A Bilevel Optimization Model for Large Scale Highway Infrastructure Maintenance Inspection and Scheduling Following a Seismic Event

  • Manoj K. Jha
  • Konstantinos Kepaptsoglou
  • Matthew Karlaftis
  • Gautham Anand Kumar Karri
Part of the Computational Methods in Applied Sciences book series (COMPUTMETHODS, volume 21)


Major highway infrastructure elements, in need of post-earthquake maintenance for improved highway life-cycle, motorist’s guidance, and safety, include bridges, pavements, interchanges, and tunnels. In addition, there are numerous, minor infrastructure elements vital to motorist’s guidance and safety, such as overhead and roadside appurtenances, including signs, guardrails, and luminaries. The maintenance and upkeep of all infrastructure components is crucial for mobility, driver safety and guidance, and overall efficient functioning of a highway system. Successive research efforts have been made in developing optimal Maintenance Repair and Rehabilitation (MR&R) strategies for major infrastructure components, such as pavements and bridges. However, there has been limited studies reported on Maintenance Inspection and Scheduling (MI&S) of minor infrastructure elements, such as signs, guardrails, and luminaries, particularly after the occurrence of seismic events. Typically, a field inspection of such elements is carried out at fixed time intervals to determine their condition, which is used to develop optimal MR&R plan over a given planning horizon, which is not. In this paper we introduce a bilevel model for developing an optimal MI&S plan for large-scale highway infrastructure elements, following a seismic event. At the lower level a set of optimal inspection routes is obtained, which is used at the upper level to obtain optimal maintenance schedule over a given planning horizon. Separate algorithms are developed for solving the lower and upper level optimization models, including a genetic algorithm for solving the lower level model and a customized heuristic for solving the upper level model. A numerical example using a real highway network from Maryland is presented. Finally, directions for future work are discussed.


Planning Horizon Travel Salesman Problem Bilevel Programming Bilevel Optimization Highway Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was jointly carried out at the Center for Advanced Transportation and Infrastructure Engineering Research, Morgan State University and the National Technical University of Greece, Athens. The authors wish to acknowledge Dr. Jawad Abdullah for his assistance with some of the work.


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Manoj K. Jha
    • 1
  • Konstantinos Kepaptsoglou
    • 2
  • Matthew Karlaftis
    • 2
  • Gautham Anand Kumar Karri
    • 1
  1. 1.Department of Civil EngineeringMorgan State UniversityBaltimoreUSA
  2. 2.School of Civil EngineeringNational Technical University of AthensAthensGreece

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