Classification of Surface Pavement Cracks as Top-down, Bottom-up, and Cement-Treated Reflective Cracking Based on Deep Learning Methods

Abstract

The treatment and repair strategies of reflective and fatigue cracking that initiate at the pavement surface (i.e. top-down cracking) and at the bottom of the asphalt concrete layer (i.e. bottom-up cracking) are noticeably different. However, pavement engineers are facing difficulties in identifying these cracks in the field as they usually appear in visually identical patterns. The objective of this study was to develop Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) applications to differentiate and classify top-down, bottom-up, and cement-treated reflective cracking in in-service pavements using deep-learning models. The developed CNN model achieved an accuracy of 93.8% in the testing and 91% in the validation phases and the ANN model showed an overall accuracy of 92%. The ANN classification tool was developed based on variables related to pavement and crack characteristics including age, Average Daily Traffic , thickness of Asphalt Concrete layer, type of base, crack orientation and location.