Open AccessEnhancing pavement performance prediction models for the Illinois Tollway System
Author(s) -
Laxmikanth Premkumar,
William R. Vavrik
Publication year - 2016
Publication title -
international journal of pavement research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.888
H-Index - 26
eISSN - 1997-1400
pISSN - 1996-6814
DOI - 10.1016/j.ijprt.2015.12.002
Subject(s) - pavement management , pavement engineering , engineering , asphalt pavement , transport engineering , driver rehabilitation , predictive modelling , civil engineering , state highway , toll , forensic engineering , computer science , rehabilitation , asphalt , genetics , cartography , neuroscience , machine learning , biology , geography
Accurate pavement performance prediction represents an important role in prioritizing future maintenance and rehabilitation needs, and predicting future pavement condition in a pavement management system. The Illinois State Toll Highway Authority (Tollway) with over 2000 lane miles of pavement utilizes the condition rating survey (CRS) methodology to rate pavement performance. Pavement performance models developed in the past for the Illinois Department of Transportation (IDOT) are used by the Tollway to predict the future condition of its network. The model projects future CRS ratings based on pavement type, thickness, traffic, pavement age and current CRS rating. However, with time and inclusion of newer pavement types there was a need to calibrate the existing pavement performance models, as well as, develop models for newer pavement types.This study presents the results of calibrating the existing models, and developing new models for the various pavement types in the Illinois Tollway network. The predicted future condition of the pavements is used in estimating its remaining service life to failure, which is of immediate use in recommending future maintenance and rehabilitation requirements for the network