Dynamic Inversion Analysis of Structural Layer Modulus of Semirigid Base Pavement considering the Influence of Temperature and Humidity

This paper is aimed to solve the overlearning problem of the neural network algorithm used to calculate the asphalt concrete pavement structural modulus in reverse. The firefly algorithm was adapted to optimize the selection of support vector machine (SVM) parameters. Based on the optimized SVM mode...

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Bibliographic Details
Published in:Advances in civil engineering 2020, Vol.2020 (2020), p.1-12
Main Authors: Hao, Meimei, Li, Xiaolong, Zhong, Yanhui, Zhang, Xu, Zhang, Bei, Liu, Jinbo
Format: Article
Language:English
Subjects:
Algorithms
Asphalt
Asphalt pavements
Civil engineering
Concrete pavements
Dynamic inversion
Genetic algorithms
Heuristic methods
Highway construction
Humidity
Iterative methods
Neural networks
Support vector machines
Viscoelasticity
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Summary:This paper is aimed to solve the overlearning problem of the neural network algorithm used to calculate the asphalt concrete pavement structural modulus in reverse. The firefly algorithm was adapted to optimize the selection of support vector machine (SVM) parameters. Based on the optimized SVM model, a new method for dynamic inversion of the semirigid base asphalt concrete pavement structural layer modulus was presented. The results show that the absolute value of relative error of each layer modulus is not more than 3.73% by using the proposed method. Then, the influences of temperature and humidity on the inversion modulus of semirigid base asphalt concrete pavement in the seasonal frozen area were analyzed, and the correction formula of the inversion modulus was established. The paper is of practical significance for improving the safety performance of semirigid base pavement in the seasonal frozen area in China.
ISSN:1687-8086
1687-8094
DOI:10.1155/2020/8899888