Application of AI Techniques for Asphalt Concrete Mix Production Optimization
Bibliographic record
Abstract
In present-day conditions of road infrastructure development, ensuring the high quality of asphalt concrete mixes contributes to the durability and reliability of road pavements.This article investigates the application of artificial intelligence techniques to analyze asphalt quality aimed at optimizing production and improving the reliability of road pavements.This study introduces a pioneering approach to asphalt concrete mix quality enhancement using artificial intelligence (AI) techniques, specifically artificial neural networks (ANN) and least-squares support vector machine (LS-SVM).The application of these methods allows for carrying out efficient analysis of data, reflecting asphalt quality, predicting asphalt characteristics, and optimizing production processes.The authors conducted experiments using real asphalt properties, which were used to train and set ANN and LS-SVM models.The obtained results were compared with existing methods of asphalt quality analysis.The conducted analysis confirmed the effectiveness of using ANN and SVM to analyze asphalt quality.This approach provides an opportunity for accurate prediction of asphalt performance characteristics and production process optimization, contributing to the improvement of the durability and reliability of road pavements.The obtained results have practical significance for engineers and specialists in the field of road infrastructure construction and maintenance.The results of the study validate the superiority of AI-driven models in achieving precise and reliable asphalt mix designs, marking a considerable advancement over traditional methods.
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 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 | 0.001 | 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) | 0.001 | 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".