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A quadratic hierarchical Bayesian dynamic prediction model for infrastructure maintenance

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Abstract

The main objective of this study is the development of a correlation model in dynamic Bayesian belief networks (DBBNs) followed by an inverse economic analysis. This is based on a quadratic hierarchical Bayesian inference prediction method using Markov chain Monte Carlo simulations. The developed model is implemented to predict the future degradation and maintenance budget for a suspension bridge system. Bayesian inference is applied to find the posterior probability density function of the source parameters (damage indices and serviceability), given 10 years maintenance data. The simulated risk prediction under decreased serviceability conditions gives posterior distributions based on a prior distribution and likelihood data updated from annual maintenance tasks. Compared with a conventional linear prediction model, the proposed quadratic model provides highly improved convergence and closeness to the measured data. Finally, the developed inverse DBBN analysis method allows forecasts of future performance and the financial management of complex infrastructures by providing the sensitivity of serviceability and risky factors to the maintenance budgets of structural components and the overall system.

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Acknowledgement

This work was supported by the Daejin University Special Research Grants in 2010. The authors hereby express their sincere appreciation.

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Authors and Affiliations

  1. Department of Civil Engineering, Daejin University, Sundan 11-1, Pocheon, 487-711, Korea

    Taejun Cho & Seong-Soo Kim

  2. Department of Architectural Engineering, Hanbat National University, Daejeon, 305-719, Korea

    Tae-Soo Kim

Authors
  1. Taejun Cho
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  2. Seong-Soo Kim
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  3. Tae-Soo Kim
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Correspondence to Tae-Soo Kim.

Appendix

Appendix

Table 4 Part of predicted seven stochastic parameters for 160 months
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Cho, T., Kim, SS. & Kim, TS. A quadratic hierarchical Bayesian dynamic prediction model for infrastructure maintenance. Nonlinear Dyn 76, 609–626 (2014). https://doi.org/10.1007/s11071-013-1155-6

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