• Title/Summary/Keyword: Underground Cavity Grade

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Study on Management System of Ground Sinking Based on Underground Cavity Grade (공동관리 등급에 따른 지반함몰 관리등급제에 대한 연구)

  • Lee, Kicheol;Kim, Dongwook;Park, Jeong-Jun
    • Journal of the Korean Geosynthetics Society
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    • v.16 no.2
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    • pp.23-33
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    • 2017
  • Due to the rapid development of cities, densities and heights of urban structures are increased, and much larger and more underground spaces are being developed accordingly. Increasing development of underground spaces has induced more ground sinks and underground cavities. Therefore, safety of people is threatened by potential ground collapses and possible accidents, which may result from underground cavity. To actively respond against potential danger of ground sink, evaluation of existing cavity grade and development of recovery procedure are important. There exists the ground sinking management grade system of expressway developed as local standards in Japan. Recently, ground sinking management grade system of Seoul was developed with consideration of road and asphalt conditions. In this study, 209 underground cavities of ${\bigcirc}{\bigcirc}$ area were explored and their cavity shapes and grades were evaluated based on both ground sinking management grade systems of Japan and Seoul. Comparison is made between cavity grades evaluated based on both grading systems from Japan and Seoul. As a result of comparative analysis, the conservatively-estimated cavity grades requiring emergency restoration based on the Japanese management grade system of expressway result from neglection of layer thickness of surface pavement, considering only width and cover depth of a cavity.

Analysis of the Effect of Pavement Crack Depth of the Cavity Management Grade (포장 균열 깊이가 공동 관리 등급에 미치는 영향 분석)

  • Park, Jeong Jun
    • Journal of the Society of Disaster Information
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    • v.16 no.3
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    • pp.449-457
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    • 2020
  • Purpose: The Seoul Metropolitan Government classifies the cavity risks into emergency, priority, general, and observation grades in consideration of the cavity size, asphalt pavement thickness, and pavement depth based on the cavity management grade criteria of Seoul. In this study, the depth of cracking was measured at 17 cracks identified by checking the pavement condition of the cavity at 265 cavities found in the 2019 cavity investigation service. Method: In the first phase, crack width and depth were measured using a vernier caliper, taper gauge, and depth gauge to check the cracks of the identified cavities. In the second phase, the location of the largest crack in the upper road surface was confirmed, and A.C. was drilled to further measure the crack depth. Results: As a result, the cavity management level was raised in nine of the 17 test cavity identified. Therefore, in case of emergency and priority recovery, the grade should be adjusted according to the depth of pavement crack and the thickness of residual A.C. pavement. Conclusion: In the case of cracks in the upper part of the cavity, the crack progression must be determined through the perforation and the remaining asphalt concrete thickness must be determined to determine the cavity grade.

Analysis of the under Pavement Cavity Growth Rate using Multi-Channel GPR Equipment (멀티채널 GPR 장비를 이용한 도로하부 공동의 크기 변화 분석)

  • Park, Jeong Jun;Kim, In Dae
    • Journal of the Society of Disaster Information
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    • v.16 no.1
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    • pp.60-69
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    • 2020
  • Purpose: Cavity growth process monitoring is to periodically monitor changes in common size and topography for general and observational grades to predict the rate of common growth. The purpose of this study is to establish a systematic cavity management plan by evaluating the general and observational class community in a non-destructive method. Method: Using GPR exploration equipment, the acquired surface image and the surrounding status image are analyzed in the GPR probe radargram in depth, profile, and cross section of the location. The exact location is selected using the distance and surrounding markings shown on the road surface of the initial detection cavity, and the test cavity is analyzed by calling the radar at the corresponding location. Result: As a result of monitoring tests conducted at a cavity 30 sites of general and observation grade, nine sites have been recovered. Changes in scale were seen in 21 cavity locations, and changes in size and grade occurred in 13 locations. Conclusion: The under road cavity is caused by various causes such as damage to the burial site, poor construction, soil leakage caused by groundwater leakage, waste and ground vibration. Among them, indirect factors could infer the effects of groundwater and localized rainfall.

Ground Subsidence Risk Grade Prediction Model Based on Machine Learning According to the Underground Facility Properties and Density (기계학습 기반 지하매설물 속성 및 밀집도를 활용한 지반함몰 위험도 예측 모델)

  • Sungyeol Lee;Jaemo Kang;Jinyoung Kim
    • Journal of the Korean GEO-environmental Society
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    • v.24 no.4
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    • pp.23-29
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    • 2023
  • Ground subsidence shows a mechanism in which the upper ground collapses due to the formation of a cavity due to the movement of soil particles in the ground due to the formation of a waterway because of damage to the water supply/sewer pipes. As a result, cavity is created in the ground and the upper ground is collapsing. Therefore, ground subsidence frequently occurs mainly in downtown areas where a large amount of underground facilities are buried. Accordingly, research to predict the risk of ground subsidence is continuously being conducted. This study tried to present a ground subsidence risk prediction model for two districts of ○○ city. After constructing a data set and performing preprocessing, using the property data of underground facilities in the target area (year of service, pipe diameter), density of underground facilities, and ground subsidence history data. By applying the dataset to the machine learning model, it is evaluated the reliability of the selected model and the importance of the influencing factors used in predicting the ground subsidence risk derived from the model is presented.