A Backpropagation Neural Network-based Flexural-tensile Strength Prediction Model for Asphalt Mixture in Cold Regions under Cyclic Thermal Stress
Bibliographic record
Abstract
This paper attempts to disclose how the varying cold season temperature affects the performance of asphalt mixture i=n cold regions, and create a model to predict the flexuraltensile strength under cyclic thermal stress. For this purpose, the author investigated the influencing factors of asphalt mixture performance in cold regions, such as temperature level and variation in temperature difference, and employed the backpropagation neural network (BPNN) to learn, train and verify 120 samples of SBS AC-13 database. On this basis, a BPNN-based prediction model was established for the flexural-tensile strength of asphalt mixture under cyclic thermal stress. Next, the predicted results were compared with the actual results through regression analysis. The comparison shows that our model output a correlation coefficient (R) of 0.9706, an evidence for good prediction accuracy. This means our model can effectively predict the flexural-tensile strengths of asphalt mixture under cyclic thermal stress in cold regions. The research findings provide a good reference for similar studies on asphalt mixture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".