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Advances in Materials Science and Engineering
Volume 2017 (2017), Article ID 6845215, 9 pages
Research Article

Comparisons of Faulting-Based Pavement Performance Prediction Models

1State and Local Engineering Laboratory for Civil Engineering Material, School of Civil Engineering, Chongqing Jiaotong University, Xuefu Avenue No. 66, Nan’an District, Chongqing, China
2CREEC (Chongqing) Survey, Design and Research Co. Ltd., Kunlun Avenue No. 46, Liangjiang New Area, Chongqing, China
3School of Civil Engineering, Chongqing Jiaotong University, Xuefu Avenue No. 66, Nan’an District, Chongqing, China
4Pavement Engineering Centre, Technical University of Braunschweig, Raum 104, Beethovenstraße 51 b, Braunschweig, Germany

Correspondence should be addressed to Yu Qin

Received 15 April 2017; Revised 2 July 2017; Accepted 6 August 2017; Published 18 September 2017

Academic Editor: Hainian Wang

Copyright © 2017 Weina Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR) model, artificial neural network (ANN) model, and Markov Chain (MC) model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.