Searching for Precision in Pavement Evaluation: A Comparative Analysis of Smartphones for Measuring the International Roughness Index (IRI)
Bibliographic record
Abstract
This study assesses the feasibility of using smartphones to measure the International Roughness Index (IRI) by comparing them with the Roughometer III instrument on a section of the Loja-Malacatos highway.Data on IRI were collected using a mobile app on 51 smartphones mounted in 51 different vehicles, which were then compared with measurements from the Roughometer III to evaluate accuracy and reliability.The results indicate that although the smartphones did not achieve the precision of the specialized device, with a maximum R^2 of 0.334 (RMSE = 1.6 m/km) certain models showed potential for making quick and cost-effective IRI estimates.The study revealed significant variations in accuracy among the devices, influenced by the type and condition of the vehicle, the technical specifications of the smartphone, and the environmental conditions during data collection.The mean absolute error (MAE) and the root mean square error (RMSE) were calculated for each smartphone, identifying those whose measurements deviated least from those obtained by the Roughometer III.Despite the observed limitations, the results suggest a moderate potential of smartphones for estimating IRI, particularly in contexts where specialized equipment is unavailable.The research emphasizes the need to optimize calibration methodologies and to develop specific software that improves the accuracy of data collected with these devices.In conclusion, smartphones represent an accessible and promising tool for the preliminary evaluation of pavement roughness, enabling more frequent and economical monitoring of road infrastructure and allowing greater community involvement in pavement quality management.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".