• Title/Summary/Keyword: 일축압축강도시험

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A Study on Hybrid Grout Material for Reservoir Embankment reinforcement (저수지 제방 보수보강을 위한 하이브리드형 그라우트재 연구)

  • Park, Sung-Yong;Shim, Houng-Gen;Kang, Hee-Jin;Lim, One-Bin;Sami, Ghazali-Flimban;Kim, Yong-Seong
    • Journal of the Korean Geosynthetics Society
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    • v.16 no.3
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    • pp.21-30
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    • 2017
  • Cement grouting method is generally applied for the purpose of reinforcement of deteriorated reservior. Problems have been raised due to the limit of the injection material. In order to solve these problems, various grout materials have been developed. However, there are many cases in which the grounds are disturbed in actual field. In this study, the physical properties of hybrid grout with high fineness and high viscosity characteristics were analyzed to enable penetration into the ground. Optimum inflation agent was selected and mixed with the grout. The pressure and compaction effect on expansion was examined and its effectiveness was verified. From the result of confirming expansion ratio, uniaxial compressive strength, expansion pressure and compaction effect, the HI-E (2%) sample was analyzed to be excellent in improvement effect by the inflation agent. Hence, hybrid grout can be effectively applied for the impermeable and reinforcement method of deterioration reservoir and tide embankment.

Application of linear-array microtremor surveys for rock mass classification in urban tunnel design (도심지 터널 암반분류를 위한 선형배열 상시진동 탄성파 탐사 적용)

  • Cha, Young-Ho;Kang, Jong-Suk;Jo, Churl-Hyun
    • Geophysics and Geophysical Exploration
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    • v.9 no.1
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    • pp.108-113
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    • 2006
  • Urban conditions, such as existing underground facilities and ambient noise due to cultural activity, restrict the general application of conventional geophysical techniques. At a tunnelling site in an urban area along an existing railroad, we used the refraction microtremor (REMI) technique (Louie, 2001) as an alternative way to get geotechnical information. The REMI method uses ambient noise recorded by standard refraction equipment and a linear geophone array to derive a shear-wave velocity profile. In the inversion procedure, the Rayleigh wave dispersion curve is picked from a wavefield transformation, and iteratively modelled to get the S-wave velocity structure. The REMI survey was carried out along the line of the planned railway tunnel. At this site vibrations from trains and cars provided strong seismic sources that allowed REMI to be very effective. The objective of the survey was to evaluate the rock mass rating (RMR), using shear-wave velocity information from REMI. First, the relation between uniaxial compressive strength, which is a component of the RMR, and shear-wave velocity from laboratory tests was studied to learn whether shear-wave velocity and RMR are closely related. Then Suspension PS (SPS) logging was performed in selected boreholes along the profile, in order to draw out the quantitative relation between the shear-wave velocity from SPS logging and the RMR determined from inspection of core from the same boreholes. In these tests, shear-wave velocity showed fairly good correlation with RMR. A good relation between shear-wave velocity from REMI and RMR could be obtained, so it is possible to estimate the RMR of the entire profile for use in design of the underground tunnel.

Rock Mass Stability of the Buddha Statue on a Rock Cliff using Fracture Characteristics and Geological Face-Mapping (마애불 암반의 단열특성과 지질맵핑을 이용한 안정성 해석)

  • Ihm, Myeong Hyeok
    • The Journal of the Convergence on Culture Technology
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    • v.9 no.1
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    • pp.539-544
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    • 2023
  • The subject of this study is the Maae Buddha statue in granodiorite of the Mesozoic Cretaceous period, which is concerned about stability as a standing stone cultural property located in ◯◯-dong, Gyeongsangbuk-do. For stability analysis, three-dimensional face mapping, geological properties of joints, three-dimensional scanning, ultrasonic velocity, polarization microscopy, electron microscopy analysis and XRD analysis were performed. In addition, the safety factor of the Maaebul was calculated by analyzing the damage status investigation, stereographic projection analysis, rock classification, and limit equilibrium analysis. The types and scales of damage and possible collapse by section depend on the degree of weathering of the rock and the orientation and characteristics of the joints, but wedge-failure and toppling-failure are expected to be small-scale. The safety factor of Maaebul in dry and wet conditions is less than 1.2, so stability is concerned. The types of damage were mainly observed, such as exfoliation, cracking, granular decomposition, and vegetation growth. The Maaebul rock is granodiorite, and the surface discoloration materials are K, Fe, and Mg. The 4 sets of joints are developed, J1 is tensile joint and the others are shear joint. The uniaxial compressive strength estimated by ultrasonic exploration is 514kgf/cm2, which corresponds to most soft rocks and some weathered rocks. Rock classification(RMR) is estimated to be grade 5, very poor rock mass. These technique along with the existing methods of safety diagnosis of cultural properties are expected to be a reasonable tool for objective interpretation and stability review of stone cultural properties.

Evaluation of Field Applicability with Coal Mine Drainage Sludge as a Liner: Part II: Effect of Freezing/Thawing in CMDS Mixed Liner (차수재로의 광산슬러지 재활용 적용성 평가: Part II: 동결/융해에 의한 광산슬러지 혼합 차수재의 거동)

  • Lee, Jai-Young;Bae, Sun-Young;Park, Kyoung-Joo
    • Journal of the Korean Geosynthetics Society
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    • v.10 no.2
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    • pp.73-79
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    • 2011
  • Based on the results of Part 1 of our two-parts paper, the possibility on field applicability of CMDS(Coal Mine Drainage Sludge) mixed with bentonite and cement as a liner in landfill sites was investigated. The optimum moisture content that met the landfill liner condition was obtained when the ratio of CMDS: bentonite: cement was 1: 0.5: 0.3 in a lab-scale. The relative compaction was measured in 90.1%, which results for construction field have been generally acceptable. In this study, a large-scale Lysimeter($1.0m{\times}1.5m{\times}2.0m$) was used to simulate the effects of the layer on the freeze/thaw by -20 average temperature. The mixture after freezing/thawing showed compressive strength more than $5kg/cm^2$, which was satisfied with EPA standards. Initial permeability of CMDS was $7.10{\times}10^{-7}cm/s$ and permeability its mixture after freezing/thawing was increased to $9.80{\times}10^{-7}cm/s$. The change of temperature in the layers rises and falls with linear and temperature gradient keep maintain the present state. Moisture contents in the layers have not been radically changed. Through the leaching test determined by KSLT method, it was found that heavy metals excluding Zn and Ni were not leached out or leached out less than the standards during 7 cycles of freezing/thawing process. Since it shows the increased permeability about 1.5 times and slight change in moisture content, but it was satisfied with EPA standar through 7 cycles of freezing/thawing process, this mixture can be applied as a liner in landfill final cover system.

A study on the rock mass classification in boreholes for a tunnel design using machine learning algorithms (머신러닝 기법을 활용한 터널 설계 시 시추공 내 암반분류에 관한 연구)

  • Lee, Je-Kyum;Choi, Won-Hyuk;Kim, Yangkyun;Lee, Sean Seungwon
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.23 no.6
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    • pp.469-484
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    • 2021
  • Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, Vp, Vs, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.