{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Usage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plaxis connection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Along with ``padtest`` you will need to import ``plxscripting`` and connect with Plaxis following [these instructions](https://communities.bentley.com/products/geotech-analysis/w/wiki/46005/using-plaxis-remote-scripting-with-the-python-wrapper)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from plxscripting.easy import *\n", "import padtest\n", "\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)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "**WARNING**\n", "\n", "This code will not be repeated in every example, but it is assumed it was executed.\n", "\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create foundation model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "**WARNING**\n", "\n", "``padtest`` employs units of **kN** and **m** by default.\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [geometry](geometry.ipynb) of the foundation is given by:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "d = 1.2 # Foundation depth\n", "b = 2 # Foundation width\n", "b1 = 0.5 # Foundation column width\n", "d1 = 0.5 # Foundation width" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Materials](materials.ipynb) are defined by dictionaries containing the properties required in each case by Plaxis. [Linear-Elastic](materials.ipynb#Linear-elastic), [Mohr-Coulomb](materials.ipynb#Mohr-Coulomb) and [Hardening Soil (HS)](materials.ipynb#Hardening-soil) soil materials are supported. A Mohr-Coulomb material is used for the soil:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# 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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The foundation itself is modeled by plate elements. The `concrete` method generates the dictionary with the [plate material](materials.ipynb#Plate-materials) properties from the desired compressive strength, unit weight and plate thickness." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "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)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create an [axisymmetric foundation](geometry.ipynb#Model-types) model using plate elements for the column and footing:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [], "source": [ "model = padtest.SPlate(s_i, g_i, g_o, b, d, soil, footing, column)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The resulting model geometry can be inspected visually in python:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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", 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" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.plot(figsize=2.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Static](test.ipynb#Load-test), [failure](test.ipynb#Failure-test), [safety](test.ipynb#Safety-test), [dynamic loading](test.ipynb#Dynamic-load-test) and [earthquake shake](test.ipynb#Shake-test) tests are available. A static load test is conducted using the ``load_test`` method. The test consist in consecutive load levels being applied to the foundation. Negative loads imply compression, while positive pull-out. Each load level is a phase where the load is set to the provided value." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "testid = 'test A'\n", "load = [-60, 0, -60] #kN\n", "model.load_test(testid, load)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculation results are stored in a dataframe:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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testphasepreviousplx idprevious plx idlocationsteptimesumMstageSumMsf...Mqy0qy1qxagxagyFy targetFx targetM targetratchetting
0NoneconstructionNonePhase_1Nonetop1.0None0.198850None...0.00.00.00.00.00.00.0000000.00.0False
1NoneconstructionNonePhase_1Nonetop2.0None0.397699None...0.00.00.00.00.00.00.0000000.00.0False
2NoneconstructionNonePhase_1Nonetop3.0None0.596549None...0.00.00.00.00.00.00.0000000.00.0False
3NoneconstructionNonePhase_1Nonetop4.0None0.994248None...0.00.00.00.00.00.00.0000000.00.0False
4NoneconstructionNonePhase_1Nonetop5.0None1.000000None...0.00.00.00.00.00.00.0000000.00.0False
..................................................................
2257test Atest A_stage_2test A_stage_1Phase_4Phase_31.0121.00.00.9395051.0...0.00.00.00.00.00.0-56.3736980.00.0False
2258test Atest A_stage_2test A_stage_1Phase_4Phase_31.0122.00.00.9535381.0...0.00.00.00.00.00.0-57.2148490.00.0False
2259test Atest A_stage_2test A_stage_1Phase_4Phase_31.0123.00.00.9815851.0...0.00.00.00.00.00.0-58.8961350.00.0False
2260test Atest A_stage_2test A_stage_1Phase_4Phase_31.0124.00.00.9919791.0...0.00.00.00.00.00.0-59.5192030.00.0False
2261test Atest A_stage_2test A_stage_1Phase_4Phase_31.0125.00.01.0000001.0...0.00.00.00.00.00.0-60.0000000.00.0False
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2262 rows × 24 columns

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" ], "text/plain": [ " test phase previous plx id previous plx id \\\n", "0 None construction None Phase_1 None \n", "1 None construction None Phase_1 None \n", "2 None construction None Phase_1 None \n", "3 None construction None Phase_1 None \n", "4 None construction None Phase_1 None \n", "... ... ... ... ... ... \n", "2257 test A test A_stage_2 test A_stage_1 Phase_4 Phase_3 \n", "2258 test A test A_stage_2 test A_stage_1 Phase_4 Phase_3 \n", "2259 test A test A_stage_2 test A_stage_1 Phase_4 Phase_3 \n", "2260 test A test A_stage_2 test A_stage_1 Phase_4 Phase_3 \n", "2261 test A test A_stage_2 test A_stage_1 Phase_4 Phase_3 \n", "\n", " location step time sumMstage SumMsf ... M qy0 qy1 qx agx \\\n", "0 top 1.0 None 0.198850 None ... 0.0 0.0 0.0 0.0 0.0 \n", "1 top 2.0 None 0.397699 None ... 0.0 0.0 0.0 0.0 0.0 \n", "2 top 3.0 None 0.596549 None ... 0.0 0.0 0.0 0.0 0.0 \n", "3 top 4.0 None 0.994248 None ... 0.0 0.0 0.0 0.0 0.0 \n", "4 top 5.0 None 1.000000 None ... 0.0 0.0 0.0 0.0 0.0 \n", "... ... ... ... ... ... ... ... ... ... ... ... \n", "2257 1.0 121.0 0.0 0.939505 1.0 ... 0.0 0.0 0.0 0.0 0.0 \n", "2258 1.0 122.0 0.0 0.953538 1.0 ... 0.0 0.0 0.0 0.0 0.0 \n", "2259 1.0 123.0 0.0 0.981585 1.0 ... 0.0 0.0 0.0 0.0 0.0 \n", "2260 1.0 124.0 0.0 0.991979 1.0 ... 0.0 0.0 0.0 0.0 0.0 \n", "2261 1.0 125.0 0.0 1.000000 1.0 ... 0.0 0.0 0.0 0.0 0.0 \n", "\n", " agy Fy target Fx target M target ratchetting \n", "0 0.0 0.000000 0.0 0.0 False \n", "1 0.0 0.000000 0.0 0.0 False \n", "2 0.0 0.000000 0.0 0.0 False \n", "3 0.0 0.000000 0.0 0.0 False \n", "4 0.0 0.000000 0.0 0.0 False \n", "... ... ... ... ... ... \n", "2257 0.0 -56.373698 0.0 0.0 False \n", "2258 0.0 -57.214849 0.0 0.0 False \n", "2259 0.0 -58.896135 0.0 0.0 False \n", "2260 0.0 -59.519203 0.0 0.0 False \n", "2261 0.0 -60.000000 0.0 0.0 False \n", "\n", "[2262 rows x 24 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default 6 points are selected surveyed. The load application point at the surface, and points located at the foundation level at a distance of 0, 0.25, 0.5, 0.75 and 1 times b/2. The load test results can be inspected using the ``plot_test`` function." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.plot_test('test A', location=0, legend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Multiple load tests can be performed in the same model.Each load test will start from the construction phase. **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save/Load" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Saving a model wipes out the Plaxis objects stored in it. This prevents the load test object from further communicating with Plaxis, so no additional can be performed. The information regarding the model geometry, material properties and test results is preserved." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNIGN: Saving the load test to memory whipes out the Plaxis objects. Test results and input parameters will still be avaiable, but no further interaction with Plaxis will be possible. The model can be restored with the method, but load tests will have to be recalculated to access the results whitin Plaxis.\n", "\n", " Do you whish to proceed: [Y/n] " ] } ], "source": [ "model.save('exmaple_model.mdl')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `regen` method regenerates the Plaxis model from a saved model so it can be used to calculate new tests. It can also recalculate the saved tests by setting the argument `test` to `True`. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "model = padtest.load('exmaple_model.mdl')\n", "model.regen(s_i, g_i, g_o, test=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## General model settings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `model_type` argument can be set to `'axisymmetry'` (default) or `'planestrain'`. Also, the title and comments of the model in Plaxis can be set with the `title` and `comments` arguments." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "model = padtest.Plate(s_i, g_i, g_o, b, d, soil, footing, column,\n", " model_type='planestrain', title='plane strain',\n", " comments='This is a plane strain model example.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Boundary conditions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `deformation_boundary_condition` argument imposes user defined deformation boundary conditions to the model. A dictionary must be provided with some, or all, of the fields `'XMin'`, `'XMax'`, `'YMin'` or `'YMax'`. Supported values are `\"Free\"`, `\"Normally fixed\"`, `\"Horizontally fixed\"`, `\"Vertically fixed\"` and `\"Fully fixed\"`. If a boundary is not specified by the user, the default value is adopted.\n", "\n", "The `dynamic_boundary_condtions` argument imposes user defined dynamic boundary conditions to the model to be used in a [dynamic load test](test.ipynb#Dynamic-load-test). A dictionary must be provided with some, or all, of the fields `'XMin'`, `'XMax'`, `'YMin'` or `'YMax'`. Supported values are `\"None\"` and `\"Viscous\"`. If a boundary is not specified by the user, the default value is adopted.\n", "\n", "The `shake_boundary_condtions` argument imposes user defined dynamic boundary conditions to the model to be used in a [shake test](test.ipynb#Shake-test). A dictionary must be provided with some, or all, of the fields `'XMin'`, `'XMax'`, `'YMin'` or `'YMax'`. Supported values are `\"None\"`, `\"Viscous\"` and `\"Free-field\"`. If a boundary is not specified by the user, the default value is adopted." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "model = padtest.Plate(s_i, g_i, g_o, b, d, soil, footing, column,\n", " deformation_boundary_condition={'XMin':'Fully fixed',\n", " 'YMax':'Horizontally fixed'},\n", " dynamic_boundary_condtions={'XMin':'Viscous'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Element type" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `element_type` argument controls the type of elements used in the model. It can be set to `'6-Noded'` or `'15-Noded'`. By default, 15-noded elements are used." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "model = padtest.Plate(s_i, g_i, g_o, b, d, soil, footing, column,\n", " element_type='6-Noded')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preliminary model check" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Waiting for Plaxis to build and run the initial phases can be time consuming only to check that the model has the correct geometry. The `build` attribute controls whether the Plaxis model gets build. By setting it to `False` the geometry can be inspecting with `plot()`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = padtest.Plate(s_i, g_i, g_o, b, d, soil, footing, column, build=False)\n", "model.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `build()` method creates the model in Plaxis so it can be used. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "model.build()" ] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "padtestenv", "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": "3.7.16" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "301.4px" }, "toc_section_display": true, "toc_window_display": true }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }