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 "metadata": {
  "name": "",
  "signature": "sha256:9e373112e48f07ee80b6b4af3f674669c699f5259766125b19738d8e5f5cd0b1"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter6-Soil Compaction"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex2-pg127"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "#calculate maximum dry density and optimum moisture content\n",
      "##initialisation of variables\n",
      "G= 2.6\n",
      "LL= 20.\n",
      "P= 20.\n",
      "##calclations\n",
      "R= (4804574.*G-195.55*(LL)**2+156971*(P)**0.5-9527830)**0.5\n",
      "n= (1.195e-4)*((LL)**2)-1.964*G-(6.617e-5)*(P)+7.651\n",
      "w= math.e**n\n",
      "##results\n",
      "print'%s %.1f %s'% ('maximum dry density = ',R,' kg/m^3 ')\n",
      "print'%s %.2f %s'%('optimum moisture content = ',w,' ')\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "maximum dry density =  1894.2  kg/m^3 \n",
        "optimum moisture content =  13.34  \n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex3-pg143"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "#calculate dry unit weight of compaction in the field and dry unit weight of compaction in the field\n",
      "##initialisation of variables\n",
      "do= 1570. ##kg/m^3\n",
      "mo= 0.545 ##kg\n",
      "M1= 7.59 ##kg\n",
      "M2= 4.78 ##kg\n",
      "M3= 3.007 ##kg\n",
      "w= 0.102 ##\n",
      "dmax= 19. ##KN/m^3\n",
      "##calculations\n",
      "Ms= M1-M2\n",
      "Mc= Ms-mo\n",
      "Vh= Mc/do\n",
      "Dc= M3/Vh\n",
      "Du= Dc*9.81/1000.\n",
      "f= Du/(1.+w)\n",
      "Rc= f*100./dmax\n",
      "##results\n",
      "print'%s %.2f %s'% ('dry unit weight of compaction in the field = ',f,' kN/m^3 ')\n",
      "print'%s %.1f %s'% ('relative compaction in the field = ',Rc,'')\n",
      "#calculate the value of gamma and plot the graph\n",
      "%matplotlib inline\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore')\n",
      "import math\n",
      "from math import log\n",
      "import numpy\n",
      "from math import tan\n",
      "import matplotlib\n",
      "from matplotlib import pyplot\n",
      "#given\n",
      "p=numpy.array([6,8,9,11,12,14])\n",
      "e=numpy.array([14.80,17.45,18.52,18.9,18.5,16.9])\n",
      "\n",
      "#calculations\n",
      "\n",
      "\n",
      "#results\n",
      "\n",
      "pyplot.plot(p,e)\n",
      "pyplot.xlabel('gamma')\n",
      "pyplot.ylabel('weight ,w')\n",
      "pyplot.title('Graph of gamma vs w')\n",
      "pyplot.show()\n",
      "print('look at the axis reverse in text book')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dry unit weight of compaction in the field =  18.55  kN/m^3 \n",
        "relative compaction in the field =  97.7 \n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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WJIoLgMmSxmbPdwLOlbQa8EDZIuvk3nsPTjwxJYmVV847GjPrzFo160lSD6Av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\nbAw8GhEfR8QiYCywd84xAU0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       "text": [
        "<matplotlib.figure.Figure at 0x566c2f0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "look at the axis reverse in text book\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex4-pg155"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "#calculate sustainabilty number\n",
      "##initialisation of variables\n",
      "D1= 0.36 ##mm\n",
      "D2= 0.52 ##mm\n",
      "D5= 1.42 ##mm\n",
      "##calculations\n",
      "Sn= 1.7*(math.sqrt((3./(D5)**2)+(1./(D2)**2)+(1./(D1)**2)))\n",
      "##results\n",
      "print'%s %.1f %s'% ('sustainabilty number = ',Sn,' ')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "sustainabilty number =  6.1  \n"
       ]
      }
     ],
     "prompt_number": 1
    }
   ],
   "metadata": {}
  }
 ]
}