Acknowledgement
본 연구는 국토안전관리원에서 수행하는 기본연구사업 (건설시설 안전분야 정보관리체계 개선 및 활용방안에 관한 연구)의 지원으로 수행되었습니다.
References
- Alipour, M., Harris D.K., and Barnes L.E. (2017). "Pattern Recognition in the National Bridge Inventory for Automated Screening and the Assessment of Infrastructure." Proc., Structures Congress 2017, ASCE, Reston, pp. 279-291.
- Bansal, S. (2018). "Data Science Trends on Kaggle." Kaggle, San Francisco, (April 1, 2022).
- Bektas, B.A., Carriquiry, A., and Smadi, O. (2013). "Using Classification Trees for Predicting National Bridge Inventory Condition Ratings." Journal of Infrastructure Systems, 19(4), pp. 425-433. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000143
- Bolukbasi, M., Mohammadi, J., and Arditi, D. (2004). "Estimating the Future Condition of Highway Bridge Components Using National Bridge Inventory Data." Practice Periodical on Structural Design and Construction, 9(1), pp. 16-25. https://doi.org/10.1061/(ASCE)1084-0680(2004)9:1(16)
- Cesare, B.M.A., Santamarina, C., Members, A., Turkstra, C., and Vanmarcke, E.H. (1993). "Modeling Bridge Deterioration with Markov Chains." Journal of Transportaion Engineering, 118(6), pp. 820-833.
- Chawla, N.V., Bowyer, K.W., Hall, L.O., and Kegelmeyer, W.P. (2002). "SMOTE: Synthetic Minority Over-sampling Technique." Journal of Artificial Intelligence Research, 16, pp. 321-357. https://doi.org/10.1613/jair.953
- Chen, T., and Guestrin, C. (2016). "XGBoost: A Scalable Tree Boosting System." Proc., the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, New York, pp. 785-794.
- Friedman, J.H. (2001). "Greedy function approximation: a gradient boosting machine." Annals of statistics, pp. 1189-1232.
- Han, J., Kamber, M., and Pei, J. (2011). Data mining: concepts and techniques. Morgan Kaufmann, Waltham.
- Huang, J., Huang, N., Zhang, L., and Xu, H. (2012). "A method for feature selection based on the correlation analysis." Proc., 2012 International Conference on Measurement, Information, and Control, IEEE, Piscataway, pp. 529-532.
- Huang, Y.H. (2010). "Artificial Neural Network Model of Bridge Deterioration." Journal of Performance of Constructed Facilities, 24(6), pp. 597-602. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000124
- Kim, Y.J., and Yoon, D.K. (2010). "Identifying Critical Sources of Bridge Deterioration in Cold Regions through the Constructed Bridges in North Dakota." Journal of Bridge Engineering, 15(5), pp. 542-552. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000087
- Korea Authority of Land & Infrastructure Safety (KALIS) (2021). 2021 Statistics on Facilities.
- Lim, S., and Chi, S. (2019). "Xgboost application on bridge management systems for proactive damage estimation." Advanced Engineering Informatics, 41, 100922.
- Martinez, P., Mohamed, E., Mohsen, O., and Mohamed, Y. (2020). "Comparative Study of Data Mining Models for Prediction of Bridge Future Conditions." Journal of Performance of Constructed Facilities, 34(1), 04019108.
- Ministry of Land, Infrastructure and Transport (MOLIT) (2021a). Facility Management System, (Oct. 1, 2021).
- Ministry of Land, Infrastructure and Transport (MOLIT) (2021b, Dec. 1). Guidelines for Maintenance and Performance Assessments.
- Ministry of Land, Infrastructure and Transport (MOLIT) (2022). Traffic Monitoring System, (Feb. 10, 2022).
- Morcous, G. (2006). "Performance Prediction of Bridge Deck Systems Using Markov Chains." Journal of Performance of Constructed Facilities, 20(2), pp. 146-155. https://doi.org/10.1061/(ASCE)0887-3828(2006)20:2(146)
- Reddy, M.C., Balasubramanyam, P., and Subbarayudu, M. (2013). "An Effective Approach to Resolve Multicollinearity in Agriculture Data." International Journal of Research in Electronics and Computer Engineering, 1(1), pp. 27-30.
- Shmueli, G., Patel, N.R., and Bruce, P.C. (2010). Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, Wiley, New York.
- Wu, B., Zhang, L., and Zhao, Y. (2014). "Feature selection via Cramer's V-test discretization for remote-sensing image classification." IEEE Transactions on Geoscience and Remote Sensing, 52(5), pp. 2593-2606. https://doi.org/10.1109/TGRS.2013.2263510