Neural Network for Rapid Depth Evaluation of Shallow Cracks in Asphalt Pavements

Rapid and nondestructive evaluation of pavement crack depths is a major challenge in pavement maintenance and rehabilitation. This paper presents a computer-based methodology with which one can estimate the actual depths of shallow, surface-initiated fatigue cracks in asphalt pavements based on rapid measurement of their surface characteristics. It is shown that the complex overall relationship among crack depths, surface geometrical properties of cracks, pavement properties, and traffic characteristics can be learned effectively by a neural network (NN). The learning task is facilitated by a database that includes relevant traffic and pavement characteristics of Florida's state highway network. In addition, the specific data used for the NN model development also contained laser-scanned microscopic surface geometrical properties of cracks in 95 pavement sections and pavement core samples scattered within 5 Florida counties. Relatively advanced training algorithms were investigated in addition to the Standard Backpropagation algorithm to determine optimal NN architecture. In terms of optimizing the NN training process, the "early stopping method" was effective. The crack depth evaluation model was validated based on an unused portion of the database and fresh core samples. Results indicate the promise of NN usage in nondestructive estimation of shallow crack depths based on crack-surface geometry and pavement and traffic characteristics.