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

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 th...

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Vydané v:Applied sciences Ročník 13; číslo 2; s. 1192
Hlavní autori: Chen, Gongfa, Chen, Xuedi, Yang, Linqing, Han, Zejun, Bassir, David
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.01.2023
Multidisciplinary digital publishing institute (MDPI)
Edícia:Advances in Non-Destructive Testing Methods
Predmet:
Algorithms
convolution neural network
dynamic modulus
Efficiency
Environmental Sciences
falling weight deflectometer
inversion algorithm
Load
Mathematics
Mechanical properties
Neural networks
Nondestructive testing
Optimization
Parameter identification
pavement structure
Propagation
Software
Statistics
Workloads
falling weight deflectometer
inversion algorithm
dynamic modulus
pavement structure
convolution neural network
ISSN:2076-3417, 2076-3417
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app13021192