Abstract: In order to improve the accuracy and generalization ability of asphalt mixture fatigue life prediction, this study introduces the meta-learning method, which aims to solve the problems of poor adaptability and strong data dependence of the traditional prediction model under complex working conditions. In this study, a prediction framework based on the Model-Agnostic Meta-Learning (MAML) algorithm is constructed, which realizes the fast and accurate prediction of asphalt mixture fatigue life under multi-task conditions through feature extraction, meta-knowledge learning, and a fast adaptive mechanism. The experiments were conducted using multi-class mixture data and compared with linear regression and BP neural network methods under the MATLAB platform. The results show that the meta-learning model achieves a prediction accuracy of 0.98 within 500 iterations, which is significantly better than that of the BP neural network (0.89) and linear regression (0.84), and the prediction error is controlled to be between 40 and 60 under typical working conditions, while the traditional method has an error of up to 150. Further analysis shows that the meta-learning method has a faster convergence rate (the convergence index is 0.9 for 100 iterations) and a higher convergence index of 0.9 for 100 iterations. 0.9) with higher robustness. In conclusion, the meta-learning-based prediction method shows excellent performance in fatigue life modeling, which is suitable for rapid application in real-world engineering with diverse materials and loading environments.

Longmeng Tan and Krzysztof Kowalski. “Meta-Learning Prediction Framework for Asphalt Mixtures Fatigue Life Modeling”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.10 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161058

@article{Tan2025,
title = {Meta-Learning Prediction Framework for Asphalt Mixtures Fatigue Life Modeling},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161058},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161058},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {10},
author = {Longmeng Tan and Krzysztof Kowalski}
}