A meta-learning approach for predicting asphalt pavement deflection basin area

Abstract

To address the urgent need for accurate pavement performance modeling in pavement design, this study proposes a meta-learning-based few-shot learning method for predicting the Deflection Basin Area (DBA) of asphalt pavements. The method utilizes features such as pavement temperature and load pressure, and applies cyclic DBA data from various pavement types subjected to different pressures. The objective is to predict the trend of DBA changes over cycles at a specific pressure. By leveraging pre-training on diverse pavement datasets, the proposed meta-learning model reduces the training data required for target pavement DBA prediction, enabling better generalization to the target pavement. This approach enhances DBA prediction accuracy even with a small sample size. Compared to traditional machine learning and pre-training methods using data from a single pavement type, the proposed method achieves a Mean Square Error of 13.26 and a Mean Absolute Error of 2.85, demonstrating superior performance. Furthermore, it achieves high prediction accuracy with fewer iterations. Overall, the proposed method effectively predicts DBA across various pavement structures with a few data.

Keywords

Meta-learning / deflection basin area / pre-training / few-shot learning / time series prediction