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http://dx.doi.org/10.14346/JKOSOS.2018.33.6.42

Modeling on Expansion Behavior of Gwangan Bridge using Machine Learning Techniques and Structural Monitoring Data  

Park, Ji Hyun (Busan Infrastructure Corporation)
Shin, Sung Woo (Department of Safety Engineering, Pukyong National University)
Kim, Soo Yong (Department of Civil Engineering, Pukyong National University)
Publication Information
Journal of the Korean Society of Safety / v.33, no.6, 2018 , pp. 42-49 More about this Journal
Abstract
In this study, we have developed a prediction model for expansion and contraction behaviors of expansion joint in Gwangan Bridge using machine learning techniques and bridge monitoring data. In the development of the prediction model, two famous machine learning techniques, multiple regression analysis (MRA) and artificial neural network (ANN), were employed. Structural monitoring data obtained from bridge monitoring system of Gwangan Bridge were used to train and validate the developed models. From the results, it was found that the expansion and contraction behaviors predicted by the developed models are matched well with actual expansion and contraction behaviors of Gwangan Bridge. Therefore, it can be concluded that both MRA and ANN models can be used to predict the expansion and contraction behaviors of Gwangan Bridge without actual measurements of those behaviors.
Keywords
bridge expansion joint; expansion behavior modeling; structural monitoring data; machine learning;
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Times Cited By KSCI : 3  (Citation Analysis)
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