Ground_curve_Utilization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ground Curve with the Utilization of the Shotcrete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "References:\n",
    "- Gschwandtner, Gunter Gernot: Master Thesis, Leoben, 2010\n",
    "- Schubert, P.: Beitrag zum rheologischen Verhalten vom Spritzbeton, Felsbau 6 (1988), Nr.3, pp. 150-153"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\epsilon_{2} = \\epsilon_{1} + \\dfrac{\\sigma_{1} - \\sigma_{2}}{E_{(t)}} + \\sigma_{2} \\cdot \\Delta C_{(t)} + \\Delta \\epsilon_{d} + \\Delta \\epsilon_{sh} + \\Delta \\epsilon_{t}$\n",
    "<br><br>\n",
    "where:\n",
    "<br><br>\n",
    "$\\epsilon_{2}, \\sigma_{2}$: Strain and stress at time (Dehnung bzw. Spannung am Ende von) $\\Delta t_{2}$\n",
    "<br>\n",
    "$\\epsilon_{1}, \\sigma_{1}$: Strain and stress at time (Dehnung bzw. Spannung am Ende von) $\\Delta t_{1}$\n",
    "<br>\n",
    "$E_{(t)}$: Time dependent young's modulus (Altersabhängige Elastizitätsmodulus)\n",
    "<br>\n",
    "$\\Delta C_{(t)}$: Time dependent change in specific flow strain (Altersabhängige Änderung der spezifischen Fließdehnung)\n",
    "<br>\n",
    "$\\Delta \\epsilon_{d}$: Change of delayed elastic strain (Änderung der verzögert elstischen Dehnung)\n",
    "<br>\n",
    "$\\Delta \\epsilon_{sh}$: Änderung der Schwinddehnung\n",
    "<br>\n",
    "$\\Delta \\epsilon_{t}$: Änderung der Temperaturdehnung\n",
    "<br><br>\n",
    "with:\n",
    "<br><br>\n",
    "$C_{(t)} = A \\cdot t^{1/3}$\n",
    "<br><br>\n",
    "$E_{(t)} = E_{28} \\cdot \\sqrt{\\dfrac{t}{4.2+0.85 \\cdot t}}$ $\\quad$t: time in days (Alter in Tagen); $E_{28}$: Elasticity after 28 days (Elastizitätsmodulus nach 28 Tagen)\n",
    "<br><br>\n",
    "$\\epsilon_{sh} = \\epsilon_{sh,\\infty} \\cdot \\dfrac{t}{t + B}$ $\\quad$ B: Konstante, ein Maß für die Krümmung der Schwindkurve; $\\epsilon_{sh,\\infty}$: Endschwindmaß\n",
    "<br><br>\n",
    "$\\epsilon_{t} = \\left(-cos(t^{0.25} \\cdot 250) + 1 \\right) \\cdot 30$ $\\quad$Note: Kosinus ist in Radiant zu rechnen\n",
    "<br><br>\n",
    "$\\Delta\\epsilon_{d} = (\\sigma_{2} \\cdot C_{d,\\infty} - \\epsilon_{d}) \\cdot (1 - e^{\\frac{-\\Delta C_{t}}{Q}})$\n",
    "<br><br>\n",
    "Q: Konstante, kann aus der Geschwindigkeit der umkehrbaren Kriechverformung gerechnet werden.\n",
    "<br>\n",
    "$C_{d,\\infty}$: Grenzwert der umkehrbaren Kriechverformung"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\epsilon_{2} = \\epsilon_{1} + \\dfrac{\\sigma_{1} - \\sigma_{2}}{E_{(t)}} + \\sigma_{2} \\cdot \\Delta C_{(t)} + \\Delta \\epsilon_{d} + \\Delta \\epsilon_{sh} + \\Delta \\epsilon_{t}$\n",
    "\n",
    "### Rewrite the above formula\n",
    "while using: \n",
    "<br>\n",
    "$\\Delta\\epsilon_{d} = (\\sigma_{2} \\cdot C_{d,\\infty} - \\epsilon_{d}) \\cdot (1 - e^{\\frac{-\\Delta C_{t}}{Q}})$\n",
    "<br><br>\n",
    "$\\sigma_{2} = \\dfrac{(\\epsilon_{2} - \\epsilon_{1} - \\Delta \\epsilon_{sh} - \\Delta \\epsilon_{t} + \\epsilon_{d} \\cdot (1 - e^{\\frac{-\\Delta C_{t}}{Q}}) + \\frac{\\sigma_{1}}{E_{t}}}{C_{d,\\infty} \\cdot (1 - e^{\\frac{-\\Delta C_{t}}{Q}}) + \\Delta C_{t} + \\frac{1}{E_{t}}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-30T11:24:17.170251Z",
     "start_time": "2018-09-30T11:24:16.623492Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-30T11:24:17.182139Z",
     "start_time": "2018-09-30T11:24:17.173781Z"
    }
   },
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Intersection of two curves\n",
    "- Source: https://github.com/sukhbinder/intersection\n",
    "- Based on: https://de.mathworks.com/matlabcentral/fileexchange/11837-fast-and-robust-curve-intersections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-30T11:24:17.212343Z",
     "start_time": "2018-09-30T11:24:17.186826Z"
    }
   },
   "outputs": [],
   "source": [
    "def _rect_inter_inner(x1, x2):\n",
    "    n1 = x1.shape[0]-1\n",
    "    n2 = x2.shape[0]-1\n",
    "    X1 = np.c_[x1[:-1], x1[1:]]\n",
    "    X2 = np.c_[x2[:-1], x2[1:]]\n",
    "    S1 = np.tile(X1.min(axis=1), (n2, 1)).T\n",
    "    S2 = np.tile(X2.max(axis=1), (n1, 1))\n",
    "    S3 = np.tile(X1.max(axis=1), (n2, 1)).T\n",
    "    S4 = np.tile(X2.min(axis=1), (n1, 1))\n",
    "    return S1, S2, S3, S4\n",
    "\n",
    "\n",
    "def _rectangle_intersection_(x1, y1, x2, y2):\n",
    "    S1, S2, S3, S4 = _rect_inter_inner(x1, x2)\n",
    "    S5, S6, S7, S8 = _rect_inter_inner(y1, y2)\n",
    "\n",
    "    C1 = np.less_equal(S1, S2)\n",
    "    C2 = np.greater_equal(S3, S4)\n",
    "    C3 = np.less_equal(S5, S6)\n",
    "    C4 = np.greater_equal(S7, S8)\n",
    "\n",
    "    ii, jj = np.nonzero(C1 & C2 & C3 & C4)\n",
    "    return ii, jj\n",
    "\n",
    "\n",
    "def intersection(x1,y1,x2,y2):\n",
    "    \"\"\"\n",
    "    Intersections of curves.\n",
    "       Computes the (x,y) locations where two curves intersect.  The curves\n",
    "       can be broken with NaNs or have vertical segments.\n",
    "    usage:\n",
    "    x,y=intersection(x1,y1,x2,y2)\n",
    "    Example:\n",
    "    a, b = 1, 2\n",
    "    phi = np.linspace(3, 10, 100)\n",
    "    x1 = a*phi - b*np.sin(phi)\n",
    "    y1 = a - b*np.cos(phi)\n",
    "    x2=phi\n",
    "    y2=np.sin(phi)+2\n",
    "    x,y=intersection(x1,y1,x2,y2)\n",
    "    plt.plot(x1,y1,c='r')\n",
    "    plt.plot(x2,y2,c='g')\n",
    "    plt.plot(x,y,'*k')\n",
    "    plt.show()\n",
    "    \"\"\"\n",
    "    ii, jj = _rectangle_intersection_(x1, y1, x2, y2)\n",
    "    n = len(ii)\n",
    " \n",
    "    dxy1 = np.diff(np.c_[x1, y1], axis=0)\n",
    "    dxy2 = np.diff(np.c_[x2, y2], axis=0)\n",
    " \n",
    "    T = np.zeros((4, n))\n",
    "    AA = np.zeros((4, 4, n))\n",
    "    AA[0:2, 2, :] = -1\n",
    "    AA[2:4, 3, :] = -1\n",
    "    AA[0::2, 0, :]=dxy1[ii, :].T\n",
    "    AA[1::2, 1, :]=dxy2[jj, :].T\n",
    "\n",
    "    BB = np.zeros((4, n))\n",
    "    BB[0, :] = -x1[ii].ravel()\n",
    "    BB[1, :] = -x2[jj].ravel()\n",
    "    BB[2, :] = -y1[ii].ravel()\n",
    "    BB[3, :] = -y2[jj].ravel()\n",
    "\n",
    "    for i in range(n):\n",
    "        try:\n",
    "            T[:, i] = np.linalg.solve(AA[:, :, i], BB[:, i])\n",
    "        except:\n",
    "            T[:, i] = np.NaN\n",
    "\n",
    "    in_range = (T[0, :] >= 0) & (T[1, :] >= 0) & (T[0, :] <= 1) & (T[1, :] <= 1)\n",
    "\n",
    "    xy0 = T[2:, in_range]\n",
    "    xy0 = xy0.T\n",
    "    return xy0[:, 0], xy0[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ground curve function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-30T11:28:51.974255Z",
     "start_time": "2018-09-30T11:28:51.945984Z"
    }
   },
   "outputs": [],
   "source": [
    "def ground_curve(gamma=22, H=100, nu=0.3, E=1500000, D=10, c=200, phi=28,\n",
    "                f_ck=20, E_c=5000, nu_c=0.2, t_c=0.20, dis_sup=1.5, advance_rate=5):\n",
    "    \"\"\"\n",
    "    # --------------------------------\n",
    "    # Input values for rock/soil\n",
    "    # --------------------------------\n",
    "    gamma        - [kN/m³] - specific weight of the rock mass\n",
    "    H            - [m]     - overburden\n",
    "    nu           - [-]     - Poisson's ratio of the rock\n",
    "    E            - [kPa]   - Modulus of elasticity of the rock\n",
    "    D            - [m]     - Diameter of the tunnel\n",
    "    c            - [kPa]   - Cohesion\n",
    "    phi          - [deg]   - Friction angle\n",
    "    # --------------------------------\n",
    "    # Input values for support members\n",
    "    # --------------------------------\n",
    "    f_ck         - [MPa]   - Uniaxial compressive strength of the sprayed concrete\n",
    "    E_c          - [MPa]   - Young's modulus of the sprayed concrete\n",
    "    nu_c         - [-]     - Poisson's ratio of the sprayed concrete\n",
    "    t_c          - [m]     - Thickness of the sprayed concrete\n",
    "    dis_sup      - [m]     - Distance of the support member to the face\n",
    "    advance_rate - [m/day] - Rate of advance\n",
    "    \"\"\"\n",
    "    p_o = gamma * H  # [kPa]   - in-situ stress\n",
    "    Phi = np.deg2rad(phi)  # [rad]   - convertion from degrees to radians\n",
    "    p_i = np.linspace(0, p_o, 1000)  # [kPa]   - Support pressure (an array from zero to insitu stress)\n",
    "    sigma_cm = 2 * c * np.cos(Phi) / (1 - np.sin(Phi))  # [kPa] - the uniaxial strength of the rock mass\n",
    "    k = (1 + np.sin(Phi)) / (1 - np.sin(Phi))  # [-] - slope defined by the Mohr-Coulomb criterion\n",
    "    \n",
    "    # Analysis of tunnel behaviour:\n",
    "    \n",
    "    #############################\n",
    "    # a. Tunnel wall displacement\n",
    "    #############################\n",
    "    \n",
    "    p_cr = (2 * p_o - sigma_cm) / (1 + k) \n",
    "    # [kPa] - critical support pressure\n",
    "    # if the critical support pressure is smaller than the internal \n",
    "    # support pressure then failure does not occur\n",
    "    \n",
    "    r_o = D / 2                          \n",
    "    # [m] - radius of the tunnel\n",
    "    \n",
    "    u_ie = r_o * (1 + nu) / E * (p_o - p_i) \n",
    "    # [m] - inward radial elastic displacement (Pi is a variable)\n",
    "    \n",
    "    r_p = r_o * (2 * (p_o * (k - 1) + sigma_cm) / (1 + k) / ((k - 1) * p_i + sigma_cm))**(1 / (k - 1)) \n",
    "    # [m] - radius of the plastic zone\n",
    "    \n",
    "    u_ip = r_o * (1 + nu) / E * (2 * (1 - nu) * (p_o - p_cr) * (r_p / r_o)**2 - (1 - 2*nu) * (p_o - p_i))\n",
    "    # [m] - inward radial plastic displacement (Pi is a variable)\n",
    "    \n",
    "    x = np.where(p_i > p_cr, u_ie, u_ip)\n",
    "    \n",
    "    u_annot = r_o * (1+nu) / E * (p_o-p_cr)\n",
    "    # The abscissa of the ordinate: p_cr\n",
    "    \n",
    "    #####################################\n",
    "    # b. Logitudinal displacement profile\n",
    "    #####################################\n",
    "    \n",
    "    r_pm = r_o * ((2 * (p_o * (k-1) + sigma_cm)) / ((1+k) * sigma_cm))**(1/(k-1))\n",
    "    # Maximum plastic zone radius [m]\n",
    "    \n",
    "    u_im = r_o * (1+nu)/E*(2*(1-nu)*(p_o-p_cr)*(r_pm/r_o)**2-(1-2*nu)*(p_o))\n",
    "    # Maximum displacement [m] - r_p = r_pm; p_i = 0\n",
    "    \n",
    "    u_if = (u_im / 3) * np.exp(-0.15 * (r_pm / r_o))\n",
    "    # Displacement at the tunnel face (by Vlachopoulus and Diederichs) [m]\n",
    "    \n",
    "    # Displacement ahead of the face\n",
    "    \n",
    "    x_ = np.arange(-25, 80, 1)\n",
    "    # Distance from tunnel face (an array from -25m ahead and 80m behind the face)\n",
    "    # [m]\n",
    "    \n",
    "    u_ix_a = (u_if) * np.exp(x_ / r_o)\n",
    "    # Tunnel wall displacement ahead of the face (x < 0) [m]\n",
    "    \n",
    "    # Displacement behind the face\n",
    "    \n",
    "    u_ix_b = u_im*(1-(1-u_if/u_im) * np.exp((-3*x_/r_o) / (2*r_pm/r_o)))\n",
    "    # Tunnel wall displacement behind the face (x > 0) [m]\n",
    "    \n",
    "    x_disp = np.where(x_ < 0, u_ix_a, u_ix_b)\n",
    "    # x values for displacement\n",
    "    \n",
    "    ################################################\n",
    "    # c. Calculate the actual/target strenght of SCL\n",
    "    ################################################\n",
    "    \n",
    "    x_sup = np.arange(dis_sup, 80, 1)\n",
    "    # Distance from installation of SCL\n",
    "    \n",
    "    u_io = u_im*(1-(1-u_if/u_im) * np.exp((-3*x_sup/r_o) / (2*r_pm/r_o)))\n",
    "    # Tunnel wall displacement behind SCL x > distance support [m]\n",
    "    \n",
    "    # Calculate the time from advance rate\n",
    "    arr_disp = u_io - u_io[0]\n",
    "    inc = 24 / advance_rate\n",
    "    arr_hours = np.linspace(0, len(arr_disp)*inc, len(arr_disp))\n",
    "    \n",
    "    arr_days = arr_hours / 24\n",
    "\n",
    "    del_eps = arr_disp[1:] - arr_disp[:-1]\n",
    "\n",
    "    # Strength evolution of SCL acc. Aldrian\n",
    "    sigma_target = np.array([f_ck * 0.03 * time if time < 8 else f_ck * np.sqrt((time - 5) / (45 + 0.925 * time)) \n",
    "                            for time in arr_hours])\n",
    "    # sigma_target = sigma_target * 1000  # [kPa]\n",
    "\n",
    "    # Constants for rate of flow calculation\n",
    "    E_28 = 15000  # [MPa]\n",
    "    A = 0.0001  # [1/MPa*d^(1/3)]\n",
    "    B = 600  # [d]\n",
    "    eps_sh_inf = 0.00125\n",
    "    C_d_inf = 0.00009  # [1/MPa]\n",
    "    Q = 0.0001  # [1/MPa]\n",
    "\n",
    "    # delta C_t\n",
    "    C_t = A * arr_days**(1/3)\n",
    "    del_C_t = C_t[1:] - C_t[:-1]\n",
    "\n",
    "    # change of tempreture strain\n",
    "    eps_t = (-1*np.cos(np.deg2rad(arr_days[1:]**0.25*250))+1)*30/1000000\n",
    "    eps_t = np.insert(arr=eps_t, obj=0, values=0)\n",
    "    del_eps_t = eps_t[1:] - eps_t[:-1]\n",
    "\n",
    "    # change of elasticity in time\n",
    "    P_Ar1 = 1\n",
    "    P_Ar2 = 0.3\n",
    "    P_Ar3 = 0.2\n",
    "    E_t = E_28 * (arr_days[1:] / (P_Ar1 + P_Ar2 * arr_days[1:]))**P_Ar3\n",
    "\n",
    "    eps_sh = eps_sh_inf * arr_days / (arr_days + B)\n",
    "    del_eps_sh = eps_sh[1:] - eps_sh[:-1]\n",
    "\n",
    "    sigma_2 = [0]\n",
    "    eps_d = [0]\n",
    "    for i, t in enumerate(arr_days):\n",
    "        try:\n",
    "            if i == 0:\n",
    "                value_eps_d = (sigma_2[0] * C_d_inf - eps_d[0]) * (1 - np.exp(-del_C_t[i]/Q))\n",
    "                eps_d.append(value_eps_d)\n",
    "                value_sigma_2 = ((del_eps[i] - del_eps_sh[i] - del_eps_t[i] + eps_d[0] * \n",
    "                                 (1 - np.exp(-del_C_t[i]/Q)) + sigma_2[0] / E_t[i]) /\n",
    "                                 (C_d_inf * (1 - np.exp(-del_C_t[i]/Q)) + del_C_t[i] + (1 / E_t[i])))\n",
    "                sigma_2.append(value_sigma_2)\n",
    "            else:\n",
    "                value_eps_d = (sigma_2[i-1] * C_d_inf - eps_d[i-1]) * (1 - np.exp(-del_C_t[i]/Q))\n",
    "                eps_d.append(value_eps_d)\n",
    "                value_sigma_2 = ((del_eps[i] - del_eps_sh[i] - del_eps_t[i] + eps_d[i-1] * \n",
    "                                 (1 - np.exp(-del_C_t[i]/Q)) + sigma_2[i-1] / E_t[i]) /\n",
    "                                 (C_d_inf * (1 - np.exp(-del_C_t[i]/Q)) + del_C_t[i] + (1 / E_t[i])))\n",
    "                sigma_2.append(value_sigma_2)\n",
    "        except IndexError:\n",
    "            pass\n",
    "\n",
    "    sigma_actual = np.array(sigma_2)\n",
    "    \n",
    "    p_scmax = sigma_target / 2 * (1 - ((D/2 - t_c)**2 / (D/2)**2))\n",
    "    # maximum support pressure\n",
    "    \n",
    "    k_sc = E_t * ((D/2)**2 - (D/2 - t_c)**2) / (2 * (1 - nu_c**2) * (D/2 - t_c) * (D/2)**2)\n",
    "    # stiffness of the support\n",
    "    \n",
    "#     ratio_sc = p_scmax[1:] / k_sc\n",
    "#     # ration of the support\n",
    "    \n",
    "#     u_io = u_im*(1-(1-u_if/u_im) * np.exp((-3*dis_sup/r_o) / (2*r_pm/r_o)))\n",
    "#     # tunnel wall displacement at the time of support installation\n",
    "    \n",
    "#     u_iy = u_io + ratio_sc\n",
    "#     # displacement at the yield surface of support\n",
    "    \n",
    "    x_support = u_io\n",
    "    y_support = p_scmax\n",
    "    \n",
    "#     x_sup_intersect = np.linspace(u_io, u_iy, len(x))\n",
    "#     y_sup_intersect = np.linspace(0, p_scmax, len(x))\n",
    "    \n",
    "    # find the intersection of support & ground curves\n",
    "    x_int, y_int = intersection(x_support, y_support, x, p_i/1000)\n",
    "    \n",
    "    # find the radius of plastic zone at support installation\n",
    "    r_pl_sup = r_o * (2 * (p_o * (k - 1) + sigma_cm) / (1 + k) / ((k - 1) * y_int + sigma_cm))**(1 / (k - 1))\n",
    "    \n",
    "    fig, axs = plt.subplots(2, 2, dpi=100, figsize=(15, 10), edgecolor='w')\n",
    "    ax = axs.flatten()\n",
    "    if len(r_pl_sup) != 0:\n",
    "        if r_pl_sup < r_o:\n",
    "            r_pl_sup = '< r'\n",
    "        if r_pl_sup is str:\n",
    "            ax[0].plot(x, p_i/1000, 'r-', lw=1.5, markersize=3, label='Ground curve\\n$R_{pl}$ = %' % r_pl_sup)\n",
    "        else:\n",
    "            ax[0].plot(x, p_i/1000, 'r-', lw=1.5, markersize=3, label='Ground curve\\n$R_{pl}$ = %0.2f m' % r_pl_sup)\n",
    "    else:\n",
    "        ax1.plot(x, p_i/1000, 'r-', lw=1.5, markersize=3, label='Ground curve')\n",
    "    if len(y_int) != 0:\n",
    "        safety_factor = p_scmax.max()/y_int[0]\n",
    "        ax[0].plot(x_support, y_support, 'y-o', lw=1.5, markersize=3,\n",
    "                 label='Support curve\\nF.S.: %0.2f' % safety_factor)\n",
    "        ax[0].plot(x_int, y_int, 'ro')\n",
    "        ax[0].annotate('$P_{sup} = $%0.2f MPa' % y_int,\n",
    "                     # Annotate the critical point\n",
    "                     xy=(x_int, y_int),\n",
    "                     xytext=(x_int, y_int * 4),\n",
    "                     arrowprops=dict(arrowstyle='->',\n",
    "                                     facecolor='w',\n",
    "                                     connectionstyle=\"arc3,rad=-0.2\",\n",
    "                                     edgecolor='k'),\n",
    "                     fontsize=12)\n",
    "    else:\n",
    "        ax[0].plot(x_support, y_support, 'y-o', lw=1.5, markersize=3,\n",
    "                 label='Support curve\\nF.S.: N/A')\n",
    "    ax[0].set_title('Ground Reaction Curve')\n",
    "    ax[0].set_ylabel('Support Pressure $P_i\\,[MPa]$', fontsize=12)\n",
    "    ax[0].set_xlabel('Tunnel Wall Displacement $u_i\\,[m]$', fontsize=12)\n",
    "    ax[0].legend()\n",
    "\n",
    "    ax[1].plot(x_, x_disp, 'b-o', lw=1.5, markersize=3)\n",
    "    ax[1].set_title('Displacement Curve')\n",
    "    ax[1].set_ylabel('Distance from tunnel face $x\\,[m]$', fontsize=12)\n",
    "    ax[1].set_xlabel('Tunnel Wall Displacement $u_i\\,[m]$', fontsize=12)\n",
    "    ax[1].vlines(0, ymin=x_disp.min(), ymax=x_disp.max(), color='gray',\n",
    "               alpha=0.5)\n",
    "    # horizontal line at support distance\n",
    "    ax[1].vlines(dis_sup, ymin=x_disp.min(), ymax=x_disp.max(), linestyle='-.',\n",
    "                 color='red', alpha=0.5)\n",
    "    ax[1].hlines(0, xmin=x_.min(), xmax=x_.max(), color='gray', alpha=0.5)\n",
    "    \n",
    "    ax[2].plot(arr_days, sigma_target*1000, label='$\\sigma_{target}$')\n",
    "    ax[2].plot(arr_days, sigma_actual, label='$\\sigma_{actual}$')\n",
    "    ax[2].set_title('$\\sigma$ - time Curve')\n",
    "    ax[2].set_ylabel('$\\sigma\\,[kPa]$', fontsize=12)\n",
    "    ax[2].set_xlabel('Time [days]', fontsize=12)\n",
    "    ax[2].legend()\n",
    "    ax[3].axis('off')\n",
    "    plt.tight_layout()\n",
    "    del y_int, r_pl_sup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-30T11:28:53.602665Z",
     "start_time": "2018-09-30T11:28:52.515930Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0acffe8ec324f63acf8a9b838bf798f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HBox(children=(VBox(children=(Label(value='Soil Parameters'), FloatSlider(value=22.0, desc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f2f93c7705441ce8e2898d3d917a5ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gamma = widgets.FloatSlider(min=17, max=25, step=0.5, value=22, description='gamma')\n",
    "H = widgets.IntSlider(min=10, max=500, step=10, value=100, description='H')\n",
    "nu = widgets.FloatSlider(min=0.2, max=0.35, step=0.05, value=0.3, description='nu')\n",
    "E = widgets.IntSlider(min=50000, max=3000000, step=100000, value=1500000, description='E')\n",
    "D = widgets.IntSlider(min=5, max=15, step=1, value=10, description='D')\n",
    "c = widgets.IntSlider(min=10, max=1000, step=10, value=200, description='c')\n",
    "phi = widgets.IntSlider(min=15, max=35, step=1, value=28, description='phi')\n",
    "f_ck = widgets.IntSlider(min=20, max=40, step=5, value=20, description='f_ck')\n",
    "# E_c = widgets.IntSlider(min=3000, max=30000, step=1000, value=5000, description='E_c')\n",
    "# nu_c = widgets.FloatSlider(min=0.2, max=0.3, step=0.05, value=0.2, description='nu_c')\n",
    "t_c = widgets.FloatSlider(min=0, max=1, step=0.05, value=0.2, description='t_c')\n",
    "dis_sup = widgets.FloatSlider(min=0, max=10, step=0.5, value=1.5, description='dis_sup')\n",
    "advance_rate = widgets.FloatSlider(min=0, max=10, step=0.5, value=5, description='advance_rate')\n",
    "\n",
    "layout = widgets.Layout(align_items='center')\n",
    "\n",
    "soil_label = widgets.Label('Soil Parameters')\n",
    "support_label = widgets.Label('Support Parameters')\n",
    "\n",
    "list_VBox_widgets_soil = [widgets.VBox([soil_label, gamma, H, nu, E, D, c, phi], layout=layout)]\n",
    "list_VBox_widgets_support = [widgets.VBox([support_label, f_ck, # E_c, \n",
    "                                           t_c, dis_sup, advance_rate], layout=layout)]\n",
    "list_HBox_widgets = [widgets.HBox(list_VBox_widgets_soil + list_VBox_widgets_support)]\n",
    "accordion = widgets.Accordion(children=list_HBox_widgets)\n",
    "accordion.set_title(0, ' Variables')\n",
    "out = widgets.interactive_output(ground_curve,\n",
    "                                 {'gamma':gamma,\n",
    "                                 'H':H, \n",
    "                                 'nu':nu, \n",
    "                                 'E':E, \n",
    "                                 'D':D, \n",
    "                                 'c':c, \n",
    "                                 'phi':phi, \n",
    "                                 'f_ck':f_ck, \n",
    "                                 # 'E_c':E_c, \n",
    "                                 # 'nu_c':nu_c, \n",
    "                                 't_c':t_c, \n",
    "                                 'dis_sup':dis_sup,\n",
    "                                 'advance_rate':advance_rate})\n",
    "\n",
    "display(accordion, out)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.0"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "0143f54d08bf4f7693c56f07ba41098f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "H",
       "layout": "IPY_MODEL_2f6ff5d6234e400097761c046fc512f0",
       "max": 500,
       "min": 100,
       "step": 10,
       "style": "IPY_MODEL_170974fc2ec14972be2feada4f396f66",
       "value": 320
      }
     },
     "06b491d6e9b14c84ba1f231b2c4a518f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "0a41b4f36b244bb5a4d864a70925ba72": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "0a8eb558514042b2a262eb96c217c8af": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "170974fc2ec14972be2feada4f396f66": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "1bc90e9cbd0840eda99a7f2c53a98951": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "20b1b09b311a4c8cb27197970456f622": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "231e71d1668c46b5b7f39762500899ab": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "nu",
       "layout": "IPY_MODEL_5832d246fdb044f99886596bf584c246",
       "max": 0.35,
       "min": 0.2,
       "step": 0.05,
       "style": "IPY_MODEL_c085aae2281a46fa871da9b41e8ea2f1",
       "value": 0.25
      }
     },
     "24804cc05203449c952a23dd58d2b420": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "26a9ea2811884790b6330ea5a0efb8fc": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "2be3dae46319420ba245cdd817c8aa3f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "2e3717227f3b49d69058a5ce7702ec7b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "c",
       "layout": "IPY_MODEL_26a9ea2811884790b6330ea5a0efb8fc",
       "max": 3000,
       "min": 10,
       "step": 10,
       "style": "IPY_MODEL_f71b67605ec64f6fbeb0d6370008f704",
       "value": 690
      }
     },
     "2f6ff5d6234e400097761c046fc512f0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "3bb0b6e6928a453dbc3e70014a45ac86": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "D",
       "layout": "IPY_MODEL_9a3d4e50263a4a08b69aca3ade6d57bd",
       "max": 15,
       "min": 5,
       "style": "IPY_MODEL_9929df12691e4ee9bd227ba2ef9ad83d",
       "value": 10
      }
     },
     "3bbdcb7586fe448a9b86934cf5975a8a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "3e9d92386c3e418796053f89d0af95c2": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "3edc0f4668b14af49800a5395c41f4ff": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8043c479f5f84873b573c7787b2c9676",
       "outputs": [
        {
         "data": {
          "image/png": "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\n",
          "text/plain": "<Figure size 1500x600 with 2 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "3fd5d944904142f68570b853434cd993": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "phi",
       "layout": "IPY_MODEL_c29b78a18fb44442b7d1c645d271b6d1",
       "max": 35,
       "min": 15,
       "style": "IPY_MODEL_6aec563321d54d9997ff70674517fc4b",
       "value": 26
      }
     },
     "44fa4077674c4efd8be5136a0a986c4b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "533ac3576b1f42ef919c97ad20f2419c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "5832d246fdb044f99886596bf584c246": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "6aec563321d54d9997ff70674517fc4b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "6f9b01e200ef4a62a94660b2789adeb3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "LabelModel",
      "state": {
       "layout": "IPY_MODEL_aa1e35e3649f466d9a8bd563d98b21d5",
       "style": "IPY_MODEL_d8f00e28ca0146239fe6f1fa7276b363",
       "value": "Support Parameters"
      }
     },
     "79e3fb5ec5f04c2e8ac977e551bdd9c5": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "8043c479f5f84873b573c7787b2c9676": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "84b65414c00a4d8b8efe5759ca2cc1d5": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "84cf4fad2ce34a3cbeb995607f209c4b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "9929df12691e4ee9bd227ba2ef9ad83d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "9a3d4e50263a4a08b69aca3ade6d57bd": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "9ee7ac36da804bc68bc07f26037e58d8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "E",
       "layout": "IPY_MODEL_0a41b4f36b244bb5a4d864a70925ba72",
       "max": 3000000,
       "min": 50000,
       "step": 100000,
       "style": "IPY_MODEL_3e9d92386c3e418796053f89d0af95c2",
       "value": 1800000
      }
     },
     "a46a22c5daf14818a6cff77d7925df1e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "gamma",
       "layout": "IPY_MODEL_0a8eb558514042b2a262eb96c217c8af",
       "max": 25,
       "min": 20,
       "step": 0.5,
       "style": "IPY_MODEL_79e3fb5ec5f04c2e8ac977e551bdd9c5",
       "value": 22
      }
     },
     "a61fbb01eb4f4810941cbddd625f38c3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "a91de59f940b47769631c484c88eb84e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_6f9b01e200ef4a62a94660b2789adeb3",
        "IPY_MODEL_fa37cc2fef6549a69eb67f31a8d494ac",
        "IPY_MODEL_d587ff36cc9c44918f1fb0160d0c47a1",
        "IPY_MODEL_a9370bbdaf754fec9e16eab829a205b2",
        "IPY_MODEL_dde97da3f3c34a8491eb50b03f620725"
       ],
       "layout": "IPY_MODEL_ed03445fc9c94e3f8808214c55b6e2ba"
      }
     },
     "a9370bbdaf754fec9e16eab829a205b2": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "t_c",
       "layout": "IPY_MODEL_84b65414c00a4d8b8efe5759ca2cc1d5",
       "max": 1,
       "step": 0.05,
       "style": "IPY_MODEL_1bc90e9cbd0840eda99a7f2c53a98951",
       "value": 0.3
      }
     },
     "a96810c9b4644feea9445d53f3a87ea8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_b7439948a9e84c7a803aa733c51e6fa4",
        "IPY_MODEL_a91de59f940b47769631c484c88eb84e"
       ],
       "layout": "IPY_MODEL_533ac3576b1f42ef919c97ad20f2419c"
      }
     },
     "a9ab7d7d00bb495b872d070598de8849": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "aa1e35e3649f466d9a8bd563d98b21d5": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b1c032974dd1437fa5201b470c3adbc8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "LabelModel",
      "state": {
       "layout": "IPY_MODEL_06b491d6e9b14c84ba1f231b2c4a518f",
       "style": "IPY_MODEL_a61fbb01eb4f4810941cbddd625f38c3",
       "value": "Soil Parameters"
      }
     },
     "b7439948a9e84c7a803aa733c51e6fa4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_b1c032974dd1437fa5201b470c3adbc8",
        "IPY_MODEL_a46a22c5daf14818a6cff77d7925df1e",
        "IPY_MODEL_0143f54d08bf4f7693c56f07ba41098f",
        "IPY_MODEL_231e71d1668c46b5b7f39762500899ab",
        "IPY_MODEL_9ee7ac36da804bc68bc07f26037e58d8",
        "IPY_MODEL_3bb0b6e6928a453dbc3e70014a45ac86",
        "IPY_MODEL_2e3717227f3b49d69058a5ce7702ec7b",
        "IPY_MODEL_3fd5d944904142f68570b853434cd993"
       ],
       "layout": "IPY_MODEL_ed03445fc9c94e3f8808214c55b6e2ba"
      }
     },
     "c085aae2281a46fa871da9b41e8ea2f1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "c29b78a18fb44442b7d1c645d271b6d1": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "ca48fdc8c22048729043464b2424af12": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "d587ff36cc9c44918f1fb0160d0c47a1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "E_c",
       "layout": "IPY_MODEL_ca48fdc8c22048729043464b2424af12",
       "max": 30000,
       "min": 3000,
       "step": 1000,
       "style": "IPY_MODEL_3bbdcb7586fe448a9b86934cf5975a8a",
       "value": 5000
      }
     },
     "d8f00e28ca0146239fe6f1fa7276b363": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "dde97da3f3c34a8491eb50b03f620725": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "dis_sup",
       "layout": "IPY_MODEL_20b1b09b311a4c8cb27197970456f622",
       "max": 10,
       "step": 0.5,
       "style": "IPY_MODEL_f0f8c605ac00492ebf119f488dce7900",
       "value": 5
      }
     },
     "e140e0bb788c4187a7c752bce06c2d5a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "AccordionModel",
      "state": {
       "_titles": {
        "0": " Variables"
       },
       "children": [
        "IPY_MODEL_a96810c9b4644feea9445d53f3a87ea8"
       ],
       "layout": "IPY_MODEL_a9ab7d7d00bb495b872d070598de8849",
       "selected_index": null
      }
     },
     "ed03445fc9c94e3f8808214c55b6e2ba": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.1.0",
      "model_name": "LayoutModel",
      "state": {
       "align_items": "center"
      }
     },
     "ef7bf2190973495ab50ae0f9a87b9a37": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "nu_c",
       "layout": "IPY_MODEL_44fa4077674c4efd8be5136a0a986c4b",
       "max": 0.3,
       "min": 0.2,
       "step": 0.05,
       "style": "IPY_MODEL_2be3dae46319420ba245cdd817c8aa3f",
       "value": 0.2
      }
     },
     "f0f8c605ac00492ebf119f488dce7900": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "f71b67605ec64f6fbeb0d6370008f704": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "fa37cc2fef6549a69eb67f31a8d494ac": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.4.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "f_ck",
       "layout": "IPY_MODEL_84cf4fad2ce34a3cbeb995607f209c4b",
       "max": 40,
       "min": 20,
       "step": 5,
       "style": "IPY_MODEL_24804cc05203449c952a23dd58d2b420",
       "value": 20
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}