• Geomechanics and Engineering
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• 제27권6호
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• pp.605-614
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• 2021

Water inrush from fault is one of the most severe hazards during tunnel excavation. However, the traditional evaluation methods are deficient in both quantitative evaluation and uncertainty handling. In this paper, a comprehensive methodology method combined intuitionistic fuzzy AHP with a Bayesian network for the risk assessment of water inrush from fault in the subsea tunnel was proposed. Through the intuitionistic fuzzy analytic hierarchy process to replace the traditional expert scoring method to determine the prior probability of the node in the Bayesian network. After the field data is normalized, it is classified according to the data range. Then, using obtained results into the Bayesian network, conduct a risk assessment with field data which have processed of water inrush disaster on the tunnel. Simultaneously, a sensitivity analysis technique was utilized to investigate each factor's contribution rate to determine the most critical factor affecting tunnel water inrush risk. Taking Qingdao Kiaochow Bay Tunnel as an example, by predictive analysis of fifteen fault zones, thirteen of them are consistent with the actual situation which shows that the IFAHP-Bayesian Network method is feasible and applicable. Through sensitivity analysis, it is shown that the Fissure development and Apparent resistivity are more critical comparing than other factor especially the Permeability coefficient and Fault dip. The method can provide planners and engineers with adequate decision-making support, which is vital to prevent and control tunnel water inrush.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2010년도 추계 학술발표회
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• pp.955-959
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• 2010

The statistical analysis module is developed for the part of the new standardized geotechnical database computer program. The purpose of this module is that the geotechnical engineers can optimize the underground construction process of the underdeveloped urban area rehabilitation by this module providing the statistical information for the geotechnical decision making and risk assessment. This module will be modified to offer the statistical information sustainable for the newly adapted geotechnical limit-state design methods.

• Geomechanics and Engineering
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• 제22권3호
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• pp.207-217
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• 2020

Slope reliability analysis and risk assessment for spatially variable soils under rainfall infiltration are important subjects but they have not been well addressed. This lack of study may in part be due to the multiple and diverse evaluation indexes and the low computational efficiency of Monte-Carlo simulations. To remedy this, this paper proposes a highly efficient computational method for investigating random field problems for slopes. First, the probability density evolution method (PDEM) is introduced. This method has high computational efficiency and does not need the tens of thousands of numerical simulation samples required by other methods. Second, the influence of rainfall on slope reliability is investigated, where the reliability is calculated from based on the safety factor curves during the rainfall. Finally, the uncertainty of the sliding mass for the slope random field problem is analyzed. Slope failure consequences are considered to be directly correlated with the sliding mass. Calculations showed that the mass that slides is smaller than the potential sliding mass (shallow surface sliding in rainfall). Sliding mass-based risk assessment is both needed and feasible for engineered slope design. The efficient PDEM is recommended for problems requiring lengthy calculations such as random field problems coupled with rainfall infiltration.

• 한국지반공학회지:지반
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• 제36권5호
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• pp.38-53
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• 2020

• 한국지반공학회지:지반
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• 제36권6호
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• pp.48-65
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• 2020

• 한국지반공학회지:지반
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• 제36권4호
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• pp.27-45
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• 2020

• 한국지반공학회지:지반
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• 제37권4호
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• pp.8-20
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• 2021

• 한국지반공학회지:지반
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• 제37권5호
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• pp.9-24
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• 2021

• 한국지반공학회지:지반
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• 제37권2호
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• pp.34-51
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• 2021

• 한국지반공학회지:지반
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• 제37권3호
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• pp.26-36
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• 2021