Introducing an Experimental Model of Asphalt Shear Strength Using Designed Jaws and Presentation of Shear Strength Prediction Model by Genetic Programming Method
Bibliographic record
Abstract
The main material used in the construction of roads is asphalt. Therefore, the recognition of asphalt’s mechanical aspects is very important. One of the important features of asphalt is its shear strength, which should be measured accurately. However, the methods that have been presented to measure this important factor of asphalt always encounter weaknesses. So, it is necessary to find a suitable method to determine the shear strength of asphalt with more accurate results and high compatibility with reality. In this regard, the purpose of the present research was to design jaws in order to measure the shear strength in the direction and opposite direction of the traffic path and provide a model to predict shear strength using Marshall stability resulting from invented jaws. In order to examine the accuracy of the designed jaw in this study, two different types of asphalt, Binder 0–25 and Topeka 0–19 grading, were used. For this purpose, Marshall stability and shear strength tests in the direction and opposite direction of the Marshall were conducted with 12 repetitions on these samples. Also, the genetic programming (GP) evolutionary algorithm was applied in this study to provide a prediction model of shear strength. The results of this study indicated that there was a significant relationship between the Marshall stability and the shear strength in the direction and opposite direction of the Marshall applying the invented jaws in both asphalt types, and the coefficient of determination (R2) for the Binder and Topeka were 0.93 and 0.97 in the Marshall’s direction and 0.96 and 0.95 for the Marshall’s opposite direction, respectively. Also, the results of the GP method indicated that the relationships between predicted and actual values of shear strength for Binder and Topeka asphalt types were appropriately described by R2 of 99.47% and 99.21% with RMSE of 8.0177 and 5.0143 in the traffic direction, and R2 of 97.45% and 98.08% with RMSE of 1.2684 and 0.7035 in the traffic opposite direction, respectively. Therefore, GP provided a more suitable fit of all experimental data for both Binder and Topeka asphalts, and it can be said that with the help of new designed jaws, the shear strength in the direction and opposite direction of the Marshall can be estimated with high accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".