Full-Car Roughness Index as Summary Roughness Statistic
Bibliographic record
Abstract
The international roughness index (IRI) and the half-car roughness index (HRI) are the two commonly used roughness indices for pavement management, decision making, prioritization, budgeting, and planning. This work presents a new statistic, termed the full-car roughness index (FRI), for calculation of roughness from longitudinal pavement profiles. FRI is calculated from a single, equivalent profile that is a composite of four corner profiles based on both civil and mechanical engineering principles. More specifically, the full-car (four-wheel) model combines the rear and front suspension systems through an interdependent relation of motion with the longitudinal axle. To validate this model, the FRI values for different pavement sections are determined for sampling roughness measurements from several states and provinces. Then, the behavior of FRI is compared with that of IRI and HRI. The methodology of assessment uses a Monte Carlo simulation for calibration and validation of the index. Correlations derived from this sensitivity analysis on the basis of regression analysis arrive at a conversion chart to propose conversion values from these indices to FRIs. Overall, this paper suggests that the mechanical response of the proposed full-car model is more representative of the characteristics of a real vehicle than the response of a quarter- or half-car model. The results also indicate that FRI is less sensitive to the governing factors that account for the quarter-car simulation and thus provides an index that is unique, insightful, and more effective in the characterization of ride quality.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".