Mechanistic-empirical methodology for the selection of cost-effective rehabilitation strategy for flexible pavements Academic Article uri icon


  • © 2016 Informa UK Limited, trading as Taylor & Francis Group. A well-planned rehabilitation approach helps agencies to optimise the allocation of annual investment in pavement rehabilitation programs. Currently, many agencies are struggling with the selection of an optimal time-based and cost-effective rehabilitation solution to address the long-term needs of pavements. This study offers the use of a mechanistic-empirical methodology to develop a series of time-based rehabilitation strategies for high traffic volume flexible pavements located in Oklahoma. Six different pavement family groups are identified in the state, and comprehensive evaluation of existing pavements are conducted through analysis of falling weight deflectometer data and performance measures available in Oklahoma Pavement Management System database. The inadequacy of performance measures to fully characterise the condition of existing pavements are indicated, and damage factor determined from FWD data are suggested as trigger factor to select rehabilitation candidates. Three levels of rehabilitation activities including light, medium and heavy are considered as potential alternatives for rehabilitation candidates. A mechanistic-empirical methodology is employed to obtain an estimate of the performance of rehabilitation and extension in service lives of pavements. Also, an assessment output matrix is developed, which can be served as a supplemental tool to help the decision-makers in the highway agency with the rehabilitation related decision-making process. Cost-effectiveness of rehabilitation alternatives is determined through life cycle cost analysis, and three time-based renewal solutions are developed for pavement family groups that are in need of rehabilitation.

author list (cited authors)

  • Nobakht, M., Sakhaeifar, M. S., Newcomb, D., & Underwood, S.

citation count

  • 11

publication date

  • July 2016