01622436

Component

The decision making process for allocating and scheduling maintenance and rehabilitation activities within a road network depends on reliable and effective methods to effectively predict pavement’s performance over its service life. Recent studies have identified Probabilistic Neural Networks Model (PNNM) and Ordered-Probit Models (OPM) as reliable tools for predicting pavement behaviors. The objective of this study is to compare the prediction capabilities of each model in forecasting the pavement distress rate. Both models were developed using traffic data as the independent variables and pavement’s distress rate as the dependent variable. Their performance were compared from the estimation results using a dataset of 5,888 observations collected in Northern New Mexico. The findings of this study suggest that PNNM can be more accurate for predicting distress rate than OPM and can be used as input for a variety of practices in pavement management including investment, design, and rehabilitation policy. In addition, the models suggest that variables such as peak hour volume and single heavy commercial average volume are significant predictors of the distress rate for a pavement. Although both models have been proven to be effective and reliable in predicting pavement deterioration, there has not been a study comparing their performance. This study provides a better understanding in their performance when compared to each other. The application of reliable predictive models, such as the PNNM, facilitates the decision-making process.

This paper was sponsored by TRB committee AFD10 Standing Committee on Pavement Management Systems. Alternate title: Comparing Artificial Neural Networks and Ordered-Probit Models for Forecasting Pavement Condition in New Mexico,

01618707

17-01037

English

15p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References; Tables

Decision making; Forecasting; Neural networks; Pavement distress; Pavement management systems; Pavement performance

Ordered probit models

New Mexico

Highways; Pavements

Transportation Research Board Annual Meeting 2017 Paper #17-01037

TRIS, TRB, ATRI

Dec 8 2016 10:17AM