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2021 Vol.37, Issue 12 Preview Page
31 December 2021. pp. 117-125
Abstract
References
1
Bae, Y., Shin, S.,Won, J., and Lee, D. (2016), "The Road Subsidence Conditions and Safety Improvement Plans in Seoul", 2016-PR-09, The Seoul Institute.
2
Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D. T., ... and Ma, J. (2017), A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility, Catena, Vol.151, pp.147-160. 10.1016/j.catena.2016.11.032
3
Guo, S., Shao, Y., Zhang, T., Zhu, D.Z., Asce, M., and Zhang, Y. (2013), "Physical Modeling on Sand Erosion around Defective Sewer Pipes under the Influence of Groundwater", Journal of Hydraulic Engineering, Vol.139, pp.1247-1257. 10.1061/(ASCE)HY.1943-7900.0000785
4
Hosmer, D. W., Lemeshow, S., and Rodney, X. S. (2000), Applied logistic regression, Wiley, New York. 10.1002/047172214610886529
5
Indiketiya, S., Jegatheesan, P., and Pathmanathan, R. (2017), "Evaluation of Defective Sewer Pipe Induced Internal Erosion and Associated Ground Deformation Using Laboratory Model Test", Canadian Geotechnical Journal, Vol.54, pp.1184-1195. 10.1139/cgj-2016-0558
6
Korea Institute of Civil Engineering and Building Technology (2020), "Underground Space DB Accuracy Improvement and Underground Utilities Safe Management Technology", KICT 2020-215.
7
Korea Institute of Geoscience and Mineral Resources (2014), "A Study on the Causes and Policy Recommendations of Sinkholes", Final Report.
8
Kim, C., Jung, J., Choi, C., and Yoo, W. (2015), "Causes of Ground Subsidence (Sinkholes), Technology and Policy Countermeasures", Ssangyong Engineering & Construction Research Institute, Vol.71, pp.17-25.
9
Kim, K., Kim, J., Kwak, T. Y., and Chung, C. K. (2018), Logistic regression model for sinkhole susceptibility due to damaged sewer pipes, Natural Hazards, Vol.93, No.2, pp.765-785. 10.1007/s11069-018-3323-y
10
Kuwano, R., Horii, T., Yamauchi, K., and Kohashi, H. (2010), "Formation of Subsurface Cavity and Loosening due to Defected Sewer Pipes", Japanese Geotechnical Journal, Vol.5, pp.349-361. 10.3208/jgs.5.349
11
Kwak, T. Y., Woo, S. I., Kim, J., and Chung, C. K. (2019), "Model Test Assessment of the Generation of Underground Cavities and Ground Cave-ins by Damaged Sewer Pipes", Soils and Foundations, Vol.59, pp.586-600. 10.1016/j.sandf.2018.12.011
12
Kwak, T. Y., Woo, S. I., Chung, C. K., and Kim, J. (2020), "Experimental Assessment of the Relationship between Rainfall Intensity and Sinkholes Caused by Damaged Sewer Pipes", Natural Hazards and Earth System Sciences, Vol.20, pp.3343-3359. 10.5194/nhess-20-3343-2020
13
Matin, S. S., Farahzadi, L., Makaremi, S., Chelgani, S. C., and Sattari, G. (2018), "Variable Selection and Prediction of Uniaxial Compressive Strength and Modulus of Elasticity by Random Forest", Applied Soft Computing, Vol.70, pp.980-987. 10.1016/j.asoc.2017.06.030
14
Mukunoki, T., Kumano, N., Otani, J., and Kuwano, R. (2009), "Visualization of Three Dimensional Failure in Sand due to Water Inflow and Soil Drainage from Defective Underground Pipe Using X-Ray CT", Soils and Foundations, Vol.49, pp.959-968. 10.3208/sandf.49.959
15
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... and Duchesnay, E. (2011), "Scikit-learn: Machine Learning in Python. the Journal of machine Learning research", Vol.12, pp.2825-2830.
16
Sato, M. and Kuwano, R. (2015), "Influence of Location of Subsurface Structures on Development of Underground Cavities Induced by Internal Erosion", Soils and Foundations, Vol.55, pp.829-840. 10.1016/j.sandf.2015.06.014
17
Sun, D., Xu, J., Wen, H., and Wang, D. (2021), "Assessment of Landslide Susceptibility Mapping based on Bayesian Hyperparameter Optimization: A Comparison between Logistic Regression and Random Forest", Engineering Geology, Vol.281, 105972. 10.1016/j.enggeo.2020.105972
18
Yokota, T., Fukatani, W., and Miyamoto, T. (2012), "The Present Situation of the Road Cave in Sinkholes Caused by Sewer Systems".
19
Zhang, W., Wu, C., Zhong, H., Li, Y., and Wang, L. (2021), "Prediction of Undrained Shear Strength Using Extreme Gradient Boosting and Random Forest based on Bayesian Optimization", Geoscience Frontiers, Vol.12, No.1, pp.469-477. 10.1016/j.gsf.2020.03.007
Information
  • Publisher :The Korean Geotechnical Society
  • Publisher(Ko) :한국지반공학회
  • Journal Title :Journal of the Korean Geotechnical Society
  • Journal Title(Ko) :한국지반공학회 논문집
  • Volume : 37
  • No :12
  • Pages :117-125
  • Received Date :2021. 12. 07
  • Revised Date :2021. 12. 17
  • Accepted Date : 2021. 12. 17