Slope Stabilization - Meander.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Full Bank Stabilization Estimate\n",
    "\n",
    "I located the steepest bank slope at cross section 4:\n",
    "\n",
    "![Representative](files/bend2.png)\n",
    "Zooming in to slope\n",
    "![closeup](files/closeup2.png)\n",
    "Estimate the slope:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bank slope: -0.5043478260869569\n"
     ]
    }
   ],
   "source": [
    "top=(2.5,40.6)\n",
    "bottom=(14.0,34.8)\n",
    "slope = (top[1]-bottom[1])/(top[0]-bottom[0])\n",
    "print(\"Bank slope:\",slope)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The channel bottom is relatively flat. I'll assume it is completely flat and use a target slope of 1:2 to find the outer edge of the re-enforced bank toe. This calculation yields:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New bank toe:  14.100000000000009\n"
     ]
    }
   ],
   "source": [
    "target_slope = 1/2\n",
    "new_bottom_x = (top[1]-bottom[1])/target_slope + top[0]\n",
    "print(\"New bank toe: \",new_bottom_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the proposed new bank on an estimate of the current channel profile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "thickness = 0.6096# 2ft\n",
    "fig = plt.figure(figsize=(22,4))\n",
    "plt.plot([0.0,top[0],bottom[0],bottom[0]+2*thickness],[top[1],top[1],bottom[1],bottom[1]],'g',[thickness+top[0],thickness+new_bottom_x],[top[1],bottom[1]],'r')\n",
    "plt.plot([top[0],thickness+top[0]],[top[1],top[1]],'r')\n",
    "#plt.plot([0.0,top[0],bottom[0],27.5,30,55],[top[1],top[1],bottom[1],36.1,36.7,38.25],'g',[top[0],new_bottom_x],[top[1],bottom[1]],'r')\n",
    "#plt.plot([bottom[0],bottom[0]+10.67/2,bottom[0]+10.67],[bottom[1],bottom[1]+2.67,bottom[1]],'b')\n",
    "#plt.plot([bottom[0]-10.67/2.,bottom[0],bottom[0]+10.67/2.],[bottom[1],bottom[1]+2.67,bottom[1]],'b-.')\n",
    "plt.legend(['existing profile','re-enforced bank'])\n",
    "plt.axis('equal')\n",
    "plt.title('Riprap');\n",
    "plt.savefig('riprap.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the fill volume per foot along the bank:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rate of fill:  400.1556904167418 cubic feet per foot\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "ft_per_m=3.2808399\n",
    "face_length=math.sqrt((top[0]-bottom[0])**2 + (top[1]-bottom[1])**2)\n",
    "face_diag = math.sqrt((top[0]-(bottom[0]+thickness))**2+(top[1]-bottom[1])**2)\n",
    "S = (face_diag+face_length+thickness)/2.0\n",
    "par_area = 2.0*math.sqrt(S*(S-thickness)*(S-face_length)*(S-face_diag))#area of parallelogram for blanket thickness\n",
    "volume_per_ft = (abs(0.5*(top[1]-bottom[1])*(top[0]-new_bottom_x))+par_area)* (ft_per_m**2)\n",
    "print(\"Rate of fill: \", volume_per_ft, \"cubic feet per foot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total volume of riprap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fill volume:  22230.87168981899 cubic yards\n",
      "Cost at $50/cubic yard: $1111543.58\n"
     ]
    }
   ],
   "source": [
    "volume = 1500*volume_per_ft/(3**3)\n",
    "print(\"Fill volume: \",volume, \"cubic yards\")\n",
    "print(\"Cost at $50/cubic yard: ${0:.2f}\".format(volume*50.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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