Estimate a probability using randomized quasi Monte Carlo

In this basic example we are going to estimate a failure probability.

Let’s consider the following analytical example of a cantilever beam, of Young’s modulus E, length L, section modulus I.

One end is built in a wall and we apply a concentrated bending load F at the other end of the beam, resulting in a deviation:

d = \frac{F*L^3}{3*E*I}

It is considered that failure occurs when the beam deviation is too important:

d \ge 30 (cm)

Four independent random variables are considered:

  • E (the Young’s modulus) [Pa]
  • F (the load) [N]
  • L (the length) # [m]
  • I (the section) # [m^4]

Stochastic model (simplified model, no units):

  • E ~ Beta(0.93, 3.2, 2.8e7, 4.8e7)
  • F ~ LogNormal(30000, 9000, 15000)
  • L ~ Uniform(250, 260)
  • I ~ Beta(2.5, 4.0, 3.1e2, 4.5e2)
In [9]:
from __future__ import print_function
import openturns as ot
In [10]:
# Create the marginal distributions of the parameters
dist_E = ot.Beta(0.93, 3.2, 2.8e7, 4.8e7)
dist_F = ot.LogNormalMuSigma(30000, 9000, 15000).getDistribution()
dist_L = ot.Uniform(250, 260)
dist_I = ot.Beta(2.5, 4.0, 3.1e2, 4.5e2)
marginals = [dist_E, dist_F, dist_L, dist_I]
In [11]:
# Create the Copula
RS = ot.CorrelationMatrix(4)
#RS[2, 3] = -0.2
# Evaluate the correlation matrix of the Normal copula from RS
R = ot.NormalCopula.GetCorrelationFromSpearmanCorrelation(RS)
# Create the Normal copula parametrized by R
copula = ot.NormalCopula(R)
In [12]:
# Create the joint probability distribution
distribution = ot.ComposedDistribution(marginals, copula)
In [13]:
# create the model
model = ot.SymbolicFunction(['E', 'F', 'L', 'I'], ['F*L^3/(3*E*I)'])
In [14]:
# create the event we want to estimate the probability
vect = ot.RandomVector(distribution)
G = ot.CompositeRandomVector(model, vect)
event = ot.Event(G, ot.Greater(), 30.0)
In [15]:
# define the low discrepancy sequence
sequence = ot.SobolSequence()
In [16]:
# Create a simulation algorithm
algo = ot.RandomizedQuasiMonteCarlo(event, sequence)
algo.setMaximumCoefficientOfVariation(0.05)
algo.setMaximumOuterSampling(int(1e5))
algo.run()
In [17]:
# retrieve results
result = algo.getResult()
probability = result.getProbabilityEstimate()
print('Pf=', probability)
Pf= 0.005471091194017522