Reliability-based optimization of maintenance and repair for a system of bridges

C. A. Robelin, S. M. Madanat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This research addresses the determination of optimal bridge maintenance and replacement policies for a system of heterogeneous facilities. The problem of optimizing maintenance and replacement policies at the system level is formulated in a reliability-based framework, based on policies that are optimal at the facility level. The facility-level policies are determined using a finite-state, finite horizon Markov decision process in which the state of the Markov chain contains information on the history of maintenance and deterioration. This article provides formal proofs of the optimality of the system-level results in the continuous case, and a numerical study shows that the results obtained in the discrete-case implementation of the solution are valid approximations of the continuous-case results. The computational efficiency of the system-level solution makes the formulation suitable for systems of realistic sizes.

Original languageEnglish (US)
Title of host publicationApplications of Statistics and Probability in Civil Engineering -Proceedings of the 11th International Conference on Applications of Statistics and Probability in Civil Engineering
PublisherTaylor and Francis Inc.
Pages226-234
Number of pages9
ISBN (Print)9780415669863
DOIs
StatePublished - 2011
Event11th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP - Zurich, Switzerland
Duration: Aug 1 2011Aug 4 2011

Publication series

NameApplications of Statistics and Probability in Civil Engineering -Proceedings of the 11th International Conference on Applications of Statistics and Probability in Civil Engineering

Other

Other11th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP
CountrySwitzerland
CityZurich
Period8/1/118/4/11

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Statistics and Probability

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