Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/7721
Title: Stochastic-based pavement rehabilitation model at the network level with prediction uncertainty considerations
Authors: Abaza, Khaled A. 
Keywords: Pavements - Performance;Pavements - Design and construction;Markov processes;Pavement networks;Pavements - Service life;Pavements - Maintenance and repair - Management;Markov chains
Issue Date: 13-Feb-2023
Publisher: Taylor and Francis
Source: Khaled A. Abaza (2023): Stochastic-based pavement rehabilitation model at the network level with prediction uncertainty considerations, Road Materials and Pavement Design, DOI: 10.1080/14680629.2022.2164330
Journal: Road Materials and Pavement Design 
Abstract: An optimum network-level pavement rehabilitation model has been developed for generating a long term rehabilitation schedule comprised of a specified number of annual rehabilitation cycles. The optimum model deploys the discrete-time Markov model to predict the performances of both original and rehabilitated pavements wherein the pavement improvement rates are incorporated into the transition probability matrix. The model implements continuous cyclic improvements in the long-term performance curve compared to the traditionally assumed vertical improvements. A Markov chain with (m) condition states can incorporate (m-1) rehabilitation treatments with an expected improvement outcome being the upgrade to condition state (1), the state with best pavement condition. The optimum model deploys an effective decision-making policy that maximises the long-term performance while minimising rehabilitation cost. The optimum model can be solved using exhaustive search with functional evaluations. The sample results obtained for a pavement network comprised of (12) highways indicated the efficiency of proposed model in yielding practical long-term rehabilitation schedules. The sample results also provided the minimal annual budget required to progressively remove the ‘very poor’ pavements that greatly affect the life-cycle cost. Furthermore, investigation of prediction uncertainty resulted in a relatively mild impact when considering lower and upper-limit performance values using 95% confidence level.
URI: http://hdl.handle.net/20.500.11889/7721
DOI: 10.1080/14680629.2022.2164330
Appears in Collections:Fulltext Publications

Files in This Item:
File Description SizeFormat
RMPD-6.pdf2 MBAdobe PDFView/Open
Show full item record

Page view(s)

53
checked on Jun 27, 2024

Download(s)

32
checked on Jun 27, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.