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.
Bibliographical noteFunding Information:
Funding: This research was supported by a grant(17SCIP-B128492-01) from Smart Civil Infrastructure Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.
© 2020 by the authors.
- Bridge maintenance
- Degradation model
- Structural health monitoring
ASJC Scopus subject areas
- Environmental Science (miscellaneous)
- Geography, Planning and Development
- Energy Engineering and Power Technology
- Management, Monitoring, Policy and Law
- Renewable Energy, Sustainability and the Environment