01739525

Component

https://doi.org/10.1177/0361198120915889

In their pavement management decision-making processes, U.S. state highway agencies are required to develop performance-based approaches by the Moving Ahead for Progress in the 21st Century (MAP-21) federal transportation legislation. One of the performance-based approaches to facilitate pavement management decision-making processes is the use of remaining service life (RSL) models. In this study, a detailed step-by-step methodology for the development of pavement performance and RSL prediction models for flexible and composite (asphalt concrete [AC] over jointed plain concrete pavement [JPCP]) pavement systems in Iowa is described. To develop such RSL models, pavement performance models based on statistics and artificial intelligence (AI) techniques were initially developed. While statistically defined pavement performance models were found to be accurate in predicting pavement performance at project level, AI-based pavement performance models were found to be successful in predicting pavement performance in network level analysis. Network level pavement performance models using both statistics and AI-based approaches were also developed to evaluate the relative success of these two models for network level pavement performance modeling. As part of this study, in the development of pavement RSL prediction models, automation tools for future pavement performance predictions were developed and used along with the threshold limits for various pavement performance indicators specified by the Federal Highway Administration. These RSL models will help engineers in decision-making processes at both network and project levels and for different types of pavement management business decisions.

The contents of this paper reflect the views of the authors who are responsible for the facts and accuracy of the data presented. The contents do not necessarily reflect the official views and policies of the Iowa Highway Research Board, Iowa County Engineers Service Bureau, or Iowa State University. This paper does not constitute a standard, specification, or regulation. © National Academy of Sciences: Transportation Research Board 2020.

English

pp 448-460

2020-10

Transportation Research Record: Journal of the Transportation Research Board

Web

References (21)

Artificial intelligence; Composite pavements; Decision making; Flexible pavements; Mathematical models; Pavement management systems; Pavement performance; Service life; Statistics

Moving Ahead for Progress in the 21st Century Act (MAP-21)

Highways; Pavements

TRIS, TRB, ATRI

May 13 2020 3:05PM