ANALYSIS THE EFFECT OF DISTRIBUTION DATA FOR DETERMINE OF HYPOTHETICAL SLOPE STABILITY BY USING LIMIT EQUILIBRIUM METHOD
Slope stability is a very crucial condition that must be fulfilled for the smooth operation of mining production. In actual conditions, the value of the safety factor is not enough to express a safe theoretical design in a slope stability practice, where failures still occur on slopes with FS >...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/40222 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Slope stability is a very crucial condition that must be fulfilled for the smooth operation of
mining production. In actual conditions, the value of the safety factor is not enough to
express a safe theoretical design in a slope stability practice, where failures still occur on
slopes with FS > 1 and stable slopes in FS < 1 so that additional analysis needs to be used
besides safety factors, namely the probability of failure. This Final Project Report discusses
the analysis of the effect of standard deviation of input parameters on the distribution of data
in calculating slope stability (FK standard deviation) and its effect also on the probability of
failure and stable probability. This analysis was carried out using two-dimensional numerical
modeling using Limit Equilibrium Method with used cohesion data and friction angles
obtained from the classification of the RMR rock mass system class IV and class V as input
parameters. This analysis of probability of failure is based on the average value and standard
deviation (? ± ?) of cohesion, friction angles, and specific weights as input parameters to
obtain the value of safety factor (FS).
Modeling was carried out using Mohr-Coulomb collapse criteria and used several
assumptions such as specific weight data, cohesion, friction angle and safety factors
normally distributed, the modeled slope has a height of 20 m, etc. A 50x50 grid is used to
facilitate the analysis of the slope stability. In the modeling results, generally obtained FK
averages and FK standard deviations which are then analyzed the effect of standard deviation
of input parameters using the graph of normal distribution so that the trend of FK data
distribution has a rising trend at the input standard deviation which is greater and will affect
the probability of failure and the stable probability value. The greatest probability of failure
is 33% on a stable slope of class V slope of 27° (FK> 1) with a standard deviation of input
parameters 25% and the largest stable probability value is 47% on a failure slope of class V
slope of 34° (FK <1) with input parameter standard deviation of 25%. |
|---|