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Abstract of B.S. Thesis

A Study on the Stability of Underground Openings in a Discontinuous Rock Mass

M.S. Thesis of Moon, Sangho
Directed by Prof. Moon, Hyunkoo
Dept. of Mineral & Petroleum Eng.
Hanyang University, 1997

This study analysis the stability of tunnels in underground by using "DIPS" and "UNWEDGE" of Toronto University. In this study, three major factors, shapes, directions, and support types, are concerned for constructing tunnels in given rock mass conditions. Circle shape, square shape, and horseshoe shape are considered for shapes of tunnel, 10 different directions regarding strike and dip of discontinuities are considered for directions of tunnel, and pattern bolting and random bolting are considered for support types of tunnel. Finally, safest tunnel construction case is chosen through sixty case studies.



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Abstract of M.S. Thesis

A Study on the Rational Determination of Lateral Earth Pressure Coefficient

M.S. Thesis of Moon, Sangho
Directed by Prof. Moon, Hyunkoo
Dept. of Mineral & Petroleum Eng.
Hanyang University, 1999

Using 92 values of lateral earth pressure coefficient(K) measured in Korea, the tendency of K with varying depth is analyzed and compared with the range of K defined by Hoek and Brown. The horizontal stress is generally larger than the vertical stress in Korea : About 84 % of K values are above 1.

In this study, the theory of elasto-plasticity is applied to analyze the variation of K values, and the results are compared with those of numerical analysis. This reveals that the erosion, sedimentation and weathering of earth crust are important factors in the determination of K values. Surface erosion, large lateral pressure and good rock mass increase the K values, but sedimentation decreases the K values. This study makes it possible to analyze the effects of geological processes on the K values, especially at shallow depth where underground excavation takes place.

A neural network expert system, using multi-layer back-propagation algorithm, is developed to predict the K values. The neural network model has a correlation coefficient above 0.996 when compared with measured data. The comparison with 9 measured data which are not included in the back-propagation learning has shown an average inference error of 20% and the correlation coefficient above 0.95. The expert system developed in this study can be used for reliable determination of K values.

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