# Estimate a probability with FORM¶

In this example we estimate a failure probability with the FORM algorithm on the cantilever beam example. More precisely, we show how to use the associated results:

• the design point in both physical and standard space,

• the probability estimation according to the FORM approximation, and the following SORM ones: Tvedt, Hohen-Bichler and Breitung,

• the Hasofer reliability index and the generalized ones evaluated from the Breitung, Tvedt and Hohen-Bichler approximations,

• the importance factors defined as the normalized director factors of the design point in the -space

• the sensitivity factors of the Hasofer reliability index and the FORM probability.

• the coordinates of the mean point in the standard event space.

The coordinates of the mean point in the standard event space is: where is the spheric univariate distribution of the standard space and is the reliability index.

## Model definition¶

from __future__ import print_function
import openturns as ot
import openturns.viewer as viewer
from matplotlib import pylab as plt
ot.Log.Show(ot.Log.NONE)


We load the model from the usecases module :

from openturns.usecases import cantilever_beam as cantilever_beam
cb = cantilever_beam.CantileverBeam()


We use the input parameters distribution from the data class :

distribution = cb.distribution
distribution.setDescription(['E', 'F', 'L', 'I'])


We define the model

model = cb.model


Create the event whose probability we want to estimate.

vect = ot.RandomVector(distribution)
G = ot.CompositeRandomVector(model, vect)
event = ot.ThresholdEvent(G, ot.Greater(), 0.3)
event.setName("deviation")


Define a solver

optimAlgo = ot.Cobyla()
optimAlgo.setMaximumEvaluationNumber(1000)
optimAlgo.setMaximumAbsoluteError(1.0e-10)
optimAlgo.setMaximumRelativeError(1.0e-10)
optimAlgo.setMaximumResidualError(1.0e-10)
optimAlgo.setMaximumConstraintError(1.0e-10)


Run FORM

algo = ot.FORM(optimAlgo, event, distribution.getMean())
algo.run()
result = algo.getResult()


Probability

result.getEventProbability()


Out:

1.0900370418627411e-06


Hasofer reliability index

result.getHasoferReliabilityIndex()


Out:

4.735972259888527


Design point in the standard U* space.

result.getStandardSpaceDesignPoint()


[-0.665643,4.31264,1.23029,-1.3689]

Design point in the physical X space.

result.getPhysicalSpaceDesignPoint()


[6.56566e+10,458.976,2.58907,1.34803e-07]

Importance factors

graph = result.drawImportanceFactors()
view = viewer.View(graph) marginalSensitivity, otherSensitivity = result.drawHasoferReliabilityIndexSensitivity()
marginalSensitivity.setLegends(["E","F","L","I"])
marginalSensitivity.setLegendPosition('bottom')
view = viewer.View(marginalSensitivity) marginalSensitivity, otherSensitivity = result.drawEventProbabilitySensitivity()
marginalSensitivity.setLegends(["E","F","L","I"])
marginalSensitivity.setLegendPosition('bottom')
view = viewer.View(marginalSensitivity) Error history

optimResult = result.getOptimizationResult()
graphErrors = optimResult.drawErrorHistory()
graphErrors.setLegendPosition('bottom')
graphErrors.setYMargin(0.0)
view = viewer.View(graphErrors) algo = ot.SORM(optimAlgo, event, distribution.getMean())
algo.run()
sorm_result = algo.getResult()


Reliability index with Breitung approximation

sorm_result.getGeneralisedReliabilityIndexBreitung()


Out:

4.915018845541476


… with Hohenbichler approximation

sorm_result.getGeneralisedReliabilityIndexHohenbichler()


Out:

4.92039449786118

sorm_result.getGeneralisedReliabilityIndexTvedt()


Out:

4.923707817325711


SORM probability of the event with Breitung approximation

sorm_result.getEventProbabilityBreitung()


Out:

4.4386959812405156e-07


… with Hohenbichler approximation

sorm_result.getEventProbabilityHohenbichler()


Out:

4.31849736540921e-07


… with Tvedt approximation

sorm_result.getEventProbabilityTvedt()

plt.show()


Total running time of the script: ( 0 minutes 0.405 seconds)

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