at speeds above or below this simulation speed. To gain more insight into the effects of simulation speed, an investigation was carried out to determine how the roughness computed from the IRI model changes for different simulation speeds of the quarter car. This investigation was performed on an asphalt concrete data set and a jointed portland cement concrete data set. For simulation speeds between 60 and 110 km/h, the response from the IRI model was within ±0.20 m/km of the IRI for 80% of the asphalt sections and 61 % of the concrete sections used in the study. Although the output from the quarter-car model for the different simulation speeds was different from the IRI (simulation speed of 80 km/h), it is unclear to what extent a user's perception of the roughness of a roadway changes with the speed of travel. If examples of roadways are found where the subjective opinion of roadway users of the roughness seems inconsistent with the IRI, it is recommended that the IRI model be used with the current speed limit of the roadway to examine whether the obtained output provides a better match with the opinion expressed by roadway users.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".