{ "cells": [ { "cell_type": "markdown", "id": "f8e2dfc7-11ec-43f0-9a24-e832574000de", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "# Safety levels\n", "\n", "NEN 8700 specifies a number of safety levels, which are essentially reliability indices that a construction must meet:\n", "\n", "| Safety level | Assessment level |\n", "|--------------|------------------|\n", "| CC3 | 3.3 |\n", "| CC2 | 2.5 |\n", "| CC1b | 1.8 |\n", "| CC1a | 1.8 |\n", "\n", "\n", "In this example, we will show how to apply the safety levels to assess the bearing capacity of a pile. \n", "\n", "### Define model\n", "\n", "First, let's import the necessary classes:" ] }, { "cell_type": "code", "execution_count": 172, "id": "316739ad", "metadata": {}, "outputs": [], "source": [ "from probabilistic_library import UncertaintyProject, DistributionType, UncertaintyMethod, StandardNormal" ] }, { "cell_type": "markdown", "id": "988b1250", "metadata": {}, "source": [ "We use the Koppejan method to calculate the bearing capacity of a pile ($p$).\n", "\n", "For this example, we assume there are two soil layers: the top layer is clay, and the bottom layer is sand. The surface level is at $0$ m+NAP. The soil has the following characteristics:\n", "\n", "| Parameter | Description |\n", "|-----------|--------------------------------------|\n", "| z | Depth at which the sand layer starts |\n", "| q_clay | CPT resistance in clay layer |\n", "| q_sand | CPT resistance in sand layer |\n", " \n", "The pile has the following characteristics:\n", "\n", "| Parameter | Description |\n", "|-----------|---------------------|\n", "| D | Diameter of the pile|\n", "| L | Length of the pile |\n", "\n", "The Koppejan method is implemented in the following function:" ] }, { "cell_type": "code", "execution_count": 173, "id": "9bdb608f-6bb9-473e-b958-80524d3a2d7e", "metadata": {}, "outputs": [], "source": [ "from utils.models import get_bearing_capacity" ] }, { "cell_type": "markdown", "id": "fda5188c", "metadata": {}, "source": [ "The bearing capacity of a pile is compared with the applied load, resulting in a unity check value (UC). A UC value greater than $1.0$ indicates failure, meaning the load exceeds the bearing capacity of the pile." ] }, { "cell_type": "code", "execution_count": 174, "id": "5ad8dc24-fbe8-4f3a-b227-cf485fb7b383", "metadata": {}, "outputs": [], "source": [ "def get_unity_check(load, z, q_clay, q_sand, D, L):\n", " p = get_bearing_capacity(z, q_clay, q_sand, D, L)\n", " uc = load / p\n", "\n", " return uc" ] }, { "cell_type": "markdown", "id": "f585a2bd", "metadata": {}, "source": [ "### Uncertainty analysis\n", "\n", "The goal is to calculate the UC values at the safety levels. To achieve this, we conduct a uncertainty analysis. We begin by creating a uncertainty project and defining the model:" ] }, { "cell_type": "code", "execution_count": 175, "id": "29e7d511-67a1-4b6b-8add-c2bb596aa1a0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model get_unity_check:\n", "Input parameters:\n", " load\n", " z\n", " q_clay\n", " q_sand\n", " D\n", " L\n", "Output parameters:\n", " uc\n" ] } ], "source": [ "project = UncertaintyProject()\n", "project.model = get_unity_check\n", "\n", "project.model.print()" ] }, { "cell_type": "markdown", "id": "efa0104c", "metadata": {}, "source": [ "### Assign variables\n", "\n", "The following values and uncertainties are applied to the variables. The load is derived from a design value and is recalculated as a stochastic variable, as we want to perform a full probabilistic calculation." ] }, { "cell_type": "code", "execution_count": 176, "id": "46f2f7ea-d635-43fe-ab2a-0e5e61ac87a9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Load = 84275.78591160773 +- 8427.578591160775\n" ] } ], "source": [ "project.variables[\"D\"].distribution = DistributionType.normal\n", "project.variables[\"D\"].mean = 0.2\n", "project.variables[\"D\"].deviation = 0.04\n", "project.variables[\"D\"].truncated = True\n", "project.variables[\"D\"].minimum = 0.0\n", "project.variables[\"D\"].maximum = 1.0\n", "\n", "project.variables[\"L\"].distribution = DistributionType.normal\n", "project.variables[\"L\"].mean = 12.0\n", "project.variables[\"L\"].deviation = 0.8\n", "\n", "project.variables[\"z\"].distribution = DistributionType.normal\n", "project.variables[\"z\"].mean = 10.0\n", "project.variables[\"z\"].deviation = 0.2\n", "\n", "project.variables[\"q_sand\"].distribution = DistributionType.log_normal\n", "project.variables[\"q_sand\"].mean = 500.0\n", "project.variables[\"q_sand\"].deviation = 50.0\n", "\n", "project.variables[\"q_clay\"].distribution = DistributionType.log_normal\n", "project.variables[\"q_clay\"].mean = 25000.0\n", "project.variables[\"q_clay\"].deviation = 400.0\n", "\n", "project.variables[\"load\"].distribution = DistributionType.gumbel\n", "project.variables[\"load\"].design_quantile = 0.95\n", "project.variables[\"load\"].design_factor = 1.0\n", "project.variables[\"load\"].variation = 0.1\n", "project.variables[\"load\"].design_value = 100000.0\n", "\n", "print(\"Load = {0} +- {1}\".format(project.variables[\"load\"].mean, project.variables[\"load\"].deviation))" ] }, { "cell_type": "markdown", "id": "2c42a313", "metadata": {}, "source": [ "### Perform calculation \n", "\n", "We perform the uncertainty analysis using `crude_monte_carlo` method:" ] }, { "cell_type": "code", "execution_count": 177, "id": "6f65d915", "metadata": {}, "outputs": [], "source": [ "project.settings.uncertainty_method = UncertaintyMethod.crude_monte_carlo\n", "project.settings.maximum_samples = 10000\n", "project.run()\n", "uc_mc = project.stochast" ] }, { "cell_type": "markdown", "id": "01fd2459", "metadata": {}, "source": [ "Next, we derive the values of UC, which correspond to the predefined safety levels. If the UC value for a given safety level is greater than $1.0$, then that safety level is not met." ] }, { "cell_type": "code", "execution_count": 178, "id": "0fee9ef7-a82f-4065-a084-c384766b7203", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Safety level (CC1b): 1.8 => UC = 0.8564457238247961\n", "Safety level (CC2 ): 2.5 => UC = 1.0979807121952414\n", "Safety level (CC3 ): 3.3 => UC = 1.6925889446516011\n" ] } ], "source": [ "safety_levels = [1.8, 2.5, 3.3]\n", "safety_levels_txt = [\"CC1b\", \"CC2 \", \"CC3 \"]\n", "uc_sl = []\n", "\n", "def check_sl(uc, safety_levels, safety_levels_txt):\n", "\n", " for idx, sl in enumerate(safety_levels):\n", " p = StandardNormal.get_p_from_u(sl)\n", " uc_sl.append(uc.get_quantile(p))\n", " print (f\"Safety level ({safety_levels_txt[idx]}): {sl} => UC = {uc_sl[-1]}\")\n", "\n", " return uc_sl\n", "\n", "uc_sl = check_sl(uc_mc, safety_levels, safety_levels_txt)" ] }, { "cell_type": "markdown", "id": "1a1cc78e", "metadata": {}, "source": [ "Let's plot the results:" ] }, { "cell_type": "code", "execution_count": 179, "id": "04777593", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt = uc_mc.get_plot()\n", "for val in uc_sl:\n", " plt.grid()\n", " plt.axvline(x=val, color='black', linestyle='-')" ] }, { "cell_type": "markdown", "id": "bba1fc19", "metadata": {}, "source": [ "\n", "We see that the pile construction meets only the safety level CC1b. Safety levels CC2 and CC3 are not met." ] }, { "cell_type": "markdown", "id": "95760e8a-c4f7-4dec-83aa-b4ceedaab80d", "metadata": {}, "source": [ "### Alternative methods\n", "\n", "We can also apply other uncertainty methods: `form`, `importance_sampling` or `directional_sampling`. \n", "\n", "For `directional_sampling`, it is neceessary to predefine the quantiles in which we are interested (the reliability index $3.3$ is not calculated well, therefore another value is used)." ] }, { "cell_type": "code", "execution_count": 180, "id": "32738b77-f903-479f-ad72-479087434417", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variable uc:\n", " distribution = cdf_curve\n", "Definition:\n", " beta[0.8537] = 1.8\n", " beta[1.086] = 2.5\n", " beta[1.299] = 3\n", "Derived values:\n", " mean = 0.2568\n", " deviation = 0.3322\n", " variation = 1.294\n", "Quantiles:\n", " quantile 0.9641: [8.766e+04, 9.874, 2.488e+04, 483.5, 0.1325, 11.85] -> [0.8537] -> 0.8537\n", " quantile 0.9938: [8.971e+04, 9.827, 2.483e+04, 478.3, 0.1073, 11.8] -> [1.086] -> 1.086\n", " quantile 0.9987: [9.109e+04, 9.798, 2.48e+04, 475.1, 0.0915, 11.77] -> [1.299] -> 1.299\n" ] } ], "source": [ "safety_levels_ds = [1.8, 2.5, 3.0]\n", "\n", "project.settings.uncertainty_method = UncertaintyMethod.directional_sampling\n", "project.settings.maximum_directions = 1000\n", "project.settings.variation_coefficient = 0.01\n", "for sl in safety_levels_ds:\n", " project.settings.quantiles.append(StandardNormal.get_p_from_u(sl))\n", "\n", "project.run()\n", "project.result.print()" ] }, { "cell_type": "markdown", "id": "729d9cd7", "metadata": {}, "source": [ "Let's plot the results:" ] }, { "cell_type": "code", "execution_count": null, "id": "ee053a76", "metadata": { "tags": [ "gallery", "uncertainty" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Safety level (CC1b): 1.8 => UC = 0.8537128868245834\n", "Safety level (CC2 ): 2.5 => UC = 1.0859117555680067\n", "Safety level (CC3 ): 3.0 => UC = 1.2989508870840587\n" ] }, { "data": { "image/png": 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", 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