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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.
Youngjin Choi; Jinhyuk Lee; JungSik Kong. Performance Degradation Model for Concrete Deck of Bridge Using Pseudo-LSTM. Sustainability 2020, 12, 3848 .
AMA StyleYoungjin Choi, Jinhyuk Lee, JungSik Kong. Performance Degradation Model for Concrete Deck of Bridge Using Pseudo-LSTM. Sustainability. 2020; 12 (9):3848.
Chicago/Turabian StyleYoungjin Choi; Jinhyuk Lee; JungSik Kong. 2020. "Performance Degradation Model for Concrete Deck of Bridge Using Pseudo-LSTM." Sustainability 12, no. 9: 3848.
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.
Jin Hyuk Lee; Yangrok Choi; Hojune Ann; Sung Yeol Jin; Seung-Jung Lee; Jung Sik Kong. Maintenance Cost Estimation in PSCI Girder Bridges Using Updating Probabilistic Deterioration Model. Sustainability 2019, 11, 6593 .
AMA StyleJin Hyuk Lee, Yangrok Choi, Hojune Ann, Sung Yeol Jin, Seung-Jung Lee, Jung Sik Kong. Maintenance Cost Estimation in PSCI Girder Bridges Using Updating Probabilistic Deterioration Model. Sustainability. 2019; 11 (23):6593.
Chicago/Turabian StyleJin 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 11, no. 23: 6593.
For the last ten years, the number of cases of large-scale fires which occur on bridges, tunnels, and underpasses has increased. Such fires cause primary and secondary damage, including loss of human life, traffic congestion, and extensive financial damage. Therefore, a risk grade model and effective response plan need to be established for such cases in order to minimize the social and economic costs of bridge fires. In this study, the hazard factors contributing to bridge fires were selected to apply a risk grade model. A total of 144 bridge fire simulations were performed to calculate a surface temperature based on time by using Fire Dynamics Simulation (FDS). A risk grade in accordance with the degree of surface damage state caused by temperature of bridges was presented, and the mobilization time criteria for fire suppression were proposed. The surface temperatures based on time can be classified according to the vertical clearance and mobilization time criteria for fire suppression. Through the classified maximum surface temperatures based on time for bridges, the risk grade can be estimated according to the degree of surface damage state caused by temperature. In order to evaluate the applicability of the established risk grade model to the actual bridge, the arrival time taken from the bridge to the fire station was calculated through a Geographic Information System (GIS) network analysis, and the grades for actual bridge cases were assessed. The purpose of this bridge fire risk grade model is to establish a disaster prevention strategy based on risk grades and to minimize the subsequent social damage by determining a priori the disaster scale.
Hojune Ann; Youngjin Choi; Jin Hyuk Lee; Young Ik Jang; Jung Sik Kong. Semiquantitative Fire Risk Grade Model and Response Plans on a National Highway Bridge. Advances in Civil Engineering 2019, 2019, 1 -13.
AMA StyleHojune Ann, Youngjin Choi, Jin Hyuk Lee, Young Ik Jang, Jung Sik Kong. Semiquantitative Fire Risk Grade Model and Response Plans on a National Highway Bridge. Advances in Civil Engineering. 2019; 2019 ():1-13.
Chicago/Turabian StyleHojune Ann; Youngjin Choi; Jin Hyuk Lee; Young Ik Jang; Jung Sik Kong. 2019. "Semiquantitative Fire Risk Grade Model and Response Plans on a National Highway Bridge." Advances in Civil Engineering 2019, no. : 1-13.