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Maintenance Cost Estimation in PSCI Girder Bridges Using Updating Probabilistic Deterioration Model

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  • Jin Hyuk Lee

    (School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea)

  • Yangrok Choi

    (School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea)

  • Hojune Ann

    (Department of Civil, Construction and Environmental Engineering, NC State University, Raleigh, NC 27606, USA)

  • Sung Yeol Jin

    (School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea)

  • Seung-Jung Lee

    (Advanced Railroad Civil Engineering Division, Korea Railroad Research Institute, 176 Cheoldobangmulgwan-ro, Uiwang-si, Gyeonggi-do 16105, Korea)

  • Jung Sik Kong

    (School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea)

Abstract

A deterioration model plays an important role to predict the valid total maintenance cost for sustainable maintenance of bridges. In the current state-of-the-art, the deterioration model has regression parameters as a probabilistic process by an initially determined mean and standard deviation, called an existing model. However, the existing model has difficulty to predict maintenance costs accurately, because it cannot reflect an information based on structural damage at an operational stage. In this research, updating the probabilistic deterioration model is presented for the prediction of pre-stressed concrete I-type (PSCI) girder bridges using a particle filtering technique which is an advanced Bayesian updating method based on big data analysis. The method enables predicting maintenance cost fitted in the current structural status, which includes the recent information by inspection with bridge-monitoring. The method is adapted in the Mokdo Bridge which is currently being used for evaluating the efficiency of maintenance cost by effects on updated probabilistic values with two different scenarios. As the result, it is shown that the proposed method is effective in predicting maintenance costs.

Suggested Citation

  • Jin Hyuk Lee & Yangrok Choi & Hojune Ann & Sung Yeol Jin & Seung-Jung Lee & Jung Sik Kong, 2019. "Maintenance Cost Estimation in PSCI Girder Bridges Using Updating Probabilistic Deterioration Model," Sustainability, MDPI, vol. 11(23), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6593-:d:289765
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    References listed on IDEAS

    as
    1. Chul-Yong Lee & Min-Kyu Lee, 2017. "Demand Forecasting in the Early Stage of the Technology’s Life Cycle Using a Bayesian Update," Sustainability, MDPI, vol. 9(8), pages 1-15, August.
    2. Chul-Yong Lee & Sung-Yoon Huh, 2017. "Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors," Sustainability, MDPI, vol. 9(2), pages 1-15, January.
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    Cited by:

    1. Daeseok Han & Jin-Hyuk Lee & Ki-Tae Park, 2022. "Deterioration Models for Bridge Pavement Materials for a Life Cycle Cost Analysis," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    2. Youngjin Choi & Jinhyuk Lee & Jungsik Kong, 2020. "Performance Degradation Model for Concrete Deck of Bridge Using Pseudo-LSTM," Sustainability, MDPI, vol. 12(9), pages 1-19, May.

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