{
"cells": [
{
"cell_type": "markdown",
"id": "8ef12ff3",
"metadata": {},
"source": [
"# Uncertainty analysis\n",
"\n",
"### Uncertainty analysis of the critical head difference\n",
"\n",
"In this example, we demonstrate how to perform an uncertainty analysis of a model. The goal of such an analysis is to estimate the uncertainty in the model output resulting from uncertain input parameters.\n",
"\n",
"We focus on the critical head difference model according to Sellmeijer. This model is applicable to the piping failure mechanism, which describes backward internal erosion beneath dikes with horizontal seepage paths.\n",
"\n",
"### Define model\n",
"\n",
"First, let's import the necessary packages:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "4989b056",
"metadata": {},
"outputs": [],
"source": [
"from probabilistic_library import UncertaintyProject, DistributionType, UncertaintyMethod"
]
},
{
"cell_type": "markdown",
"id": "76d8a8db",
"metadata": {},
"source": [
"The critical head difference, $H_c$, according to the Sellmeijer's model is described by the following equations:"
]
},
{
"cell_type": "markdown",
"id": "8b6d5845",
"metadata": {},
"source": [
"$F_{resistance}=\\eta\\cdot \\frac{\\gamma_{sub,particles}}{\\gamma_{water}}\\cdot \\tan \\theta_{sellmeijer,rev}$\n",
"\n",
"\n",
"$F_{scale}=\\frac{d_{70.m}}{\\sqrt[3]{\\kappa\\cdot L}}\\cdot\\left(\\frac{d_{70}}{d_{70.m}}\\right)^{0.4}$ and $\\kappa = \\frac{\\nu_{water}}{g}\\cdot k$\n",
"\n",
"\n",
"$F_{geometry}=0.91\\cdot \\left(\\frac{D}{L}\\right)^{\\frac{0.28}{\\left(\\frac{D}{L}\\right)^{2.8}-1}+0.04}$\n",
"\n",
"$H_c = F_{resistance} \\cdot F_{scale} \\cdot F_{geometry} \\cdot L$\n",
"\n",
"where:
\n",
"$L$ - seepage length (m)
\n",
"$D$ - thickness of upper sand layer (m)
\n",
"$\\theta$ - bedding angle ($\\circ$)
\n",
"$d_{70}$ - particle diameter (m)
\n",
"$k$ - permeability of the upper sand layer (m/s)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "abd29107",
"metadata": {},
"outputs": [],
"source": [
"from utils.models import model_sellmeijer\n",
"label_critical_head_diff = \"critical head difference (m)\"\n",
"label_pdf = \"pdf (-)\""
]
},
{
"cell_type": "markdown",
"id": "8c7d0666",
"metadata": {},
"source": [
"### Uncertainty analysis\n",
"\n",
"We begin by creating an uncertainty project and defining the model:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "fa1879e9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model model_sellmeijer:\n",
"Input parameters:\n",
" k\n",
" L\n",
" d70\n",
" D\n",
"Output parameters:\n",
" delta_h_c\n"
]
}
],
"source": [
"project = UncertaintyProject()\n",
"project.model = model_sellmeijer\n",
"\n",
"project.model.print()"
]
},
{
"cell_type": "markdown",
"id": "cde91cc9",
"metadata": {},
"source": [
"We define all the input parameters of the model as random variables:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "ad1ee1db",
"metadata": {},
"outputs": [],
"source": [
"project.variables[\"k\"].distribution = DistributionType.log_normal\n",
"project.variables[\"k\"].mean = 0.000245598\n",
"project.variables[\"k\"].variation = 0.25\n",
"\n",
"project.variables[\"L\"].distribution = DistributionType.log_normal\n",
"project.variables[\"L\"].mean = 40.0\n",
"project.variables[\"L\"].variation = 0.25\n",
"\n",
"project.variables[\"d70\"].distribution = DistributionType.log_normal\n",
"project.variables[\"d70\"].mean = 0.00019\n",
"project.variables[\"d70\"].variation = 0.25\n",
"\n",
"project.variables[\"D\"].distribution = DistributionType.log_normal\n",
"project.variables[\"D\"].mean = 30.0\n",
"project.variables[\"D\"].variation = 0.25"
]
},
{
"cell_type": "markdown",
"id": "6d3081e6",
"metadata": {},
"source": [
"Uncertainty analysis can be performed using one of the following methods: `crude_monte_carlo`, `numerical_integration`, `fosm`, `form`, `importance_sampling` or `directional_sampling`.\n",
"\n",
"The results can be accessed from `project.stochast`."
]
},
{
"cell_type": "markdown",
"id": "75804902",
"metadata": {},
"source": [
"### Crude Monte Carlo\n",
"\n",
"Let's consider the `crude_monte_carlo` method:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "71bde011",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"gallery",
"uncertainty",
"1"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Variable delta_h_c:\n",
" distribution = histogram\n",
"Definition:\n",
" amount[1.101, 1.157] = 2\n",
" amount[1.157, 1.212] = 4\n",
" amount[1.212, 1.268] = 8\n",
" amount[1.268, 1.323] = 10\n",
" amount[1.323, 1.379] = 14\n",
" amount[1.379, 1.434] = 39\n",
" amount[1.434, 1.49] = 38\n",
" amount[1.49, 1.545] = 64\n",
" amount[1.545, 1.601] = 74\n",
" amount[1.601, 1.656] = 106\n",
" amount[1.656, 1.712] = 99\n",
" amount[1.712, 1.767] = 124\n",
" amount[1.767, 1.822] = 103\n",
" amount[1.822, 1.878] = 176\n",
" amount[1.878, 1.933] = 155\n",
" amount[1.933, 1.989] = 189\n",
" amount[1.989, 2.044] = 180\n",
" amount[2.044, 2.1] = 206\n",
" amount[2.1, 2.155] = 212\n",
" amount[2.155, 2.211] = 219\n",
" amount[2.211, 2.266] = 217\n",
" amount[2.266, 2.322] = 204\n",
" amount[2.322, 2.377] = 206\n",
" amount[2.377, 2.433] = 191\n",
" amount[2.433, 2.488] = 191\n",
" amount[2.488, 2.544] = 191\n",
" amount[2.544, 2.599] = 173\n",
" amount[2.599, 2.655] = 162\n",
" amount[2.655, 2.71] = 127\n",
" amount[2.71, 2.766] = 130\n",
" amount[2.766, 2.821] = 102\n",
" amount[2.821, 2.877] = 111\n",
" amount[2.877, 2.932] = 100\n",
" amount[2.932, 2.988] = 93\n",
" amount[2.988, 3.043] = 98\n",
" amount[3.043, 3.099] = 69\n",
" amount[3.099, 3.154] = 64\n",
" amount[3.154, 3.21] = 67\n",
" amount[3.21, 3.265] = 59\n",
" amount[3.265, 3.321] = 63\n",
" amount[3.321, 3.376] = 48\n",
" amount[3.376, 3.432] = 33\n",
" amount[3.432, 3.487] = 48\n",
" amount[3.487, 3.543] = 27\n",
" amount[3.543, 3.598] = 34\n",
" amount[3.598, 3.654] = 15\n",
" amount[3.654, 3.709] = 13\n",
" amount[3.709, 3.765] = 17\n",
" amount[3.765, 3.82] = 13\n",
" amount[3.82, 3.876] = 16\n",
" amount[3.876, 3.931] = 10\n",
" amount[3.931, 3.987] = 10\n",
" amount[3.987, 4.042] = 13\n",
" amount[4.042, 4.098] = 8\n",
" amount[4.098, 4.153] = 6\n",
" amount[4.153, 4.209] = 6\n",
" amount[4.209, 4.264] = 1\n",
" amount[4.264, 4.32] = 8\n",
" amount[4.32, 4.375] = 5\n",
" amount[4.431, 4.486] = 3\n",
" amount[4.486, 4.542] = 4\n",
" amount[4.597, 4.653] = 5\n",
" amount[4.653, 4.708] = 1\n",
" amount[4.708, 4.764] = 3\n",
" amount[4.764, 4.819] = 4\n",
" amount[4.819, 4.875] = 2\n",
" amount[4.875, 4.93] = 1\n",
" amount[4.986, 5.041] = 2\n",
" amount[5.041, 5.097] = 1\n",
" amount[5.319, 5.374] = 1\n",
" amount[5.652, 5.707] = 1\n",
" amount[5.707, 5.763] = 1\n",
"Derived values:\n",
" mean = 2.414\n",
" deviation = 0.5846\n",
" variation = 0.2422\n",
"Variable definition is not valid, plot can not be made.\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"project.settings.uncertainty_method = UncertaintyMethod.crude_monte_carlo\n",
"project.settings.minimum_samples = 4000\n",
"project.settings.maximum_samples = 5000\n",
"\n",
"project.run()\n",
"\n",
"uncer = project.stochast\n",
"uncer.print()\n",
"uncer.plot()"
]
},
{
"cell_type": "markdown",
"id": "17dcad48",
"metadata": {},
"source": [
"### FORM\n",
"\n",
"The following code demonstrates the uncertainty analysis using `form`:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "574bc6e5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Variable delta_h_c:\n",
" distribution = cdf_curve\n",
"Definition:\n",
" beta[0.413] = -7.985\n",
" beta[0.4571] = -7.487\n",
" beta[0.5062] = -6.988\n",
" beta[0.5607] = -6.49\n",
" beta[0.6214] = -5.992\n",
" beta[0.6891] = -5.493\n",
" beta[0.7647] = -4.995\n",
" beta[0.8492] = -4.496\n",
" beta[0.9439] = -3.997\n",
" beta[1.05] = -3.498\n",
" beta[1.17] = -2.999\n",
" beta[1.305] = -2.499\n",
" beta[1.458] = -2\n",
" beta[1.632] = -1.5\n",
" beta[1.829] = -1\n",
" beta[2.054] = -0.5\n",
" beta[2.31] = 0\n",
" beta[2.603] = 0.5\n",
" beta[2.938] = 1\n",
" beta[3.322] = 1.5\n",
" beta[3.761] = 2\n",
" beta[4.264] = 2.5\n",
" beta[4.84] = 2.999\n",
" beta[5.499] = 3.499\n",
" beta[6.253] = 3.999\n",
" beta[7.115] = 4.498\n",
" beta[8.1] = 4.998\n",
" beta[9.226] = 5.498\n",
" beta[10.51] = 5.997\n",
" beta[11.98] = 6.497\n",
" beta[13.66] = 6.997\n",
" beta[15.57] = 7.497\n",
" beta[17.76] = 7.996\n",
"Derived values:\n",
" mean = 2.388\n",
" deviation = 0.5795\n",
" variation = 0.2427\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"project.settings.uncertainty_method = UncertaintyMethod.form\n",
"project.settings.maximum_iterations = 50\n",
"\n",
"project.run()\n",
"\n",
"uncer = project.stochast\n",
"uncer.print()\n",
"uncer.plot()"
]
}
],
"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
}