Application software to evaluate the stability of slopes made of Blocks-In-Matrix materials

slope, stability, GLE, bimsoil, bimrock, tortuosity, A-star, Python, application, software, a-star-algorithm, blocs-in-matrix, general-limit-method, limit-equilibrium-method, slope-stability, tortuous-faiulure-surface, tortuous-path
pip install pybimstab==0.1.5



made-with-python PyPI License Documentation Status

pybimstab is an application software in Python 3 to evaluate the factor of safety against sliding of slopes made of Blocks-In-Matrix (BIM) materials.

The assessment is donde by using the limit equilibrium method through the General Limit Equilibrium (GLE) method of Fredlund & Krahn (1977).

The slip surface has a tortuous geometry and is optimally found by using the A-star algorithm proposed by Hart, Nilsson & Raphael (1968).

The following plots are the final outcome of two different analysis:

Homogeneus slope

Outcome plot example1

Slope made of BIM material

Outcome plot example2



The code was written in Python 3. The packages numpy, scipy, matplotlib and shapely are required for using pybimstab. All of them are downloadable from the PyPI repository by opening a terminal and typing the following code lines:

pip install numpy
pip install scipy
pip install matplotlib
pip install shapely


To install pybimstab open a terminal and type:

pip install pybimstab


To produce the plot shown above execute the following script

from numpy import array
from pybimstab.slope import NaturalSlope
from pybimstab.watertable import WaterTable
from pybimstab.bim import BlocksInMatrix
from pybimstab.slipsurface import CircularSurface, TortuousSurface
from pybimstab.slices import MaterialParameters, Slices
from pybimstab.slopestabl import SlopeStabl
terrainCoords = array(
    [[-2.49, 0.1, 1.7, 3.89, 5.9, 8.12, 9.87, 13.29, 20.29,
      21.43, 22.28, 23.48, 24.65, 25.17],
     [18.16, 17.88, 17.28, 15.73, 14.31, 13.58, 13, 3.61, 3.61,
      3.32, 2.71, 2.23, 1.21, 0.25]])
slope = NaturalSlope(terrainCoords)
bim = BlocksInMatrix(slopeCoords=slope.coords, blockProp=0.25,
                     tileSize=0.4, seed=12345)
watertabDepths = array([[0, 5, 10, 15],
                        [8, 7, 3, 0]])
watertable = WaterTable(slopeCoords=slope.coords,
preferredPath = CircularSurface(
    slopeCoords=slope.coords, dist1=5, dist2=15.78, radius=20)
surface = TortuousSurface(
    bim, dist1=4, dist2=15.78, heuristic='euclidean',
    reverseLeft=False, reverseUp=False, smoothFactor=2,
    preferredPath=preferredPath.coords, prefPathFact=2)
material = MaterialParameters(
    cohesion=15, frictAngle=23, unitWeight=17,
    blocksUnitWeight=21, wtUnitWeight=9.8)
slices = Slices(
    material=material, slipSurfCoords=surface.coords,
    slopeCoords=slope.coords, numSlices=15,
    watertabCoords=watertable.coords, bim=bim)
stabAnalysis = SlopeStabl(slices, seedFS=1, Kh=0, maxLambda=1)
fig = stabAnalysis.plot()


D. G. Fredlund and J. Krahn. Comparison of slope stability methods of analysis. Canadian Geotechnical Journal, 14(3)(3):429–439, 1977.

P. Hart, N. Nilsson, and B. Raphael. A formal basis for the heuristic determination of minimum cost path. IEEE Transactions of Systems Science and Cybernetics, ssc-4(2):100–107, 1968.