Maintenance cost estimation in PSCI girder bridges using updating probabilistic deterioration model

Jin Hyuk Lee, Yangrok Choi, Hojune Ann, Sung Yeol Jin, Seung Jung Lee, Jung Sik Kong

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Article number6593
JournalSustainability (Switzerland)
Volume11
Issue number23
DOIs
Publication statusPublished - 2019 Dec 1

Keywords

  • Bridge-monitoring
  • Deterioration model
  • Maintenance cost
  • Particle filtering
  • Sustainable maintenance

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Management, Monitoring, Policy and Law

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