{
"cells": [
{
"cell_type": "markdown",
"id": "8ef12ff3",
"metadata": {},
"source": [
"# Effect of statistical uncertainty\n",
"\n",
"Probability distributions of load variables are typically based on limited data, leading to (statistical) uncertainty in the estimates of loads for given exceedance probabilities.\n",
"\n",
"Let's consider a load variable $X$ and a random variable $V$, which represents the associated statistical uncertainty. The probability $P(X>x)$ is known, as well as the probability distribution of $V$. Now, let's define a new variable \n",
"$X_{incl}=X+V$. The goal is to estimate $P(X_{incl}>x)$.\n",
"\n",
"In this example, we will demonstrate how to calculate the effect of statistical uncertainty on the exceedance probability of river discharge.\n",
"\n",
"First, let's import the necessary packages:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "4989b056",
"metadata": {},
"outputs": [],
"source": [
"from probabilistic_library import ReliabilityProject, DistributionType, ReliabilityMethod, FragilityValue, ConditionalValue, StandardNormal\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"id": "fce15965",
"metadata": {},
"source": [
"We consider the following limit state function:\n",
"\n",
"$Z = w - (Q +V)$\n",
"\n",
"where: \n",
"\n",
"$Q$ is the river discharge, without the statistical uncertainty (m3/s) \n",
"$V$ is the statistical uncertainty (m3/s) \n",
"$w$ represents a specific value of the river discharge (m3/s)\n",
"\n",
"The river discharge $Q$ is represented as an empirical cumulative distribution function (CDF). The exceedance probabilities of the river discharge for different stages are defined as follows:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8a732284",
"metadata": {},
"outputs": [],
"source": [
"Q_value = [5940,7970,9130,10910,12770,14000,14840,14970,15520,16270,16960,17710]\n",
"Pf_no_stat_uncer = [0.083333333,0.033333333,0.016666667,0.005555556,0.001666667,0.000555556,0.000166667,0.000133333,5.55556E-05,1.66667E-05,5.55556E-06,1.66667E-06]"
]
},
{
"cell_type": "markdown",
"id": "cd87d8fb",
"metadata": {},
"source": [
"The statistical uncertainty $V$ is normally distributed with a mean of $0$. The standard deviation $σ$ is a function of $Q$, defined as follows:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0919d169",
"metadata": {},
"outputs": [],
"source": [
"V_q_value = [5939.9,5940,7970,9130,10910,12770,14000,14840,14970,15520,16270,16960,17710]\n",
"sigma = [340,340,440,500,600,700,560,620,640,750,930,1120,1350]"
]
},
{
"cell_type": "markdown",
"id": "27de6d94",
"metadata": {},
"source": [
"We define the limit state function:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8f01ff35",
"metadata": {},
"outputs": [],
"source": [
"def limit_state_function(q, v, w):\n",
" return w - (q + v)"
]
},
{
"cell_type": "markdown",
"id": "54341fc6",
"metadata": {},
"source": [
"To perform a reliability analysis, we create a reliability project and specify the limit state function (model):"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "784295d2",
"metadata": {},
"outputs": [],
"source": [
"project = ReliabilityProject()\n",
"project.model = limit_state_function"
]
},
{
"cell_type": "markdown",
"id": "aaf99858",
"metadata": {},
"source": [
"Now we define the stochastic variables, starting with the discharge $Q$. We represent this variable as `cdf_curve`. In this case, it is needed to define the `FragilityValue` object and specify its attributes `x` and `probability_of_failure`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2ed3cf17",
"metadata": {},
"outputs": [],
"source": [
"project.variables[\"q\"].distribution = DistributionType.cdf_curve\n",
"\n",
"for ii, val in enumerate(Q_value):\n",
"\n",
" fc = FragilityValue()\n",
" fc.x = val\n",
" fc.probability_of_failure = Pf_no_stat_uncer[ii]\n",
" project.variables[\"q\"].fragility_values.append(fc)"
]
},
{
"cell_type": "markdown",
"id": "cfce598f",
"metadata": {},
"source": [
"We define the statistical uncertainty $V$ using a conditional variable. We assume that $V$ is normally distributed and depends on the value of $Q$. This is defined as follows:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "47d33724",
"metadata": {},
"outputs": [],
"source": [
"project.variables[\"v\"].distribution = DistributionType.normal\n",
"project.variables[\"v\"].mean = 0\n",
"project.variables[\"v\"].deviation = 1\n",
"project.variables[\"v\"].conditional = True\n",
"project.variables[\"v\"].conditional_source = \"q\"\n",
"\n",
"for ii in range(0, len(V_q_value)):\n",
" conditional = ConditionalValue()\n",
" conditional.x = V_q_value[ii]\n",
" conditional.mean = 0.0\n",
" conditional.deviation = sigma[ii]\n",
" project.variables[\"v\"].conditional_values.append(conditional)"
]
},
{
"cell_type": "markdown",
"id": "5f4751bd",
"metadata": {},
"source": [
"We perform reliability calculations with `form` for different values of $w$. This results in the exceedance probability of discharge accounting for the statistical uncertainty."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "51a2856e",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"gallery",
"statistics"
]
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"project.settings.reliability_method = ReliabilityMethod.form\n",
"Pf_with_stat_uncer = []\n",
"\n",
"for w in Q_value:\n",
"\n",
" project.variables[\"w\"].mean = w\n",
" project.run()\n",
" beta = project.design_point.reliability_index \n",
" Pf_with_stat_uncer.append(StandardNormal.get_q_from_u(beta))\n",
" \n",
"plt.figure()\n",
"plt.semilogy(Q_value, Pf_no_stat_uncer, label=\"no statistical uncertainty\")\n",
"plt.semilogy(Q_value, Pf_with_stat_uncer, label=\"with statistical uncertainty\")\n",
"plt.xlabel(\"River discharge (m3/s)\")\n",
"plt.ylabel(\"Exceedance probability\")\n",
"plt.grid()\n",
"plt.legend()\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}