Comparisons of Faulting-Based Pavement Performance Prediction Models

Author:

Wang Weina1ORCID,Qin Yu2ORCID,Li Xiaofei3,Wang Di4,Chen Huiqiang3

Affiliation:

1. State and Local Engineering Laboratory for Civil Engineering Material, School of Civil Engineering, Chongqing Jiaotong University, Xuefu Avenue No. 66, Nan’an District, Chongqing, China

2. CREEC (Chongqing) Survey, Design and Research Co. Ltd., Kunlun Avenue No. 46, Liangjiang New Area, Chongqing, China

3. School of Civil Engineering, Chongqing Jiaotong University, Xuefu Avenue No. 66, Nan’an District, Chongqing, China

4. Pavement Engineering Centre, Technical University of Braunschweig, Raum 104, Beethovenstraße 51 b, Braunschweig, Germany

Abstract

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.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

General Engineering,General Materials Science

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