Prediction of Interface Shear Stiffness Modulus of Asphalt Pavement using Bagging Ensemble-based Hybrid Machine Learning Model

Interface shear stiffness modulus ( K ) is one of the important bonding properties of layers. It is also used to evaluate interface shear strength between asphalt layers of asphalt pavement. Direct determination of K parameter in field or laboratory requires time, cost and special equipment. In this article, K has been estimated based on three interlayer shear strength affecting factors namely maximum size of the asphalt concrete aggregate ( D max ), normal pressure and temperature using Machine Learning (ML) methods such as Multilayer Perception Neural Network, Bagging Random Forest (Bagging-RF), and Bagging Reduced Error Pruning Tree (Bagging-REPT). The ML models for the prediction of shear strength were built based on the laboratory shear tests results of 180 double-layer asphalt samples. The data was divided randomly into a ratio of 70/30 to train and test model, respectively. Standard statistical measures were used to evaluate and validate the models’ performance. All the developed models performed well in correctly predicting K value of AC, but performance of the Bagging-RF model is the best as it is giving Correlation Coefficient ( R ) value 0.88 between estimated value and determined value. The proposed ML predictive models will reduce the field and laboratory experimental efforts and increase the efficiency in estimating the K parameter for the safe designing, construction and maintenance of asphalt concrete pavements.