Pavement freezing depth estimation using hybrid deep-learning models
Bibliographic record
Abstract
Predicting pavement temperature by depth is crucial for road design, analysis, and maintenance. However, current methods predominantly utilize regression and/or open-form solutions focusing on highways. Additionally, most machine-learning models focus on asphalt layers and do not extend to deeper pavement layers. Therefore, this study provides deep-learning models using weather parameters to predict pavement temperature from surface to sublayers and estimate pavement freezing depth for developing massive apartment complexes. Temperature-by-depth data collected from thin pavements from three locations in South Korea were used. Comparative analyses of long short-term memory (LSTM), convolutional neural network-LSTM (CNN-LSTM), and convolutional LSTM were performed. Results showed that CNN-LSTM model performed better with coefficients of determination ( R 2 ) of 0.965, 0.987, and 0.981. Additionally, the CNN-LSTM predicted freezing depth with 0.3%–13.1% error margins outperforming the LSTM, Aldrich’s, and Korean Ministry of Transport approaches. The proposed approach shows that deep-learning models better estimate the freezing depth of pavements than existing approaches.
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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".