From 84f8c1a74763aceb1fee635b4ff4bb2c326e93dd Mon Sep 17 00:00:00 2001 From: navanvliet Date: Fri, 22 Mar 2024 17:36:26 +0100 Subject: [PATCH] update material characteristics concrete + concrete steel --- .../material_characteristics_concrete.ipynb | 311 +++++++++++++++++- ...erial_characteristics_concrete_steel.ipynb | 20 +- 2 files changed, 326 insertions(+), 5 deletions(-) diff --git a/book/basics/concrete/material_characteristics_concrete.ipynb b/book/basics/concrete/material_characteristics_concrete.ipynb index 66c2ee8..b1cd174 100644 --- a/book/basics/concrete/material_characteristics_concrete.ipynb +++ b/book/basics/concrete/material_characteristics_concrete.ipynb @@ -4,20 +4,323 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Material characteristics concrete" + "# Material characteristics concrete\n", + "\n", + "This paragraph contains tables containing the different material properties of the defined concrete introduction strength classes.\n", + "\n", + "## Strenght and stiffness\n", + "\n", + "| Material Property | C12/15 | C20/25 | C30/37 | C35/45 | C45/55 | C50/60 |\n", + "|-----------------------------------------------|----------------|--------|--------|--------|--------|--------|\n", + "| Density $ρ_{rep}$ (kg/m³) | 2500 | 2500 | 2500 | 2500 | 2500 | 2500 |\n", + "| Modulus of Elasticity (uncracked) $E_{cm}$ (N/mm²) | 27000 | 30000 | 33000 | 34000 | 36000 | 37000 |\n", + "| Mean Compressive Strength $f_{cm}$ (N/mm²) | 20 | 28 | 38 | 43 | 53 | 58 |\n", + "| Characteristic Cylinder Strength $f_{ck}$ (N/mm²) | 12 | 20 | 30 | 35 | 45 | 50 |\n", + "| Characteristic Cube Strength $f_{ck;cube}$ (N/mm²) | 15 | 25 | 37 | 45 | 55 | 60 |\n", + "| Mean Value Tensile Strength $f_{ctm}$ (N/mm²) | 1.6 | 2.2 | 2.9 | 3.2 | 3.8 | 4.1 |\n", + "| Characteristic Tensile Strength $f_{ctk;0,05}$ (N/mm²) | 1.1 | 1.5 | 2.0 | 2.2 | 2.7 | 2.9 |\n", + "\n", + "\n", + "## Strength\n", + "\n", + "Design compressive strength: $ \\frac{f_{ck}}{\\gamma} = \\frac{f_{ck}}{1.5} $\n", + "\n", + "Design shear strength: $ b \\cdot d \\cdot 0.035 \\cdot k^{\\frac{3}{2}} \\cdot \\sqrt{f_{ck}} $ (lower bound, for exact value, see EC2). Shear resistance can be increased using shear reinforcement.\n", + "\n", + "$ k = 1 + \\sqrt{\\frac{200}{d}} \\leq 2 $; d in mm\n", + "\n", + "## Stress-strain diagram\n", + "- $σ_{c}$ : concrete compressive stress\n", + "- $ε_c$ : concrete compressive strain\n", + "- $f_{cd}$ : design compressive strength\n", + "- $ε_{c3}$ : strain at the start of plastic deformation\n", + "- $ε’_{bu}$ : limit strain\n", + "- $ε_{c3}$ : 1,75 ‰\n", + "- $ε_{cu3}$ : 3,50 ‰" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "eps_array = np.array([[0, 1.75, 3.5],\n", + " [0, 1.75, 3.5],\n", + " [0, 1.75, 3.5],\n", + " [0, 1.75, 3.5],\n", + " [0, 1.75, 3.5],\n", + " [0, 1.75, 3.5],\n", + " [0, 1.8, 3.1],\n", + " [0, 1.9, 2.9],\n", + " [0, 2.0, 2.7],\n", + " [0, 2.2, 2.6],\n", + " [0, 2.3, 2.6]])\n", + "\n", + "f_ck_array = np.array([12, 20, 30, 35, 45, 50, 55, 60, 70, 80, 90])\n", + "\n", + "\n", + "plt.figure(figsize=(12,6))\n", + "i=4\n", + "\n", + "f_ck = np.array([0, f_ck_array[i], f_ck_array[i]])\n", + "f_cd = f_ck/1.5\n", + "eps = eps_array[i,:]\n", + " \n", + "plt.plot(eps, f_ck, c='r', linestyle='--', label=f'f_ck = {f_ck[-1]:.2f} N/mm^2')\n", + "plt.plot(eps, f_cd, c='r', label=f'f_cd = {f_cd[-1]:.2f} N/mm^2')\n", + "plt.axhline(f_ck[-1], linewidth=0.5, linestyle='--')\n", + "plt.axhline(f_cd[-1], linewidth=0.5, linestyle='--')\n", + "plt.axvline(eps[-1], linewidth=0.5, linestyle='--')\n", + "plt.axvline(eps[-2], linewidth=0.5, linestyle='--')\n", + "plt.text(0.01, f_ck[-1], 'f_ck', rotation='horizontal', va='bottom')\n", + "plt.text(0.01, f_cd[-1], 'f_cd', rotation='horizontal', va='bottom')\n", + "plt.text(eps[-1], 0.01, 'ε_cu3', rotation='horizontal', va='bottom')\n", + "plt.text(eps[-2], 0.01, 'ε_c3', rotation='horizontal', va='bottom')\n", + "\n", + "\n", + "plt.title('Stress-strain diagram')\n", + "plt.xlabel('ε_c [‰]')\n", + "plt.xlim(0,4)\n", + "plt.ylabel('σ_c [N/mm²]')\n", + "plt.ylim(0,50)\n", + "plt.legend(loc='best');" ] }, { "cell_type": "markdown", "metadata": {}, + "source": [ + "`````{Note}\n", + "This diagram changes when higher strength classes are chosen. For the strength classes shown in the table in this paragraph these values are valid.\n", + "`````" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "24886687986840b380c7491b14661990", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Colors\n", + "grey = '#eeeeee' # light grey fill\n", + "\n", + "## Prepare all the graphs that are to be plotted\n", + "# Define the range of densities to plot\n", + "range_elements = np.arange(0, 11, 1)\n", + "n_element = np.max(range_elements)\n", + "\n", + "## Create plot\n", + "fig = go.Figure()\n", + "\n", + "base_traces = 0 # Number of traces that are always visible\n", + "\n", + "eps_array = np.array([[0, 1.75, 3.5],\n", + " [0, 1.75, 3.5],\n", + " [0, 1.75, 3.5],\n", + " [0, 1.75, 3.5],\n", + " [0, 1.75, 3.5],\n", + " [0, 1.75, 3.5],\n", + " [0, 1.8, 3.1],\n", + " [0, 1.9, 2.9],\n", + " [0, 2.0, 2.7],\n", + " [0, 2.2, 2.6],\n", + " [0, 2.3, 2.6]])\n", + "\n", + "f_ck_array = np.array([12, 20, 30, 35, 45, 50, 55, 60, 70, 80, 90])\n", + "\n", + "# Add traces, one for each slider step\n", + "for i in range_elements: \n", + " f_ck = np.array([0, f_ck_array[i], f_ck_array[i]])\n", + " f_cd = f_ck/1.5\n", + " eps = eps_array[i,:]\n", + "\n", + " fig.add_trace( \n", + " go.Scatter(\n", + " visible=False,\n", + " x=eps,\n", + " y=f_ck,\n", + " line=dict(color='#00FF00'),\n", + " mode='lines',\n", + " name=f'f_ck = {f_ck[-1]:.2f} N/mm^2'))\n", + "\n", + " fig.add_trace( \n", + " go.Scatter(\n", + " visible=False,\n", + " x=eps,\n", + " y=f_cd,\n", + " line=dict(color='#FF0000'),\n", + " mode='lines',\n", + " name=f'f_cd = {f_cd[-1]:.2f} N/mm^2'))\n", + "\n", + "traces_per_step = 2 # Number of traces per value of i\n", + "\n", + "# Show the traces for one value of a when loading the plot (initial setup)\n", + "active_a_index = 1\n", + "for i in range(traces_per_step):\n", + " curr_idx = int(base_traces + active_a_index*traces_per_step + i)\n", + " fig.data[curr_idx].visible = True\n", + "\n", + "# Create and add slider\n", + "steps = []\n", + "for i in range(0, range_elements.shape[0]):\n", + " visarray = [False] * len(fig.data)\n", + " visarray[0:base_traces] = [True] * base_traces\n", + " curr_idx = int(base_traces + i * traces_per_step)\n", + " next_idx = int(base_traces + (i+1) * traces_per_step)\n", + " visarray[curr_idx:next_idx] = [True] * traces_per_step\n", + " step = dict(\n", + " method=\"update\",\n", + " args=[{\"visible\": visarray}],\n", + " # label=str(round(range_elements[i], 1))\n", + " label=str(round(f_ck_array[i], 1))\n", + " )\n", + " steps.append(step)\n", + "\n", + "sliders = [dict(\n", + " active=active_a_index,\n", + " currentvalue={\"prefix\": r\"Concrete strength class f_ck = \"},\n", + " steps=steps\n", + ")]\n", + "\n", + "fig.update_layout(\n", + " sliders=sliders,\n", + " legend_title=\"Legend\",\n", + ")\n", + "\n", + "fig.update_xaxes(title_text='ε_c [‰]', range=[-0.1, 3.6])\n", + "fig.update_yaxes(title_text='σ_c [N/mm²]', range=[-2, 92])\n", + "\n", + "fig.update_layout(\n", + " title=\"Stress-strain diagram\",\n", + " sliders=sliders,\n", + " legend_title=\"Legend\"\n", + ")\n", + "\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "geospatial", "language": "python", - "name": "python3" + "name": "geospatial" }, "language_info": { "codemirror_mode": { @@ -29,7 +332,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.0" + "version": "3.9.13" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/book/basics/concrete/material_characteristics_concrete_steel.ipynb b/book/basics/concrete/material_characteristics_concrete_steel.ipynb index 44f5267..64e4d3b 100644 --- a/book/basics/concrete/material_characteristics_concrete_steel.ipynb +++ b/book/basics/concrete/material_characteristics_concrete_steel.ipynb @@ -4,7 +4,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Material characteristics concrete steel" + "# Material characteristics concrete steel\n", + "\n", + "This paragraph contains information on the material properties of reinforcement steel.\n", + "\n", + "## Strength and stiffness\n", + "\n", + "### Material properties (bars)\n", + "\n", + "| Steel Grade | Tensile Strength $( f_{t;k} )$ (N/mm²) | Yield Stress $( f_{y;k} )$ (N/mm²) | Strain at Maximum Load $\\varepsilon_{su}$ ‰ |\n", + "|---------------------|-----------------------------------------|-------------------------------------|----------------------------------------------------|\n", + "| FeB 400 HWL, HK | 400 | 350 | 4.00 |\n", + "| FeB 500 HWL, HK | 500 | 435 | 3.25 |\n", + "| FeB 500 HKN | 500 | 435 | 2.75 |\n", + "\n", + "### Material properties (nets)\n", + "\n", + "| Steel Grade | Tensile Strength $( f_{t;k} )$ (N/mm²) | Design value tensile strength $( f_{y;k} )$ (N/mm²) | Strain at Maximum Load $\\varepsilon_{su}$ ‰ |\n", + "|----------------------------|-------------------------|--------------------------------------|------------------------------------------------|\n", + "| FeB 500 HKN, HWN | 500 | 435 | 2.175 |\n" ] }, {