A BP Neural Network Method for Grade Classification of Loose Damage in Semirigid Pavement Bases

This study aims to address the problem that loose damage of the pavement base course cannot currently be quantitatively identified, and thus the classification and recognition of the extent of looseness mainly rely on empirical judgments. Based on the finite-difference time-domain (FDTD) method, a backpropagation (BP) neural network identification method for loose damage of a semirigid base is presented. The FDTD method is used to simulate a semirigid base road model numerically with different degrees of looseness, and the eigenvalue parameters for recognition of the presence and extent of the looseness of the base layer are obtained. Then, a BP neural network identification method is used to classify and identify the loose damage of the base course. The results show that the classification and recognition of simulated electromagnetic waves have an accuracy of over 90%; the classification and recognition of radar data from an actual project have a recognition accuracy of over 80%. The good agreement between the classification and recognition results for the simulated data and measured data verifies the feasibility of the classification and recognition method, which can provide a new method for the use of ground-penetrating radar to detect loose damage and the extent of looseness in the base.