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Performance Degradation Model for Concrete Deck of Bridge Using Pseudo-LSTM

Author

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  • Youngjin Choi

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

  • Jinhyuk Lee

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

  • Jungsik Kong

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

Abstract

The purpose of a bridge maintenance strategy is to make effective decisions by evaluating current performance and predicting future conditions of the bridge. The social cost because of the rapid increase in the number of decrepit bridges. The current bridge maintenance system relies on traditional man-power-based methods, which determine the bridge performance by employing a material deterioration model, and thus shows uncertainty in predicting the bridge performance. In this study, a new type of performance degradation model is developed using the actual concrete deck condition index (or grade) data of the general bridge inspection history database (1995–2017) on the national road bridge of the bridge management system in Korea. The developed model uses the long short-term memory algorithm, which is a type of recurrent neural network, as well as layer normalization and label smoothing to improve the applicability of basic data. This model can express the discrete historical degradation indices in continuous form according to the service life. In addition, it enables the prediction of bridge performance by using only basic information about new and existing bridges.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3848-:d:355458
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    References listed on IDEAS

    as
    1. 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.
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    Cited by:

    1. Youngjin Choi & Yangrok Choi & Jun-sang Cho & Dongwoo Kim & Jungsik Kong, 2023. "Utilization and Verification of Imaging Technology in Smart Bridge Inspection System: An Application Study," Sustainability, MDPI, vol. 15(2), pages 1-31, January.
    2. Xin Gao & Gengxin Duan & Chunguang Lan, 2021. "Bayesian Updates for an Extreme Value Distribution Model of Bridge Traffic Load Effect Based on SHM Data," Sustainability, MDPI, vol. 13(15), pages 1-15, August.
    3. Binqiang Guo & Renzhi Wang & Chen Lu & Weijian Shi & Qingfei Gao, 2021. "Numerical Investigation on the Dynamic Performance of Steel–Concrete Composite Continuous Rigid Bridges Subjected to Moving Vehicles," Sustainability, MDPI, vol. 13(24), pages 1-16, December.
    4. Roman Trach & Victor Moshynskyi & Denys Chernyshev & Oleksandr Borysyuk & Yuliia Trach & Pavlo Striletskyi & Volodymyr Tyvoniuk, 2022. "Modeling the Quantitative Assessment of the Condition of Bridge Components Made of Reinforced Concrete Using ANN," Sustainability, MDPI, vol. 14(23), pages 1-19, November.

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