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Optimal Maintenance Models of Social Infrastructures Considering Natural Disasters

In: Reliability and Statistical Computing

Author

Listed:
  • Takumi Kishida

    (Tottori University)

  • Kodo Ito

    (Tottori University)

  • Higuchi Yoshiyuki

    (Fukushima University)

  • Toshio Nakagawa

    (Aichi Institute of Technology)

Abstract

Social infrastructures such as roads and bridges in Japan are to exceed 50 years after their constructions in 2023. These infrastructures have to be maintained under strict budgets of local governments because local governments maintain them and are facing serious financial difficulties by the downturn of local economies. Moreover, they have to be maintained under scarcity of skillful engineers because of the aging workforce. Comparing mechanical system maintenance, social infrastructure maintenances have some practical interesting characteristics such as maintenance time delay and wide variety of preventive maintenance costs. Maintenance policies with various kinds of factors such as maintenance periods, maintenance time delay, wide variety of preventive maintenance costs and degradation levels, and natural disaster distribution, have to be practically established. In this chapter, considering natural disasters, we form stochastically cumulative damage models and discuss their optimal policies theoretically and numerically. The expected cost rates are obtained and optimal preventive maintenance levels which minimize them are derived. Extended models with a natural disaster and its recovery based on these models could be proposed and be applied to actual social infrastructures.

Suggested Citation

  • Takumi Kishida & Kodo Ito & Higuchi Yoshiyuki & Toshio Nakagawa, 2020. "Optimal Maintenance Models of Social Infrastructures Considering Natural Disasters," Springer Series in Reliability Engineering, in: Hoang Pham (ed.), Reliability and Statistical Computing, pages 245-263, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-030-43412-0_15
    DOI: 10.1007/978-3-030-43412-0_15
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