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connor-krill committed Jul 26, 2023
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3. FORM - Structural Reliability
==============================================
The benchmark problem is a simple structural reliability problem
The benchmark problem is a simple structural reliability problem (example 7.1 in :cite:`FORM_XDu`)
defined in a two-dimensional parameter space consisting of a resistance :math:`R` and a stress :math:`S`. The failure
happens when the stress is higher than the resistance, leading to the following limit-state function:
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# %% md
#
# With everything defined we are ready to run the first-order reliability method and print the results.
# The analytic solution to this problem is :math:`\bm{u}^*=(-2, 1)` with a reliability index of
# The analytic solution to this problem is :math:`\textbf{u}^*=(-2, 1)` with a reliability index of
# :math:`\beta_{HL}=2.2361` and a probability of failure :math:`P_{f, \text{form}} = \Phi(-\beta_{HL}) = 0.0127`

# %%
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# %% md
#
# This problem can be visualized in the following plots that show the FORM results in both :math:`\bm{X}` and
# :math:`\bm{U}` space.
# This problem can be visualized in the following plots that show the FORM results in both :math:`\textbf{X}` and
# :math:`\textbf{U}` space.

# %%

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8 changes: 8 additions & 0 deletions docs/source/bibliography.bib
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Expand Up @@ -879,3 +879,11 @@ @article{Kle2D
author = {Zhibao Zheng and Hongzhe Dai},
keywords = {Multi-dimensional random field, Karhunen–Loève expansion, Random field simulation, Fredholm integral equation},
}

@misc{FORM_XDu,
title = "Probabilistic Engineering Design, Chapter 7, First Order and Second Reliability Methods",
author = "Xiaoping Du",
year = 2005,
publisher = "University of Missouri – Rolla",
howpublished = "\url{https://pdesign.sitehost.iu.edu/me360/ch7.pdf}"
}
6 changes: 3 additions & 3 deletions docs/source/reliability/form.rst
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^


In FORM, the performance function is linearized according to
In FORM :cite:`FORM_XDu`, the performance function is linearized according to

.. math:: G(\textbf{U}) \approx G(\textbf{U}^\star) + \nabla G(\textbf{U}^\star)(\textbf{U}-\textbf{U}^\star)^\intercal

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Hasofer-Lind-Rackwitz-Fiessler (HLRF) algorithm. The convergence criteria used for HLRF algorithm are:


.. math:: \text{tolerance}_U:\ ||\textbf{U}^{k} - \textbf{U}^{k-1}||_2 \leq 10^{-3}
.. math:: \text{tolerance}_{\textbf{U}}:\ ||\textbf{U}^{k} - \textbf{U}^{k-1}||_2 \leq 10^{-3}
.. math:: \text{tolerance}_\beta:\ ||\beta_{HL}^{k} - \beta_{HL}^{k-1}||_2 \leq 10^{-3}
.. math:: \text{tolerance}_{\nabla G(U)}:\ ||\nabla G(\textbf{U}^{k})- \nabla G(\textbf{U}^{k-1})||_2 \leq 10^{-3}
.. math:: \text{tolerance}_{\nabla G(\textbf{U})}:\ ||\nabla G(\textbf{U}^{k})- \nabla G(\textbf{U}^{k-1})||_2 \leq 10^{-3}



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4 changes: 2 additions & 2 deletions docs/source/reliability/sorm.rst
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SORM
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

In SORM the performance function is approximated by a second-order Taylor series around the design point according to
In SORM :cite:`FORM_XDu` the performance function is approximated by a second-order Taylor series around the design point according to


.. math:: G(\textbf{U}) = G(\textbf{U}^\star) + \nabla G(\textbf{U}^\star)(\textbf{U}-\textbf{U}^\star)^\intercal + \frac{1}{2}(\textbf{U}-\textbf{U}^\star)\textbf{H}(\textbf{U}-\textbf{U}^\star)
.. math:: G(\textbf{U}) = G(\textbf{U}^\star) + \nabla G(\textbf{U}^\star)(\textbf{U}-\textbf{U}^\star)^\intercal + \frac{1}{2}(\textbf{U}-\textbf{U}^\star)\textbf{H}(\textbf{U}-\textbf{U}^\star)(\textbf{U}-\textbf{U}^\star)^T

where :math:`\textbf{H}` is the Hessian matrix of the second derivatives of :math:`G(\textbf{U})` evaluated at
:math:`\textbf{U}^*`. After the design point :math:`\textbf{U}^*` is identified and the probability of failure
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2 changes: 1 addition & 1 deletion src/UQpy/reliability/taylor_series/FORM.py
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Expand Up @@ -115,7 +115,7 @@ def __init__(
"""Design point in the parameter space X."""
self.error_record: list = []
"""Record of the error defined by
criteria :math:`\\text{tolerance}_U, \\text{tolerance}_\\beta, \\text{tolerance}_{\\nabla G(U)}`."""
criteria :math:`\\text{tolerance}_\\textbf{U}, \\text{tolerance}_\\beta, \\text{tolerance}_{\\nabla G(\\textbf{U})}`."""
self.failure_probability: list = []
"""FORM probability of failure :math:`\\Phi(-\\beta)`."""
self.g0 = None
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