Journal Home Online First Current Issue Archive For Authors Journal Information 中文版

Frontiers of Structural and Civil Engineering >> 2020, Volume 14, Issue 2 doi: 10.1007/s11709-020-0609-4

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

. College of Civil Engineering, Hunan University, Changsha 410082, China.. Department of Civil and Environmental Engineering, Michigan Technological University, Houghton, MI 49931, USA

Accepted: 2020-04-09 Available online: 2020-04-09

Next Previous


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.

Related Research