Assessing artificial neural network performance for predicting interlayer conditions and layer modulus of multi-layered flexible pavement

Abstract

The objective of this study is to evaluate the performance of the artificial neural network (ANN) approach for predicting interlayer conditions and layer modulus of a multi-layered flexible pavement structure. To achieve this goal, two ANN based back-calculation models were proposed to predict the interlayer conditions and layer modulus of the pavement structure. The corresponding database built with ANSYS based finite element method computations for four types of a structure subjected to falling weight deflectometer load. In addition, two proposed ANN models were verified by comparing the results of ANN models with the results of PADAL and double multiple regression models. The measured pavement deflection basin data was used for the verifications. The comparing results concluded that there are no significant differences between the results estimated by ANN and double multiple regression models. PADAL modeling results were not accurate due to the inability to reflect the real pavement structure because pavement structure was not completely continuous. The prediction and verification results concluded that the proposed back-calculation model developed with ANN could be used to accurately predict layer modulus and interlayer conditions. In addition, the back-calculation model avoided the back-calculation errors by considering the interlayer condition, which was barely considered by former models reported in the published studies.

Keywords

asphalt pavement / interlayer conditions / finite element method / artificial neural network / back-calculation

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 51278188, 50808077, and 51778224) and the Project of Young Core Instructor Growth from Hunan Province. The first author also acknowledges the financial support from the China Scholarship Council (CSC) under No. 201606130003. The authors are sincerely grateful for their financial support. In addition, the manuscript has received the written quality improvement assistance from Michigan Tech Multiliteracies Center during the revisions. The views and findings of this study represent those of the authors and may not reflect those of NSFC, Hunan University, and CSC.

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