========= pyBIMstab

.. image:: https://img.shields.io/badge/Made%20with-Python-brightgreen.svg :target: https://www.python.org/ :alt: made-with-python

.. image:: https://img.shields.io/pypi/v/pybimstab.svg :target: https://pypi.python.org/pypi/pybimstab :alt: PyPI

.. image:: https://img.shields.io/badge/License-BSD%202--Clause-brightgreen.svg :target: https://github.com/eamontoyaa/pybimstab/blob/master/LICENSE :alt: License

.. image:: https://readthedocs.org/projects/pybimstab/badge/?version=latest :target: https://pybimstab.readthedocs.io/en/latest/?badge=latest :alt: 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) <https://doi.org/10.1139/t77-045>_.

The slip surface has a tortuous geometry and is optimally found by using the A-star algorithm proposed by Hart, Nilsson & Raphael (1968) <https://doi.org/10.1109/TSSC.1968.300136>_.

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

Homogeneus slope

.. figure:: https://rawgit.com/eamontoyaa/pybimstab/master/examples/figures/coverPlot1.svg :alt: Outcome plot example1

Slope made of BIM material

.. figure:: https://rawgit.com/eamontoyaa/pybimstab/master/examples/figures/coverPlot2.svg :alt: Outcome plot example2

Features

• Documentation <https://pybimstab.readthedocs.io>_
• PyPI <https://pypi.org/project/pybimstab>_
• GitHub <https://github.com/eamontoyaa/pybimstab>_
• Open source and free software: BSD-2-Clause <https://opensource.org/licenses/BSD-2-Clause>_.

Requirements

The code was written in Python 3. The packages numpy <http://www.numpy.org/>, scipy <https://www.scipy.org/>, matplotlib <https://matplotlib.org/>_ and shapely <https://pypi.org/project/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

Installation

To install pybimstab open a terminal and type:

::

pip install pybimstab

Example

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,
                        watertabDepths=watertabDepths,
                        smoothFactor=3)
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()

References

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.