fifteen_pier_condition_factor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "scour_depth_sample_number = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "condition_factor = []\n",
    "pier_local_scour_depth = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def condition_factor_calculation():\n",
    "    \n",
    "    import numpy as np\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    F_w = 1\n",
    "    F_d = 0.8\n",
    "    F_mo = 0.9\n",
    "\n",
    "    F_j = F_w * F_d * F_mo\n",
    "\n",
    "    d_s_loc_min = 1.20\n",
    "    d_s_loc_max = 2.60\n",
    "\n",
    "    F_CM_max = 0.90\n",
    "    F_CM_min = 0.30\n",
    "\n",
    "    x = [d_s_loc_min, d_s_loc_max]\n",
    "    y = [F_CM_max, F_CM_min]\n",
    "\n",
    "    slope_intercept = np.polyfit(x, y, 1) #https://www.w3schools.com/datascience/ds_linear_slope.asp\n",
    "\n",
    "    m = slope_intercept[0]\n",
    "    q = slope_intercept[1]\n",
    "\n",
    "    d_s_loc_no_damage = 0\n",
    "    F_CM_no_damage = 1\n",
    "\n",
    "    d_s_loc_vector = np.linspace(d_s_loc_no_damage, d_s_loc_max, scour_depth_sample_number)\n",
    "\n",
    "    #https://www.pythonpool.com/numpy-piecewise/\n",
    "    F_CM_function = np.piecewise(d_s_loc_vector, [d_s_loc_vector <= 1.00, d_s_loc_vector > 1.00], [lambda d_s_loc_vector: 1.00, lambda d_s_loc_vector: q + m*d_s_loc_vector])\n",
    "\n",
    "    plt.scatter(d_s_loc_vector, F_CM_function, color = 'black')\n",
    "    plt.plot(d_s_loc_vector, F_CM_function, color = 'black')\n",
    "\n",
    "    plt.xlabel('Pier local scour depth')\n",
    "    plt.ylabel('Investigated cracks factor')\n",
    "\n",
    "    F_C = F_CM_function * F_j\n",
    "\n",
    "    plt.plot(d_s_loc_vector, F_C, color = 'black')\n",
    "    plt.xlabel('Pier local scour depth')\n",
    "    plt.ylabel('Condition factor')\n",
    "\n",
    "    condition_factor.append(list(F_C))\n",
    "    pier_local_scour_depth.append(list(d_s_loc_vector))\n",
    "\n",
    "    print(len(condition_factor))\n",
    "\n",
    "    print(condition_factor)\n",
    "    print(pier_local_scour_depth)\n",
    "\n",
    "    %store condition_factor\n",
    "    %store pier_local_scour_depth\n",
    "\n",
    "    # return F_C,d_s_loc_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "[[0.7200000000000001, 0.7200000000000001, 0.7200000000000001, 0.7200000000000001, 0.6617142857142858, 0.5725714285714285, 0.4834285714285714, 0.3942857142857141, 0.3051428571428569, 0.21599999999999975]]\n",
      "[[0.0, 0.2888888888888889, 0.5777777777777778, 0.8666666666666667, 1.1555555555555557, 1.4444444444444446, 1.7333333333333334, 2.0222222222222226, 2.3111111111111113, 2.6]]\n",
      "Stored 'condition_factor' (list)\n",
      "Stored 'pier_local_scour_depth' (list)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "condition_factor_calculation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.7200000000000001,\n",
       "  0.7200000000000001,\n",
       "  0.7200000000000001,\n",
       "  0.7200000000000001,\n",
       "  0.6617142857142858,\n",
       "  0.5725714285714285,\n",
       "  0.4834285714285714,\n",
       "  0.3942857142857141,\n",
       "  0.3051428571428569,\n",
       "  0.21599999999999975]]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(condition_factor[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.0,\n",
       "  0.2888888888888889,\n",
       "  0.5777777777777778,\n",
       "  0.8666666666666667,\n",
       "  1.1555555555555557,\n",
       "  1.4444444444444446,\n",
       "  1.7333333333333334,\n",
       "  2.0222222222222226,\n",
       "  2.3111111111111113,\n",
       "  2.6]]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pier_local_scour_depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pier_local_scour_depth[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored variables and their in-db values:\n",
      "arch_capacity_geometry_variables             -> {'b': 1, 't': 0.5, 'e': 0.08333333333333333}\n",
      "condition_factor                             -> [[0.7200000000000001, 0.7200000000000001, 0.720000\n",
      "pier_local_scour_depth                       -> [[0.0, 0.2888888888888889, 0.5777777777777778, 0.8\n"
     ]
    }
   ],
   "source": [
    "%store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}