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.

Based on the earthquakes that occurred recently in Gyeongju and Pohang provinces, the Korea Peninsula needs to be prepared for a strong earthquake that might occur in the future. In this study, a strain tendency model based on the response surface method was used to analyze buried straight pipelines. The strains of the buried pipelines were computed through structural dynamics analysis, considering the section properties and ground types. In the case of the buried straight pipelines, this strain tendency model provided the necessary information for setting the strain-based design and a guideline for predicting post-earthquake damages in the Korea Peninsula.

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