Hybrid deep learning model for predicting failure properties of asphalt binder from fracture surface images

Abstract

Cracking impacts asphalt concrete durability primarily due to cohesive asphalt binder failures. The poker chip test has recently been introduced to better characterize the cracking potential of asphalt binders by fracturing a specimen in a realistic stress state to a thin binder film. However, broader adoption faces challenges due to high instrumentation costs for measuring load and displacement. This paper presents and validates a deep learning model that predicts ductility and tensile strength from posttest images of fractured binder surfaces, with potential extensions to simplified instrumentation. The hybrid model, named PCNet, integrates a custom lightweight convolutional neural network (CNN) developed to capture local features (e.g., edges, boundaries, contours) within fracture cavities with a Swin Transformer that models global contextual dependencies. A bidirectional cross-attention fusion module is designed to facilitate mutual information exchange between CNN and transformer branches. The fused features are then processed by a fully connected network (FCN) to predict indices derived from the test. The proposed model demonstrates high predictive accuracy across a range of binders and test configurations, achieving an $R^2$ of 0.966 and a mean absolute percentage error (MAPE) of 12.95% in predicting ductility, while also attaining an $R^2$ of 0.947 and a MAPE of 9.15% for strength, outperforming standalone models. Monte Carlo Dropout is also incorporated in the FCN to quantify prediction confidence. This cost-effective methodology provides insights into fracture propagation in soft viscoelastic media and contributes to the field of experimental mechanics. With further data collection, the model holds potential for broader implementation, directly linking fracture surface images and mixture or field-scale cracking behavior.