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http://dx.doi.org/10.6106/KJCEM.2020.21.6.046

Predicting Highway Concrete Pavement Damage using XGBoost  

Lee, Yongjun (Department of Infrastructure Safety Research, Korea Institute of Civil Engineering and Building Technology)
Sun, Jongwan (Department of Infrastructure Safety Research, Korea Institute of Civil Engineering and Building Technology)
Publication Information
Korean Journal of Construction Engineering and Management / v.21, no.6, 2020 , pp. 46-55 More about this Journal
Abstract
The maintenance cost for highway pavement is gradually increasing due to the continuous increase in road extension as well as increase in the number of old routes that have passed the public period. As a result, there is a need for a method of minimizing costs through preventative grievance Preventive maintenance requires the establishment of a strategic plan through accurate prediction old Highway pavement. herefore, in this study, the XGBoost among machine learning classification-based models was used to develop a highway pavement damage prediction model. First, we solved the imbalanced data issue through data sampling, then developed a predictive model using the XGBoost. This predictive model was evaluated through performance indicators such as accuracy and F1 score. As a result, the over-sampling method showed the best performance result. On the other hand, the main variables affecting road damage were calculated in the order of the number of years of service, ESAL, and the number of days below the minimum temperature -2 degrees Celsius. If the performance of the prediction model is improved through more data accumulation and detailed data pre-processing in the future, it is expected that more accurate prediction of maintenance-required sections will be possible. In addition, it is expected to be used as important basic information for estimating the highway pavement maintenance budget in the future.
Keywords
Highway Pavement; Damage Prediction; Maintenance; Machine Learning; XGBoost;
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Times Cited By KSCI : 3  (Citation Analysis)
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