{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Geometry"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model types"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 4 model types classified by the way in which the foundation structure is modeled (plate elements or solid polygons) and the geometry (symmetric and non-symmetric models):\n",
"\n",
"- ``Plate``: Non-symetric plate model.\n",
"- ``SPlate``: Symetric plate model.\n",
"- ``Solid``: : Non-symetric solid model.\n",
"- ``SSolid``: : Symetric solid model.\n",
"\n",
"In a symmetric model only half of the foundation is build in Plaxis, with the other half assumed to be a mirror image. Consequently in symmetric models the column is always centered in the foundation, and only vertical loads can be applied. The following code creates four models in Plaxis. These four models represent the same strip foundation with a centered column with either plates or a solid and in full or only half:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from plxscripting.easy import *\n",
"import padtest\n",
"\n",
"# start server\n",
"password = \"nicFgr^TtsFm~h~M\"\n",
"localhostport_input = 10000 \n",
"localhostport_output = 10001\n",
"s_i, g_i = new_server('localhost', localhostport_input, password=password) \n",
"s_o, g_o = new_server('localhost', localhostport_output, password=password)\n",
"\n",
"# Geoemtry\n",
"d = 1.2 # Foundation depth\n",
"b = 2 # Foundation width\n",
"b1 = 0.5 # Foundation column width\n",
"d1 = 0.5 # Foundation width\n",
"\n",
"# Mohr-Coulomb soil material\n",
"soil = {}\n",
"soil['SoilModel'] = 'mohr-coulomb'\n",
"soil[\"DrainageType\"] = 'Drained'\n",
"soil['gammaSat'] = 20 # kN/m3\n",
"soil['gammaUnsat'] = 17 # kN/m3\n",
"soil['e0'] = 0.2\n",
"soil['Eref'] = 4e4 # kN\n",
"soil['nu'] = 0.2\n",
"soil['cref'] = 0 # kPa\n",
"soil['phi'] = 35 # deg\n",
"soil['psi'] = 0 # deg\n",
"\n",
"# Plate material for a f'c=30MPa concrete slab with a 40cm thickness and 24kN/m3 unit weight\n",
"column = padtest.concrete(24, b1, fc=30)\n",
"# Plate material for a f'c=30MPa concrete slab with a 60cm thickness and 24kN/m3 unit weight\n",
"footing = padtest.concrete(24, d1, fc=30)\n",
"\n",
"# elastic material for concrete with equivalent stiffness than the column and footing\n",
"concrete = {}\n",
"concrete['SoilModel'] = 'linear elastic'\n",
"concrete[\"DrainageType\"] = 'Drained'\n",
"concrete['Eref'] = 30e6 # kPa\n",
"concrete['nu'] = 0.4 #\n",
"concrete['gammaSat'] = 20 # kN/m3\n",
"concrete['gammaUnsat'] = 17 # kN/m3\n",
"\n",
"# Model creation (run one at the time)\n",
"model1 = padtest.SPlate(s_i, g_i, g_o, b, d, soil, footing, column, model_type='planestrain')\n",
"model2 = padtest.Plate(s_i, g_i, g_o, b, d, soil, footing, column, model_type='planestrain')\n",
"model3 = padtest.SSolid(s_i, g_i, g_o, b, d, b1, d1, soil, concrete, model_type='planestrain')\n",
"model4 = padtest.Solid(s_i, g_i, g_o, b, d, b1, d1, soil, concrete, model_type='planestrain')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The resulting models look like:\n",
"\n",
"![\"Model](\"images/models.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Both symmetric and non-symmetric model can be [plane-strain or axisymmetric in Plaxis](workflow.ipynb#General-model-settings). A symmetric plane-strain model represents a strip foundation with centered column. A symmetric axisymmetric model represents a circular foundation also with a centered column. Non-symmetric plane-strain model represents a strip foundation, with the advantage that [the column does not need to be centered](#Column-offset) in the foundation and that not only vertical, but also [horizontal and moment loads](test.ipynb#Load-test) can be applied to the foundation. A non-symmetric axisymmetric model represents a ring foundation, for example under a tank or silo. As with the plane-strain case, not only vertical, but horizontal and moment loads can be applied, and the column does not need to be centered.\n",
"\n",
"When the column is centered in the foundation and only vertical loads are of interest, the ``SPlate`` and ``SSolid`` are preferable as they required half the number of elements and thus are much faster to run. Although [shake tests](test.ipynb#Shake-test) can be performed in ``SPlate`` and ``SSolid`` models, they do not have a valid physical interpretation as they would imply the soil stretching outwards and contracting from the axis of the foundation. The same happens in ``Plate`` and ``Solid`` models if they are axisymmetric."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solid foundations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Solid foundations model the foundation structure as a solid polygon instead of a plate. A soil material must be provided for the foundation material."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"code_folding": []
},
"outputs": [],
"source": [
"# Geometry\n",
"d = 3 # Foundation depth\n",
"b = 2 # Foundation width\n",
"b1 = 0.5 # Foundation column width\n",
"d1 = 0.5 # Foundation width\n",
"\n",
"# Mohr-Coulomb soil material\n",
"soil = {}\n",
"soil['SoilModel'] = 'mohr-coulomb'\n",
"soil[\"DrainageType\"] = 'Drained'\n",
"soil['gammaSat'] = 20 # kN/m3\n",
"soil['gammaUnsat'] = 17 # kN/m3\n",
"soil['e0'] = 0.2\n",
"soil['Eref'] = 4e4 # kN\n",
"soil['nu'] = 0.2\n",
"soil['cref'] = 0 # kPa\n",
"soil['phi'] = 35 # deg\n",
"soil['psi'] = 0 # deg\n",
"\n",
"# elastic material for concrete\n",
"concrete = {}\n",
"concrete['SoilModel'] = 'linear elastic'\n",
"concrete[\"DrainageType\"] = 'Drained'\n",
"concrete['Eref'] = 30e6 # kPa\n",
"concrete['nu'] = 0.4 #\n",
"concrete['gammaSat'] = 20 # kN/m3\n",
"concrete['gammaUnsat'] = 17 # kN/m3\n",
"\n",
"# build model\n",
"model = padtest.Solid(s_i, g_i, g_o, b, d, b1, d1, soil, concrete)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The resulting model is:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Column offset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The column offset in ``Plate`` and ``Solid`` models is controlled with the ``b2`` argument that represents the distance from the left edge of the footing to the column axis. In ``Solid`` models the column width ``b1`` is included in the geometry, which limits ``b2`` in $b_1 /\\ 2 \\leq b_2 \\leq b - b_1 /\\ 2$. In ``Plate`` models the axis of the column can be located at the edge of the footing plate, so $0 \\leq b_2 \\leq b$."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Geometry\n",
"d = 3 # Foundation depth\n",
"b = 2 # Foundation width\n",
"b1 = 0.5 # Foundation column width\n",
"d1 = 0.5 # Foundation width\n",
"b2 = 0.25 # distance from the left edge of the footing to the center of the column\n",
"\n",
"# Mohr-Coulomb soil material\n",
"soil = {}\n",
"soil['SoilModel'] = 'mohr-coulomb'\n",
"soil[\"DrainageType\"] = 'Drained'\n",
"soil['gammaSat'] = 20 # kN/m3\n",
"soil['gammaUnsat'] = 17 # kN/m3\n",
"soil['e0'] = 0.2\n",
"soil['Eref'] = 4e4 # kN\n",
"soil['nu'] = 0.2\n",
"soil['cref'] = 0 # kPa\n",
"soil['phi'] = 35 # deg\n",
"soil['psi'] = 0 # deg\n",
"\n",
"# elastic material for concrete\n",
"concrete = {}\n",
"concrete['SoilModel'] = 'linear elastic'\n",
"concrete[\"DrainageType\"] = 'Drained'\n",
"concrete['Eref'] = 30e6 # kPa\n",
"concrete['nu'] = 0.4 #\n",
"concrete['gammaSat'] = 20 # kN/m3\n",
"concrete['gammaUnsat'] = 17 # kN/m3\n",
"\n",
"# build model\n",
"model = padtest.Solid(s_i, g_i, g_o, b, d, b1, d1, soil, concrete, b2=b2)\n",
"model.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The same model with plate structure is created with:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Plate material for a f'c=30MPa concrete slab with a 40cm thickness and 24kN/m3 unit weight\n",
"column = padtest.concrete(24, b1, fc=30)\n",
"# Plate material for a f'c=30MPa concrete slab with a 60cm thickness and 24kN/m3 unit weight\n",
"footing = padtest.concrete(24, d1, fc=30)\n",
"\n",
"# build model\n",
"model = padtest.Plate(s_i, g_i, g_o, b, d, soil, footing, column, b2=b2)\n",
"model.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Surface foundations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A surface foundation is created by setting the depth to 0. If the foundation is modeled by plate elements, no material need to be provided for the column:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d = 0 # Foundation depth\n",
"b = 2 # Foundation width\n",
"d1 = 0.5 # Foundation width\n",
"\n",
"# Mohr-Coulomb soil material\n",
"soil = {}\n",
"soil['SoilModel'] = 'mohr-coulomb'\n",
"soil[\"DrainageType\"] = 'Drained'\n",
"soil['gammaSat'] = 20 # kN/m3\n",
"soil['gammaUnsat'] = 17 # kN/m3\n",
"soil['e0'] = 0.2\n",
"soil['Eref'] = 4e4 # kN\n",
"soil['nu'] = 0.2\n",
"soil['cref'] = 0 # kPa\n",
"soil['phi'] = 35 # deg\n",
"soil['psi'] = 0 # deg\n",
"\n",
"# Create with f'c=30MPa concrete slab with a 60cm thickness and 24kN/m3 unit weight\n",
"footing = padtest.concrete(24, d1, fc=30)\n",
"column = None\n",
"\n",
"# Create model\n",
"model = padtest.Plate(s_i, g_i, g_o, b, d, soil, footing, column)\n",
"model.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the foundation is modeled as a solid, the column width is still required as the width of the applied uniform load on top of the foundation."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Geometry\n",
"d = 0 # Foundation depth\n",
"b = 2 # Foundation width\n",
"b1 = 0.6 # Load width\n",
"d1 = 0.3 # Foundation width\n",
"\n",
"# Mohr-Coulomb soil material\n",
"soil = {}\n",
"soil['SoilModel'] = 'mohr-coulomb'\n",
"soil[\"DrainageType\"] = 'Drained'\n",
"soil['gammaSat'] = 20 # kN/m3\n",
"soil['gammaUnsat'] = 17 # kN/m3\n",
"soil['e0'] = 0.2\n",
"soil['Eref'] = 4e4 # kN\n",
"soil['nu'] = 0.2\n",
"soil['cref'] = 0 # kPa\n",
"soil['phi'] = 35 # deg\n",
"soil['psi'] = 0 # deg\n",
"\n",
"# elastic material for concrete\n",
"concrete = {}\n",
"concrete['SoilModel'] = 'linear elastic'\n",
"concrete[\"DrainageType\"] = 'Drained'\n",
"concrete['Eref'] = 30e6 # kPa\n",
"concrete['nu'] = 0.4 #\n",
"concrete['gammaSat'] = 20 # kN/m3\n",
"concrete['gammaUnsat'] = 17 # kN/m3\n",
"\n",
"# build model\n",
"model = padtest.Solid(s_i, g_i, g_o, b, d, b1, d1, soil, concrete)\n",
"model.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Solid foundations can be partially embedded:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Geometry\n",
"d = 0.3 # Foundation depth\n",
"b = 2 # Foundation width\n",
"b1 = 0.6 # Load width\n",
"d1 = 0.5 # Foundation width\n",
"\n",
"# build model\n",
"model = padtest.SSolid(s_i, g_i, g_o, b, d, b1, d1, soil, concrete)\n",
"model.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stratification"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Multiple soil layers are defined by providing a soil material for each layer and specify each layer thickness with the `dstrata` parameter. If the model height exceeds the combined height of the individual layers, the bottom layer is extended."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d = 3 # Foundation depth\n",
"b = 2 # Foundation width\n",
"b1 = 0.5 # Foundation column width\n",
"d1 = 0.5 # Foundation width\n",
"\n",
"# Mohr-Coulomb soil material\n",
"top_layer = {}\n",
"top_layer['SoilModel'] = 'mohr-coulomb'\n",
"top_layer[\"DrainageType\"] = 'Drained'\n",
"top_layer['gammaSat'] = 20 # kN/m3\n",
"top_layer['gammaUnsat'] = 17 # kN/m3\n",
"top_layer['e0'] = 0.2\n",
"top_layer['Eref'] = 4e4 # kN\n",
"top_layer['nu'] = 0.2\n",
"top_layer['cref'] = 0 # kPa\n",
"top_layer['phi'] = 35 # deg\n",
"top_layer['psi'] = 0 # deg\n",
"\n",
"# Mohr-Coulomb second soil material\n",
"mid_layer = {}\n",
"mid_layer['SoilModel'] = 'mohr-coulomb'\n",
"mid_layer[\"DrainageType\"] = 'Drained'\n",
"mid_layer['gammaSat'] = 20 # kN/m3\n",
"mid_layer['gammaUnsat'] = 17 # kN/m3\n",
"mid_layer['e0'] = 0.2\n",
"mid_layer['Eref'] = 4e4 # kN\n",
"mid_layer['nu'] = 0.2\n",
"mid_layer['cref'] = 0 # kPa\n",
"mid_layer['phi'] = 32 # deg\n",
"mid_layer['psi'] = 0 # deg\n",
"\n",
"# Mohr-Coulomb third soil material\n",
"bottom_layer = {}\n",
"bottom_layer['SoilModel'] = 'mohr-coulomb'\n",
"bottom_layer[\"DrainageType\"] = 'Drained'\n",
"bottom_layer['gammaSat'] = 20 # kN/m3\n",
"bottom_layer['gammaUnsat'] = 17 # kN/m3\n",
"bottom_layer['e0'] = 0.2\n",
"bottom_layer['Eref'] = 4e4 # kN\n",
"bottom_layer['nu'] = 0.2\n",
"bottom_layer['cref'] = 0 # kPa\n",
"bottom_layer['phi'] = 39 # deg\n",
"bottom_layer['psi'] = 0 # deg\n",
"\n",
"# Plate material for a f'c=30MPa concrete slab with a 40cm thickness and 24kN/m3 unit weight\n",
"column = padtest.concrete(24, b1, fc=30)\n",
"# Plate material for a f'c=30MPa concrete slab with a 60cm thickness and 24kN/m3 unit weight\n",
"footing = padtest.concrete(24, d1, fc=30)\n",
"\n",
"# build model\n",
"model = padtest.SPlate(s_i, g_i, g_o, b, d,\n",
" [top_layer, mid_layer, bottom_layer], footing, column,\n",
" dstrata=[1, 2, 3])\n",
"model.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fill"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The fill material used in buried foundations is included in the model by provided a soil material in the `fill` argument and the excavation slope (in degrees) in the `fill_angle` argument. The model includes the initial phase with only the original stratigraphy, an excavation phase where the soil material is removed, and a construction phase where the foundation and the fill are activated."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d = 3 # Foundation depth\n",
"b = 2 # Foundation width\n",
"b1 = 0.5 # Foundation column width\n",
"d1 = 0.5 # Foundation width\n",
"\n",
"# Mohr-Coulomb top soil material\n",
"soil = {}\n",
"soil['SoilModel'] = 'mohr-coulomb'\n",
"soil[\"DrainageType\"] = 'Drained'\n",
"soil['gammaSat'] = 20 # kN/m3\n",
"soil['gammaUnsat'] = 17 # kN/m3\n",
"soil['e0'] = 0.2\n",
"soil['Eref'] = 4e4 # kN\n",
"soil['nu'] = 0.2\n",
"soil['cref'] = 0 # kPa\n",
"soil['phi'] = 30 # deg\n",
"soil['psi'] = 0 # deg\n",
"\n",
"# Mohr-Coulomb second soil material\n",
"fill = {}\n",
"fill['SoilModel'] = 'mohr-coulomb'\n",
"fill[\"DrainageType\"] = 'Drained'\n",
"fill['gammaSat'] = 20 # kN/m3\n",
"fill['gammaUnsat'] = 17 # kN/m3\n",
"fill['e0'] = 0.2\n",
"fill['Eref'] = 4e4 # kN\n",
"fill['nu'] = 0.2\n",
"fill['cref'] = 0 # kPa\n",
"fill['phi'] = 32 # deg\n",
"fill['psi'] = 0 # deg\n",
"\n",
"\n",
"# Plate material for a f'c=30MPa concrete slab with a 40cm thickness and 24kN/m3 unit weight\n",
"column = padtest.concrete(24, b1, fc=30)\n",
"# Plate material for a f'c=30MPa concrete slab with a 60cm thickness and 24kN/m3 unit weight\n",
"footing = padtest.concrete(24, d1, fc=30)\n",
"\n",
"# build model\n",
"model = padtest.Plate(s_i, g_i, g_o, b, d, soil, column, footing, fill=fill, fill_angle=25)\n",
"\n",
"model.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `nfill` parameter generates multiple fill layers of equal thickness. A list of soil materials for each layer must be provided, from top to bottom. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = padtest.SPlate(s_i, g_i, g_o, b, d, soil, footing, column,\n",
" nfill=4, fill=[fill,fill,fill,fill],\n",
" fill_angle=25)\n",
"model.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `dfill` parameter generates fill layers of the requested thickness. If the cumulative thickness of the fill layers is below the foundation depth, and additional layer is added."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = padtest.SPlate(s_i, g_i, g_o, b, d, soil, footing, column,\n",
" dfill=[0.5, 1], fill=[fill,fill,fill], fill_angle=25)\n",
"model.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `bfill` parameter controls the distance between the edge of the foundation and the start of the fill slope."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = padtest.SPlate(s_i, g_i, g_o, b, d, soil, footing, column,\n",
" fill=[fill], fill_angle=25, bfill=3)\n",
"model.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Water table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `wt` parameter specifies the global water table of the model, which is applied to all stages."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d = 3 # Foundation depth\n",
"b = 2 # Foundation width\n",
"b1 = 0.5 # Foundation column width\n",
"d1 = 0.5 # Foundation width\n",
"\n",
"# Mohr-Coulomb soil material\n",
"soil = {}\n",
"soil['SoilModel'] = 'mohr-coulomb'\n",
"soil[\"DrainageType\"] = 'Drained'\n",
"soil['gammaSat'] = 20 # kN/m3\n",
"soil['gammaUnsat'] = 17 # kN/m3\n",
"soil['e0'] = 0.2\n",
"soil['Eref'] = 4e4 # kN\n",
"soil['nu'] = 0.2\n",
"soil['cref'] = 0 # kPa\n",
"soil['phi'] = 35 # deg\n",
"soil['psi'] = 0 # deg\n",
"\n",
"# Plate material for a f'c=30MPa concrete slab with a 40cm thickness and 24kN/m3 unit weight\n",
"column = padtest.concrete(24, b1, fc=30)\n",
"# Plate material for a f'c=30MPa concrete slab with a 60cm thickness and 24kN/m3 unit weight\n",
"footing = padtest.concrete(24, d1, fc=30)\n",
"\n",
"# initialize model\n",
"model = padtest.Plate(s_i, g_i, g_o, b, d, soil, footing, column, wt=2)\n",
"\n",
"# plot\n",
"model.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Output locations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When a [test](test.ipynb) is conducted, displacements are retrieved at several points in the foundation. These locations include the top of the column (or the top of the slab in a surface foundation) and several points in the bottom of the footing. The location of these points in the model is defined with the `location` parameter. Values between -1 and 1 are admitted, with -1 representing the left edge of the foundation, 0 the column axis and 1 the right edge. The default output locations at the bottom of the foundation are `[-1, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1]`.\n",
"\n",
"The position of the output locations can be seen in the foundation plot:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Geometry\n",
"d = 3 # Foundation depth\n",
"b = 2 # Foundation width\n",
"b1 = 0.5 # Foundation column width\n",
"d1 = 0.5 # Foundation width\n",
"b2 = 0.7 # column location\n",
"\n",
"# Mohr-Coulomb soil material\n",
"soil = {}\n",
"soil['SoilModel'] = 'mohr-coulomb'\n",
"soil[\"DrainageType\"] = 'Drained'\n",
"soil['gammaSat'] = 20 # kN/m3\n",
"soil['gammaUnsat'] = 17 # kN/m3\n",
"soil['e0'] = 0.2\n",
"soil['Eref'] = 4e4 # kN\n",
"soil['nu'] = 0.2\n",
"soil['cref'] = 0 # kPa\n",
"soil['phi'] = 35 # deg\n",
"soil['psi'] = 0 # deg\n",
"\n",
"# elastic material for concrete\n",
"concrete = {}\n",
"concrete['SoilModel'] = 'linear elastic'\n",
"concrete[\"DrainageType\"] = 'Drained'\n",
"concrete['Eref'] = 30e6 # kPa\n",
"concrete['nu'] = 0.4 #\n",
"concrete['gammaSat'] = 20 # kN/m3\n",
"concrete['gammaUnsat'] = 17 # kN/m3\n",
"\n",
"# build model\n",
"self = padtest.Solid(s_i, g_i, g_o, b, d, b1, d1, soil, concrete, build=False,\n",
" b2=b2, locations=[-1, -0.5, 0, 0.5, 1])\n",
"\n",
"self.plot(output_location=True, figsize=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interfaces"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Interfaces can be included between the footing and the soil by setting the ``interface`` parameter to ``True``. The interface strength is controlled by the ``'Rinter'`` parameter of the adjacent soil or by assigning them a [soil material](materials.ipynb#Soil-materials). Interfaces can be shown in the model plot by setting the ``interface`` parameter to ``True``. The interfaces are shown as red lines in the foundation perimeter."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d = 3 # Foundation depth\n",
"b = 2 # Foundation width\n",
"b1 = 0.5 # Foundation column width\n",
"d1 = 0.5 # Foundation width\n",
"\n",
"# Mohr-Coulomb soil material\n",
"soil = {}\n",
"soil['SoilModel'] = 'mohr-coulomb'\n",
"soil[\"DrainageType\"] = 'Drained'\n",
"soil['gammaSat'] = 20 # kN/m3\n",
"soil['gammaUnsat'] = 17 # kN/m3\n",
"soil['e0'] = 0.2\n",
"soil['Eref'] = 4e4 # kN\n",
"soil['nu'] = 0.2\n",
"soil['cref'] = 0 # kPa\n",
"soil['phi'] = 35 # deg\n",
"soil['psi'] = 0 # deg\n",
"soil['InterfaceStrengthDetermination'] = 'Manual' \n",
"soil['Rinter'] = 0.5 # interface strength\n",
"\n",
"# Plate material for a f'c=30MPa concrete slab with a 40cm thickness and 24kN/m3 unit weight\n",
"# elastic material for concrete\n",
"concrete = {}\n",
"concrete['SoilModel'] = 'linear elastic'\n",
"concrete[\"DrainageType\"] = 'Drained'\n",
"concrete['Eref'] = 30e6 # kPa\n",
"concrete['nu'] = 0.4 #\n",
"concrete['gammaSat'] = 20 # kN/m3\n",
"concrete['gammaUnsat'] = 17 # kN/m3\n",
"\n",
"# build model\n",
"model = padtest.Solid(s_i, g_i, g_o, b, d, b1, d1, soil, concrete,\n",
" interface=True)\n",
"\n",
"# plot\n",
"model.plot(interface=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Individual interfaces can be activated or deactivated using a dictionary with keys ``'column'``, ``'top'``, ``'latera'`` and ``'bottom'``."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# build model\n",
"model = padtest.Solid(s_i, g_i, g_o, b, d, b1, d1, soil, concrete,\n",
" interface={'column':True, 'top':False, 'lateral':True, 'bottom':False})\n",
"\n",
"# plot\n",
"model.plot(interface=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Soil materials](materials.ipynb#Soil-materials) can be assigned to each interface:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Mohr-Coulomb material for the interface\n",
"interface = {}\n",
"interface['SoilModel'] = 'mohr-coulomb'\n",
"interface[\"DrainageType\"] = 'Drained'\n",
"interface['gammaSat'] = 20 # kN/m3\n",
"interface['gammaUnsat'] = 17 # kN/m3\n",
"interface['e0'] = 0.2\n",
"interface['Eref'] = 4e4 # kN\n",
"interface['nu'] = 0.2\n",
"interface['cref'] = 0 # kPa\n",
"interface['phi'] = 35 # deg\n",
"interface['psi'] = 0 # deg\n",
"\n",
"# build model\n",
"model = padtest.Solid(s_i, g_i, g_o, b, d, b1, d1, soil, concrete,\n",
" interface={'column':interface, 'top':False,\n",
" 'lateral':interface, 'bottom':False})\n",
"\n",
"# plot\n",
"model.plot(interface=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model size"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `model_width` and `model_depth` parameters control the extent of the model."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
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"d = 3 # Foundation depth\n",
"b = 2 # Foundation width\n",
"b1 = 0.5 # Foundation column width\n",
"d1 = 0.5 # Foundation width\n",
"\n",
"# Mohr-Coulomb soil material\n",
"soil = {}\n",
"soil['SoilModel'] = 'mohr-coulomb'\n",
"soil[\"DrainageType\"] = 'Drained'\n",
"soil['gammaSat'] = 20 # kN/m3\n",
"soil['gammaUnsat'] = 17 # kN/m3\n",
"soil['e0'] = 0.2\n",
"soil['Eref'] = 4e4 # kN\n",
"soil['nu'] = 0.2\n",
"soil['cref'] = 0 # kPa\n",
"soil['phi'] = 35 # deg\n",
"soil['psi'] = 0 # deg\n",
"\n",
"# Plate material for a f'c=30MPa concrete slab with a 40cm thickness and 24kN/m3 unit weight\n",
"# elastic material for concrete\n",
"concrete = {}\n",
"concrete['SoilModel'] = 'linear elastic'\n",
"concrete[\"DrainageType\"] = 'Drained'\n",
"concrete['Eref'] = 30e6 # kPa\n",
"concrete['nu'] = 0.4 #\n",
"concrete['gammaSat'] = 20 # kN/m3\n",
"concrete['gammaUnsat'] = 17 # kN/m3\n",
"\n",
"# build model\n",
"model = padtest.Solid(s_i, g_i, g_o, b, d, b1, d1, soil, concrete,\n",
" model_width=10, model_depth=4)\n",
"\n",
"# plot\n",
"model.plot()"
]
}
],
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