TY - JOUR
T1 - Comparisons of Faulting-Based Pavement Performance Prediction Models
AU - Wang, Weina
AU - Qin, Yu
AU - Li, Xiaofei
AU - Wang, Di
AU - Chen, Huiqiang
N1 - Funding Information:
This paper is sponsored by National Natural Science Foundation of China [51508064, 51408083], China Postdoctoral Science Foundation [2014M562287], Chongqing Science and Technology Commission [cstc2014jcyjA30018, cstc2016jcyjA0128], Department of Human Resource and Social Security of Chongqing [Xm2014094], and State and Local Engineering Laboratory for Civil Engineering Material of Chongqing Jiaotong University [LHSYS-2016-01].
Publisher Copyright:
© 2017 Weina Wang et al.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85030775976&partnerID=8YFLogxK
U2 - 10.1155/2017/6845215
DO - 10.1155/2017/6845215
M3 - Article
AN - SCOPUS:85030775976
SN - 1687-8434
VL - 2017
JO - Advances in Materials Science and Engineering
JF - Advances in Materials Science and Engineering
M1 - 6845215
ER -