Kwang-Ho You

Strength-Reduction Stability Analysis of Rock Slopes Using the Hoek-Brown Failure Criterion

The shear strength reduction technique is a method of performing slope stability analysis using a finite element or finite difference program. This technique is adapted here for use with the non-linear Hoek-Brown failure criterion, an empirical approach to estimating rock mass strength. Hoek-Brown strength parameters can be estimated using rock mass classification schemes such as Rock-mass Rating (RMR) or Geological Strength Index (GSI). Numerical results obtained by strength reduction are compared with limit analysis upper bound solutions derived using a series of linear failure surfaces tangent to the actual non-linear failure surface. Results are presented in graphs of normalized slope height versus RMR that could be used as stability charts.

Stability Analysis of Jointed/Weathered Rock Slopes Using the Hoek-Brown Failure Criterion

ABSTRACT The failure of rock slopes usually occurs along the discontinuities. But for weathered or highly jointed rock mass, failure surface is often curved as in soil slopes. To assess the stability of jointed/weathered rock slopes, shear strength reduction technique is adapted for use with the non-linear Hoek-Brown failure criterion, an empirical approach to estimating rock mass strength. Hoek-Brown strength parameters can be estimated using rock mass classification schemes such as RMR or GSI. Numerical results obtained by strength reduction are compared with limit analysis upper bound solutions derived using a series of linear failure surfaces tangent to the actual non-linear failure surface. Results are presented in graphs of normalized slope height versus RMR that could be used as stability charts.

A study on the fast prediction of the fragmentation zone using artificial neural network when a blasting occurs around a tunnel

When collapse occurs due to explosion near a tunnel, fragmentation zone should be comprehended quickly to recover the function of the tunnel itself. In this study, a method to interpret explosion behavior and predict the fragmentation zone fast. For this purpose, the various 3D-meshes were generated using SolidWorks and explosion analyses were carried out using AUTODYN. The influence of explosion variables such as source location on fragmentation volume were examined by performing sensitivity analyses. Also, a training database for an artificial neural network analysis had been established and the optimal training model was selected, and the predicted results for fragmentation volume and radius were verified. The suggested method had demonstrated that it could be effective for the fast prediction of fragmentation zone.