International roughness index prediction based on multigranularity fuzzy time series and particle swarm optimization
Bibliographic record
Abstract
• Based on fuzzy trend forecasting, automatic clustering and PSO techniques. • IRI is regarded as fuzzy time series, which are divided into different granular spaces. • IRI prediction in this research can utilize multiple factors. • PSO technique is employed incorporating with multi-granularity analysis. The effective prediction of pavement performance trends can help in achieving the cost-effective management of pavements over their service life. The international roughness index (IRI) is a widely used pavement performance index, which can be considered as a time-dependent variable in terms of scientific modeling. This research aims to develop an innovative IRI prediction model based on fuzzy-trend time-series forecasting and particle swarm optimization (PSO) techniques. Raw datasets extracted from the Long-Term Pavement Performance database are used for model training, testing, and performance assessment. First, IRI values are divided into different granular spaces, which are considered as the principal factor and subfactors. In addition, the multifactor interval division method is proposed according to the principle of the automatic clustering technique. Next, a second-order fuzzy-trend model and fuzzy-trend relationship classification method are proposed to predict the fuzzy-trend of each factor. Then, the fuzzy-trend states for multiple granular spaces are generated while giving full consideration to various uncertainties. Finally, the PSO technique is used to optimize the performance model while carrying out future IRI forecasting. Comparative experiments are performed using more than 20,000 data items from different regions to verify the effectiveness of the proposed method. The experimental results indicate that the proposed method outperforms other approaches including the polynomial fitting, autoregressive integrated moving average, and backpropagation neural network methods in terms of the root mean squared error (0.191) and relative error (6.37%).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".