International Roughness Index Modeling utilizing Adaptive Neuro-Fuzzy Inference System (ANFIS)
Bibliographic record
Abstract
As an essential component of pavement management systems (PMS), deterioration models have been adopted for predicting future conditions of pavement sections. It assists in selecting the best maintenance, repair, and rehabilitation decisions. As a result of the formation and growth of distresses such as cracks and rutting, pavement deterioration reduces serviceability and results in the failure of pavement sections. This paper aimed to develop a pavement condition forecasting model based on a pavement performance indicator called the international roughness index (IRI). The Long-term pavement performance (LTPP) database was used to obtain historical IRI data for pavement sections, including pavement sections in the United States (US) and Canada. Additionally, other pavement characteristics were collected to be used as model inputs, such as pavement age, annual precipitation, annual temperature, Freezing Index, Freeze Thaw, annual Average Humidity min, annual Average Humidity max, wind speed, the ratio of AADTT to AADT, KESAL, and SN. ANOVA analysis was implemented to select the most significant factors to include in the model. Moreover, Adaptive Neuro-Fuzzy Inference System (ANFIS) method was applied to develop the deterioration model. In preparation for model development, the dataset was cleansed and preprocessed. In addition, the data was split into 85% training and 15% testing. The ANFIS model analysis was performed using MATLAB software. The developed model's performance was measured using mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), and it was found to be 0.019, 0.139, and 0.0983, respectively. This study concluded that ANFIS was able to model pavement conditions effectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".