Comparisons of Faulting-Based Pavement Performance Prediction Models

Weina Wang,Huiqiang Chen,Yu Qin,Xiaofei Li,Di Wang

doi:10.1155/2017/6845215

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.

Highlights

Transverse joint faulting is the common type of distress for jointed concrete pavement, which has negative effect on driving safety and resulting costly rehabilitation [1]
Many researchers have been focusing on the developing of pavement performance prediction and improving its accuracy
The eight important factors that affected faulting are used in multivariate nonlinear regression (MNLR) model and artificial neural network (ANN) model

Summary

Introduction

Transverse joint faulting is the common type of distress for jointed concrete pavement, which has negative effect on driving safety and resulting costly rehabilitation [1]. Three different models including MNLR model, ANN model, and MC model are briefly introduced. These models are quantitatively evaluated and compared using a set of concrete pavement survey faulting data with varying design features, traffic, and climate data. These survey faulting data are taken for evaluating the performance of the three models. The results of these prediction models are presented. Suggestions for future research work are proposed by incorporating the advantages and disadvantages of different models

Literature Survey

Data Preparation

Models Used for Comparison

Results

Section 1 Section 2 Section 3 Section 4 Section 5

Conclusions