Predicting the Recovery and Nonrecoverable Compliance Behaviour of Asphalt Binders Using Artificial Neural Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Additives are widely used to enhance the rheological and performance properties of asphalt binder to satisfy the demands of extreme loading and climatic conditions. Meanwhile, adding to the complexity of asphalt binder behaviour that requires more time, effort, and material resources during laboratory work. The purpose of this research was to use Artificial Neural Networks (ANNs) to predict the recovery (R) and nonrecoverable compliance (Jnr) behaviour of asphalt binder based on mechanical test parameters and rheological properties of asphalt binder. A comprehensive experimental database consisting of the results of the frequency sweep and Multiple Stress Creep Recovery (MSCR) test using a dynamic shear rheometer (DSR) at five test temperatures (46 ∘C, 52 ∘C, 58 ∘C, 64 ∘C, and 70 ∘C). Prediction models for R and Jnr of asphalt binder modified with different contents of fly ash, fly ash-based geopolymer, glass powder/fly ash-based geopolymer, and styrene–butadiene styrene (SBS) were developed. The ANNs model was developed using five input parameters (temperature, frequency, storage modulus, loss modulus, and viscosity) and one hidden layer with five neurons. The results pointed out that the hybrid and 4%SBS binders achieved the highest ability to resist extremely heavy traffic and to recover the deformation with 60.1% and 85.5% at 46 ∘C, respectively, compared with the other modified asphalt binders. Excellent R-values for the total data set of 0.937, 0.997, 0.985, and 0.987 for Jnr3.2 of unaged binder, Jnr3.2 of aged binder, R3.2 of unaged binder, and R3.2 of aged binder, respectively. Therefore, the ANNs model is appropriate tool to predict the R3.2 and Jnr3.2 using unaged or aged binders at different temperatures.
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.
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 it