• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.10-17
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• 2020

Recently, the liquefaction phenomenon was first discovered in Korea due to a magnitude 5.4 earthquake that occurred in Pohang, Gyeonsangbuk-do. When liquefaction occurs, some of the water and sand are ejected to the ground, producing a space, which leads to various dangerous situations, such as ground subsidence, building collapse, and sinkhole generation. Recently, the necessity of producing a liquefaction risk map in Korea has increased to grasp potential liquefaction areas in advance. Therefore, this study examined the drilling information from the national geotechnical information DB center at the Ministry of Land, Infrastructure, and Transport to produce a liquefaction risk map, and developed a module to implement functions for basic data modeling and 3D analysis based on drilling information database extraction and information. Through this study, effective interlocking technology of the integrated database of national land information was obtained, and three-dimensional information was generated for each stage of liquefaction risk analysis, such as soil resistance value and a liquefaction risk map. In the future, the technology developed in this study can be used as a comprehensive decision support technology for establishing a foundation for building 3D liquefaction information and for establishing a response system of liquefaction.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.2
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• pp.227-242
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• 2019

This paper presents a method to predict ground types ahead of a tunnel face utilizing operational data of the earth pressure-balanced (EPB) shield tunnel boring machine (TBM) when running through soil ground. The time series analysis model which was applicable to predict the mixed ground composed of soils and rocks was modified to be applicable to soil tunnels. Using the modified model, the feasibility on the choice of the soil conditioning materials dependent upon soil types was studied. To do this, a self-organizing map (SOM) clustering was performed. Firstly, it was confirmed that the ground types should be classified based on the percentage of 35% passing through the #200 sieve. Then, the possibility of predicting the ground types by employing the modified model, in which the TBM operational data were analyzed, was studied. The efficacy of the modified model is demonstrated by its 98% accuracy in predicting ground types ten rings ahead of the tunnel face. Especially, the average prediction accuracy was approximately 93% in areas where ground type variations occur.