A New Approach to Predict Dynamic Loads Considering Highway Alignment Using Data Mining Techniques
Bibliographic record
Abstract
Premature damage to heavy-duty pavement has been found to be significantly caused by the vehicle–highway alignment interaction, especially in mountainous regions. This phenomenon was further verified by field pavement damage investigations and field tests. In order to elucidate the potential mechanism of this interaction, it is important to address the vehicle dynamic loads generated by the interaction between vehicle and pavement. Based on this, the paper realizes a new method of vehicle dynamic load prediction using data mining techniques, namely artificial neural network (ANN) and support vector machine (SVM)). The data, including dynamic loads and highway geometric characteristics, were collected by a wheel force transducer (WFT) and global positioning system (GPS), respectively. The coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the performance of the prediction models. The results showed that the proposed dynamic load prediction model established by ANN was better than that by SVM. Moreover, the model implied that dynamic loads were highly correlated with curvature and longitudinal grade, and furthermore, curvature was found to have a larger effect. The proposed dynamic load prediction technique provides a feasible and rapid approach to identify pavement damage under complex vehicle–highway alignment interactions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".