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http://dx.doi.org/10.14481/jkges.2022.23.8.5

Development of Machine Learning Model to Predict the Ground Subsidence Risk Grade According to the Characteristics of Underground Facility  

Lee, Sungyeol (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Kang, Jaemo (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Kim, Jinyoung (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Publication Information
Journal of the Korean GEO-environmental Society / v.23, no.8, 2022 , pp. 5-10 More about this Journal
Abstract
Ground Subsidence has been continuously occurring in densely populated downtown. The main cause of ground subsidence is the damaged underground facility like sewer. Currently, ground subsidence is being dealt with by discovering cavities in ground using GPR. However, this consumes large amount of manpower and cost, so it is necessary to predict hazardous area for efficient operation of GPR. In this study, ◯◯city is divided into 500 m×500 m grids. Then, data set was constructed using the characteristics of the underground facility and ground subsidence in grids. Data set used to machine learning model for ground subsidence risk grade prediction. The purposed model would be used to present a ground subsidence risk map of target area.
Keywords
Ground Subsidence; Sewer; Machine Learning; Ground subsidence prediction model; Ground subsidence risk map;
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