Prediction of the hot asphalt mix properties using deep neural networks
Bibliographic record
Abstract
Abstract Background Marshall design process is the most common method used for estimating the Optimum asphalt content (OAC) and this process is called the asphalt mix design. However, this method is time-consuming, labor-intensive, and its results are subjected to variations. Results This paper employs artificial neural network (ANN) for the estimation of Marshall test parameters (OAC, Stability, Flow, Air voids, Voids in mineral aggregate) using the aggregate gradation as the input of the prediction process. Multiple ANNs are tested in order to optimize the NN hyperparameters and produce accurate predictions. Different activation functions, number of hidden layers, and number of neurons per hidden layer are tested and heatmaps are generated to compare the performance of every ANN. Results show that the optimum ANN hyperparameters change depending on the predicted parameter. Finally, the deep NN can predict the OAC, stability, flow, density, air voids, and voids in mineral aggregate with R values of 0.91, 0.8, 0.53, 0.65, 0.77, and 0.66. Conclusion The linear activation function is the most efficient activation function and generates more accurate results than the logistic and the hyperbolic tangent functions. Additionally, it is shown that the deep neural network approach represents a major innovative tool for the prediction of the asphalt mix properties as results of this approach outperforms results of the shallow ANN that consists of a single hidden layer which is the only approach used in the literature. Thus, the use of the deep ANN can be useful during the phase of the design of the asphalt mix process because of its ability to predict variables with high accuracy. For example, the ANN with 3 hidden layers and 16 neurons per layer with the linear activation function can predict the OAC with high accuracy ( R = 0.91), which can be helpful in the design process as the ANN can be employed for the prediction of the OAC of the asphalt mix.
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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 | 0.001 | 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".