An Inversion Algorithm for the Dynamic Modulus of Concrete Pavement Structures Based on a Convolutional Neural Network

• Published in: Applied sciences Vol. 13; no. 2; p. 1192
• Main Authors: Chen, Gongfa, Chen, Xuedi, Yang, Linqing, Han, Zejun, Bassir, David
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2023; Multidisciplinary digital publishing institute (MDPI)
• Series: Advances in Non-Destructive Testing Methods
• Subjects: Algorithms; Concrete pavements; convolution neural network; Deflectometers; dynamic modulus; Dynamic response; Dynamic stability; Efficiency; Environmental Sciences; falling weight deflectometer; Inversion; inversion algorithm; Load; Mathematics; Mechanical properties; Multilayers; Neural networks; Nondestructive testing; Numerical stability; Optimization; Parameter identification; pavement structure; Propagation; Software; Spectral element method; Statistics; Thickness; Workloads; falling weight deflectometer; inversion algorithm; dynamic modulus; pavement structure; convolution neural network
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13021192

Based on the spectral element method (SEM) and a convolutional neural network (CNN), an inversion algorithm for the dynamic modulus of concrete pavement structures is proposed in this paper. In order to evaluate the service performance of pavement structures more systematically and accurately via the existing testing techniques using a falling weight deflectometer (FWD), it is necessary to obtain accurate dynamic modulus parameters of the structures. In this work, an inversion algorithm for predicting the dynamic modulus is established by using a CNN which is trained with the dynamic response samples of a multi-layered concrete pavement structure obtained through SEM. The gradient descent method is used to adjust the weight parameters in the network layer by layer in reverse. As a result, the accuracy of the CNN can be improved via iterative training. With the proposed algorithm, more accurate results of the dynamic modulus of pavement structures are obtained. The accuracy and numerical stability of the proposed algorithm are verified by several numerical examples. The dynamic modulus and thickness of concrete pavement structure layers can be accurately predicted by the CNN trained with a certain number of training samples based on the displacement curve of the deflection basin from the falling weight deflectometer. The proposed method can provide a reliable testing tool for the FWD technique of pavement structures.