path: root/Principles_Of_Geotechnical_Engineering_by_B._M._Das/Chapter11.ipynb

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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter11-Compressibility of Soil"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex1-pg303"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#evaluvate The elastic settlement at the centre of foundation\n",
      "Tz=150.\n",
      "b=1.\n",
      "l=2.\n",
      "z=5.*b\n",
      "Es= (10000*2 + 8000*1 +12000*2)/5\n",
      "a=4.\n",
      "H=z\n",
      "m=l/b\n",
      "n=2.*H/b\n",
      "F1=0.641 ##from tables 11.1 and 11.2\n",
      "F2=0.031\n",
      "u=0.3\n",
      "Is= F1 + ((2.-u)/(1.-u))*F2\n",
      "If=0.71 ##from  table 11.3\n",
      "Sef= Tz *a*b/l *(1-u**2)*Is*If/Es\n",
      "Ser=0.93*Sef\n",
      "print'%s %.3f %s'%('The elastic settlement at the centre of foundation =',Ser,'m')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The elastic settlement at the centre of foundation = 0.012 m\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex2-pg312"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#calculate the value of pressure\n",
      "#find the value of e and plot the graph\n",
      "## one value of e is done \n",
      "Gs=2.75\n",
      "A=30.68\n",
      "Ms=128.\n",
      "p=1.\n",
      "Hs=Ms/(A*Gs*p)\n",
      "H=2.540\n",
      "Hv=H-Hs\n",
      "e=Hv/Hs\n",
      "print'%s %.3f %s'%('the value of e for give values =',e,'')\n",
      "import math\n",
      "%matplotlib inline\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore')\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([0,0.5,1,2,4,8,16,32])\n",
      "e=numpy.array([0.671,0.637,0.622,0.599,0.572,0.529,0.464,0.390])\n",
      "e1=.9\n",
      "e2=.8\n",
      "sig1=4.\n",
      "sig2=2.\n",
      "#calculations\n",
      "Cc=(e1-e2)/log(sig1/sig2)\n",
      "\n",
      "#results\n",
      "print '%s %.2f %s'%('The value of Cv (cm^2/sec) = ',Cc,'')\n",
      "pyplot.plot(p,e)\n",
      "pyplot.xlabel('Pressure (ton/ft^2)')\n",
      "pyplot.ylabel('void ratio ,e')\n",
      "pyplot.title('Graph of pressure vs void ratio')\n",
      "pyplot.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "the value of e for give values = 0.674 \n",
        "The value of Cv (cm^2/sec) =  0.14 \n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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GxNRm9rsLeA+4sihRvAgMjYgWnyjgRGG1YMkSeOCBxiaqiMZ+jS9/2f0a1vmq\naa6nYcBzETEzIhaTnrc9qpn9TgD+DrzRzDZ/77Ka17Mn1NXBhRfC88+npqn+/eHUU2GtteCb34Qb\nbvCIcKtelUwUA4FXipZnZes+JmkgKXlckq0qrh4EcLekSZKOrmCcZp1Ggs03hzPOgEcfTRMUDh8O\nV1wBAwemTvBLL4XZs/OO1KxRzwoeu5w2oYuA0yIiJIlP1iCGR8RcSf2BuyRNi4j7mx6gvr7+49d1\ndXXU1dUtX9RmnWjgQPj+99PPO+809mv85Cfw+c839mt88Yvu17D2a2hooKGhod3lK9lHsR1QHxEj\ns+XTgaXFHdqSXqAxOaxJ6qc4OiJuaXKsM4FFEXF+k/Xuo7AuafHidOdUoV9jhRUak8aOO6bmLLP2\nqqbO7J6kzuwRwBzgEZrpzC7a/0rgnxFxk6S+QI+IWChpZeBO4KyIuLNJGScK6/IiUhNVIWm89FJ6\nQt+oUbDHHtCvX94RWq2pmkSRBbMnjbfHXhER50oaDRARlzXZtzhRbADclG3qCfwlIs5t5vhOFNbt\nvPJK6hAfOxYeeijdOVUYr7H22nlHZ7WgqhJFpTlRWHf39ttpttuxY+GOO9IU6YUmqsGD3a9hzXOi\nMOumPvwQ/vWvxiaq3r0bk8bw4dCjR94RWrVwojAzImDy5MakMWsW7L13Shq77w4rr5x3hJYnJwoz\nW8ZLLzX2azzySJrpttCvMWBA3tFZZ3OiMLOS3nqrsV9j/PjUl1FoovrCF/KOzjqDE4WZle2DD6Ch\nISWNW25JTVKFeai23979Gl2VE4WZtUsEPP54Y7/G3Lmwzz4paey2G/Ttm3eE1lGcKMysQ7z4YmO/\nxqRJsMsuKWnssw985jN5R2fLw4nCzDrc/PkwblxKGnfdleaeKvRrbLxx3tFZWzlRmFlFffAB3HNP\nY7/Gpz/dmDS23TbNS2XVzYnCzDrN0qWpWarQr/Hmm439GrvuCn365B2hNceJwsxyU3gw09ix8MQT\n8JWvNPZrrLlm3tFZgROFmVWFefPS88LHjoW774Ytt2xsotpww7yj696cKMys6vzf/8GECSlp/POf\nsPrqjUljm23cr9HZnCjMrKotXZqmESn0ayxYkKYS2W8/GDECVlop7wi7PicKM6spM2Y0Jo2nnkqd\n4KNGpUkM11gj7+i6JicKM6tZb7wBt96aOsQnTICtt25sotpgg7yj6zqcKMysS3j//dQJXujX+Mxn\nGpPG0KHr5CsFAAAMbElEQVTu11geThRm1uV89BE8/HBjE9XChalPY7/90i24K66Yd4S1xYnCzLq8\n6dMbk8Z//pMmLSz0a6y2Wt7RVT8nCjPrVl57LfVrjB2bpkz/0pcam6gGDco7uurkRGFm3da77zb2\na9x6K6y9dmPS2HprUNkfjV2bE4WZGalfY+LExiaq999vfChTXR307p13hPlxojAzayICpk1rTBrT\npsEee6SkseeesOqqeUfYudqaKCp6g5mkkZKmSZoh6dQS+20jaYmkA9pa1sysNRJsuimcdlqqZUyd\nmkaB/+UvsN56aZDf//4vvPxy3pFWp4rVKCT1AKYDuwKzgUeBQyNiajP73QW8B1wZETe2oaxrFGa2\nXBYtgjvvTIP8br0V1l23sV9jq626Zr9GNdUohgHPRcTMiFgMXAeMama/E4C/A2+0o6yZ2XLp1w++\n9jW46ip49VX4zW/SOI0DD0x3TZ1wQuogX7w470jzU8lEMRB4pWh5VrbuY5IGkhLAJdmqQvWg1bJm\nZh2tZ0/YaSc4//w0B9W4cenOqZ/+FAYMgMMOg+uvh3feyTvSztWzgscup03oIuC0iAhJAgpVobLb\nk+rr6z9+XVdXR11dXRtCNDNrngSbbZZ+fvITmDMnTSVy9dVw9NGw/faNo8PXXTfvaEtraGigoaGh\n3eUr2UexHVAfESOz5dOBpRExpmifF2hMDmuS+imOBl5vrWy23n0UZtbpFi6E8ePTHVTjxqUmqkK/\nxhZbVH+/RtXcHiupJ6lDegQwB3iEZjqki/a/EvhnRNxUblknCjPL2+LF8MADjY+AjWgcr/HlL0Ov\nXnlHuKyq6cyOiCXA8cB4YApwfURMlTRa0uj2lK1UrGZm7dWrF+yyC1x4YeMzw/v3h1NPhbXWgm9+\nE264IdVCapUH3JmZVcisWalfY+xYePBBGD481TT23RcG5nh7TtU0PXUGJwozqxXvvAN33JGSxu23\nw+c/n5LG/vvDF7/YubE4UZiZVbnFi+Ff/0pJ48034a9/7dzzO1GYmVlJVdOZbWZmXYMThZmZleRE\nYWZmJTlRmJlZSU4UZmZWkhOFmZmV5ERhZmYlOVGYmVlJThRmZlaSE4WZmZXkRGFmZiU5UZiZWUlO\nFGZmVpIThZmZleREYWZmJTlRmJlZSU4UZmZWkhOFmZmV5ERhZmYlVTRRSBopaZqkGZJObWb7KElP\nSnpC0mOSvlK0baakp7Jtj1QyTjMza1nFEoWkHsDvgJHAYOBQSZs22e3uiNgyIoYARwK/L9oWQF1E\nDImIYZWKM08NDQ15h7BcHH++ajn+Wo4daj/+tqpkjWIY8FxEzIyIxcB1wKjiHSLi3aLFfsCbTY6h\nCsaXu1r/Y3P8+arl+Gs5dqj9+NuqkoliIPBK0fKsbN0nSNpf0lTgduDEok0B3C1pkqSjKxinmZmV\n0LOCx46ydoq4GbhZ0peBa4BNsk3DI2KupP7AXZKmRcT9FYrVzMxaoIiyPs/bfmBpO6A+IkZmy6cD\nSyNiTIkyzwPDImJek/VnAosi4vwm6ysTvJlZFxcRZTftV7JGMQnYSNIgYA5wMHBo8Q6SPg+8EBEh\naWuAiJgnqS/QIyIWSloZ2B04q+kJ2vKLmplZ+1QsUUTEEknHA+OBHsAVETFV0uhs+2XAAcDhkhYD\ni4BDsuJrATdJKsT4l4i4s1KxmplZyyrW9GRmZl1DzY7Mbm0wX7WrpQGFkv4o6TVJTxetW13SXZKe\nlXSnpFXzjLGUFuKvlzQru/5PSBqZZ4ylSFpX0r2S/iPpGUknZutr4j0oEX9NvAeSVpL0sKTJkqZI\nOjdbX/XXv0Tsbbr2NVmjyAbzTQd2BWYDjwKHRsTUXANrA0kvAkMjYn7esbQmuyNtEfCniNg8W/cr\n4M2I+FWWqFeLiNPyjLMlLcR/JrAwIi7INbgySFoLWCsiJkvqBzwG7A8cRQ28ByXiP4jaeQ/6RsR7\nknoCDwCnAPtRG9e/udhH0IZrX6s1ilYH89WImuiMz25LfqvJ6v2Aq7PXV5P+41elFuKH2rn+r0bE\n5Oz1ImAqaUxSTbwHJeKH2nkP3ste9ib1ub5F7Vz/5mKHNlz7Wk0UZQ3mq3K1PqBwQES8lr1+DRiQ\nZzDtdEI219gV1dhs0JzsLsIhwMPU4HtQFP9D2aqaeA8krSBpMuk63xsR/6FGrn8LsUMbrn2tJora\nay9b1vBsjqs9geOy5pGaFKn9stbek0uAzwFbAXOB80vvnr+s2eZG4KSIWFi8rRbegyz+v5PiX0QN\nvQcRsTQitgLWAXaStEuT7VV7/ZuJvY42XvtaTRSzgXWLltcl1SpqRkTMzf59A/gHqTmtlryWtT0j\naW3g9ZzjaZOIeD0ywOVU+fWX1IuUJK7JZjOAGnoPiuL/cyH+WnsPACLibeA2YCg1dP3hE7F/qa3X\nvlYTxceD+ST1Jg3muyXnmMomqa+kT2WvCwMKny5dqurcAhyRvT4CuLnEvlUn+49d8FWq+PorDSi6\nApgSERcVbaqJ96Cl+GvlPZC0ZqFpRlIfYDfgCWrg+rcUeyHBZVq99jV51xOApD2Bi2gczHduziGV\nTdLnSLUIaBxQWLXxS7oW2BlYk9TO+d/AWOBvwHrATOCgiFiQV4ylNBP/mUAdqdodwIvA6KL25qoi\naUfgX8BTNDZvnA48Qg28By3E/xPSTA1V/x5I2pzUWb1C9nNNRJwnaXWq/PqXiP1PtOHa12yiMDOz\nzlGrTU9mZtZJnCjMzKwkJwozMyvJicLMzEpyojAzs5KcKMzMrCQnCqtqkj7KpkF+WtLfskFDVU/S\nZyTdlr3eMhv3s7zHvFTS9pK+kE0b/ZikDSQd2sy+a0t6LtunX9H6PpJukzQ1m/L73KJtJ0r61vLG\naV2PE4VVu/ciYkg2PfiHwPeKN2ZTJ3eKNp7reOCq7PUQYK8OCGFb0mSA+wM3RMRQ0mCvw4p3ykb9\n/wP4EWmw1d+bxP6riNg0i2t40bMIrgRO6IA4rYtxorBacj+woaSdJd0vaSzwTDY75nmSHslmwzwG\nPv5W/a+iGsnwbN+rsuWnJJ2U7dsgaWj2es3seSFIOlLSLZImAHdl06/8UelhMI9L2q+FWL8O3JZN\nMfNz4OAsjgOVHnhzcxbrxGz0bOFhMn9UesjP85I+/tCWtCnwLLAHcBLwfUn3AOcCX86OfVKWEP4K\n/DIi/hERvyVNNfEHgIh4PyLuy14vBh4nm3k5m2hwnqTNOuLNsq6j076NmS2P7ANwL2BctmoIsFlE\nvJQlhgURMUzSisADku4EvgbcERHnZPMNrZyV+2zRA4xWyY5XavbPIcDmEbFA0jnAhIj4djaHzsOS\n7i6a87/woJ6PCusk/Yz0kKrCk93+F3gsIvZXmoX0T9k5ADYGdgFWAaZLujgiPiLNMnx7RNwu6VKy\nh85I2hk4JSL2LYq3+DURcXEL13TVbN/i+aMeAXYC/tNcGeueXKOwatdH0hOkpxjOBP5IeuDKIxHx\nUrbP7sDh2X4PAasDG2ZljlJ6mt0W2dTWzwMbSPqtpD2AT0zX3YK7iubw2R04LTvXvcCKfHImY4D1\nSVM3F4hPPiRmOHANQETcC6yRNRcFcFtELI6IeaTZSAvPONgduKPJMYv/bZMs8V4L/CYiZhZtmgMM\nas8xretyjcKq3fvZczs+lioHvNtkv+Mj4q6mhZWe87EPcJWkCyLiGklbkppwvkd6HOd3gCU0fnFa\nqclhmp7raxExo5W4iz/Am6uptPQB/2HR64+AnpL6AqtGxKutnLMtfg9Mz5qmmsblCeDsE1yjsK5g\nPHBsocNW0sZZX8J6wBsRcTlpzv2tJa0B9IiIm4Cf0djkMxP4Uvb6662c68TCgqQhzezzElA8jfNC\n4FNFy/cD38jK12UxLqT55CFSU9Q9LcTT9NitkvQLUtPWfzWzeW3StTD7mBOFVbvmvt027U+4HJgC\nPC7padLTu3qSphKfLOlxUs3hIlLH7b1Z09E1pOm6AX5N6iB+HFij6PhNz3U20CvrCH8GOGuZ4NI3\n/55KzxqB1EQ1uNCZDdQDQyU9CZxD4zMNmp6r8HpPPtnsVLztSeCj7HbZk5rG0pSkdUhTfG9Kul5P\nSPpO0S7DSInM7GOeZtysAiTVA1Mj4voOONZjwLCsU7tiso79CRGxTSXPY7XHicKsAiT1B66OiI4Y\nP9EpJJ0IzI+IP+cdi1UXJwozMyvJfRRmZlaSE4WZmZXkRGFmZiU5UZiZWUlOFGZmVpIThZmZlfT/\nAaONSfy0Nv4aAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5b0e130>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex3-pg321"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#find the value compression index\n",
      "import math\n",
      "%matplotlib inline\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore')\n",
      "#calculate Compression index\n",
      "e11=0.9\n",
      "e21=0.8\n",
      "T2=4.\n",
      "T1=2.\n",
      "Cc= (e11-e21)/math.log10(T2/T1) ## from loading branch\n",
      "e1=0.67\n",
      "e2=0.655\n",
      "Cs=(e1-e2)/math.log10(T2/T1)\n",
      "k=Cs/Cc\n",
      "T3=12.\n",
      "e3=e11-Cc*math.log10(T3/T1)\n",
      "print'%s %.2f %s'%('Compression index Cc= ',Cc,'')\n",
      "print'%s %.2f %s'%(' Cs/Cc = ',k,'')\n",
      "print'%s %.2f %s'%(' e3 = ',e3,'')\n",
      "#calculate the value of Cv\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([.25,.5,1.,2.,4.,8.,16.,8.,4.,2.])\n",
      "e=numpy.array([1.03,1.02,0.98,0.91,0.79,0.71,0.62,0.635,0.655,0.67])\n",
      "e1=.9\n",
      "e2=.8\n",
      "sig1=4.\n",
      "sig2=2.\n",
      "#calculations\n",
      "Cc=(e1-e2)/log(sig1/sig2)\n",
      "\n",
      "#results\n",
      "print 'The value of Cv (cm^2/sec) = ',Cc\n",
      "pyplot.plot(p,e)\n",
      "pyplot.xlabel('Pressure (ton/ft^2)')\n",
      "pyplot.ylabel('void ratio ,e')\n",
      "pyplot.title('Graph of pressure vs void ratio')\n",
      "pyplot.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Compression index Cc=  0.33 \n",
        " Cs/Cc =  0.15 \n",
        " e3 =  0.64 \n",
        "The value of Cv (cm^2/sec) =  0.144269504089\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lhvBFi/KOzszaqpg2iiNIJYk1gL8CN0fElA6IrVUuUVSH+nr4179S57133oET\nToADD4Tll887MrOuqRyN2aNJyWHSsgZXak4U1SUC7r8/dd6bMiXdWnvYYdCnT96RmXUtHmbcqsIT\nT6SE8cAD8POfw5FHwqqr5h2VWddQzjYKs5IZNiwNPvjAA+luqQ03TG0Zr7+ed2Rm1lhZE4WkkZKm\nSXpe0glNbD9O0sTsZ7KkhZJWybZNl/R0tm1COeO0/Hzxi3DVVTBxIsyfD0OGpAbwl17KOzIza1C2\nqidJ3UjzXu8EzAIeo4l5rwv2/xZwdETslC2/DAyLiGZHFHLVU+fz1ltw3nlwySXpltoTT4RNN807\nKrPOpZKqnoYDL0TE9IhYAIwB9mxh//2Amxqt8533Xczqq8PvfpdKFJttBjvvDHvsAQ8/nHdkZl1X\nORPFAGBGwfLMbN1SJPUGdgFuLVgdwL2SHpd0WNmitIq00krpNtqXXoKRI2G//eDrX4d77kl3T5lZ\nx+lexnO35e28O/BgRMwpWPfViHg9m/9irKRpETG+8YG1tbWfPa6pqaGmpqad4Vol6tULjjgi3UZ7\n881wzDFp3ahR8O1vw3K+HcOsVXV1ddTV1bX7+HK2UWwN1EbEyGx5FFAfEWc3se9tpL4aY5o516nA\nhxHxh0br3UbRxdTXwz/+kTrvvf9+KnXsv78775m1RSW1UTwObCRpkKTlgX2AOxrvJGll0vSqfy9Y\n11vSitnjPsDOwOQyxmpVYrnlYM890wRKF10EN96Ybq09/3z4+OO8ozPrnMqWKCJiIXAkcDcwhVRi\nmCrpcEmHF+y6F3B3RMwrWNcfGC9pEvAo8M+IuKdcsVr1kWDHHWHsWLj1VqirSzPvnXEGzJnT6uFm\n1gbumW2dxpQpcPbZ8M9/pjaNo4+GNdfMOyqzylNJVU9mHWqTTdJse08+CR99lJaPOAJefjnvyMyq\nmxOFdTrrrQcXXABTp8Iqq8CWW6bRav/737wjM6tOThTWafXvn+6OevHFVLoYMQL22gsefTTvyMyq\ni9sorMuYNw+uvDLNvLfhhqkvxogRnnnPuh4PM27WigUL4Kab4Kyz0lwYJ52Ubrl15z3rKpwozIpU\nXw9//3uaF+PDD1Pnvf32gx498o7MrLycKMzaKALGjUsJ48UX4bjj4Ec/SkOFmHVGvj3WrI0k2Gmn\nlCxuvhnuvTd13hs9Og0TYtbVOVGYFdhqK7j99pQ0pk6FDTZIjd6zZ+cdmVl+nCjMmjBkCFx7LTz+\nOHzwAWx9vXmZAAARIElEQVS8cZrX+5VX8o7MrOM5UZi1YP314cIL0/AgffvC0KFw8MFp2ayrcKIw\nK8Kaa6bbaV98EQYPTpMofec78NhjeUdmVn5OFGZtsMoqcPLJafyor38dvvvd1BD+n/945j3rvHx7\nrNkymD8/zYlx1lmw8sqp4XuPPdx5zyqb+1GY5WDRonS31OjRaaiQE05Ivb1XXjnvyMyW5kRhlqOI\n1A/jj3+E8eNh003hG99I1VNbbeUpW60yVFSikDQS+BPQDbi88XzZko4D9s8WuwMbA/0iYk5rx2bH\nO1FYxZo3Dx56KCWOsWPhuedgu+0WJ44hQzwgoeWjYhKFpG7As8BOwCzgMWDfiJjazP7fAo6OiJ2K\nPdaJwqrJO+/AffctThwff5xGr/3GN9LvddbJO0LrKiopUWwDnBoRI7PlEwEi4qxm9r8RGBcRVxR7\nrBOFVbOXXko9wMeOTXdNrb764tLGDju4fcPKp5LGehoAzChYnpmtW4qk3sAuwK1tPdasWm2wQZrb\n+y9/gTffhBtuSKWKCy5Iv7fdFk49NbV1zJ+fd7TWlXUv47nb8lV/d+DBiJjT1mNra2s/e1xTU0NN\nTU0bLmtWGZZbLvX6HjoUjj9+yfaNY45x+4Ytm7q6Ourq6tp9fDmrnrYGaguqj0YB9c00St8G3BwR\nY9pyrKuerKtw+4aVUiW1UXQnNUiPAF4DJtB0g/TKwEvAOhExr43HOlFYl+T2DVsWFZMosmB2ZfEt\nrldExGhJhwNExKXZPgcDu0TEfq0d28T5nSisy6uvh0mTFpc2HnnE/TesZRWVKMrNicJsae6/Ya1x\nojCzJbh9wxpzojCzFrl9w5wozKxoDe0bY8emEofbN7oGJwoza7eG9o2GxOH2jc7JicLMSqahfaMh\ncbh9o3NwojCzsnnppZQw7r3X7RvVzInCzDqE2zeqlxOFmeXC7RvVw4nCzCqC2zcqlxNFhZk4EaZO\nhc02gy98AXr0yDsis3y4faNyOFFUmHHj4JJLYPJkeOUVGDw41eNuttni32uv7SK5dS1u38iXE0UF\nmzcPpkyBp59OiePpp9PPwoVLJo5NN4UvfQn69s07YrOO4faNjuVEUYVmz16cOBp+T50Ka621dALZ\ncEPo1i3viM3Ky+0b5eVE0UksXAgvvLB0Apk9GzbeeOkEssYaeUdsVj5u3ygtJ4pObu5ceOaZJZPH\n5MnQs+fSbR+bbAIrrJB3xGal5faNZedE0QVFwMyZS5c+XngBBg1aOoGst16ao9msM3D7RttVVKKQ\nNJLFs9Rd3sx82TXAH4EewNsRUZOtnw58ACwCFkTE8CaOdaJowfz5MG3a0gnkgw9SY3lhAtl0U1h1\n1bwjNlt2bt9oXcUkCkndSPNe7wTMAh6j0bzXklYB/o80FepMSf0i4u1s28vAsIh4t4VrOFG0w7vv\npqRRmECeeSZVU623Hqy7bvpp/Hj11f3NzKqP2zeWVkmJYhvg1IgYmS2fCBARZxXscwSwZkT8ponj\nXwa+EhHvtHANJ4oSqa+HN9+EV19N/T1efXXpxx99tDhxNJVI1lkntZWYVSq3bySVlCi+RyopHJYt\nHwBsFRE/L9inocppCLAicF5EXJdtewl4n1T1dGlEXNbENZwoOtCHH8KMGc0nk1mz4HOfa7lUsuqq\nLpVY5eiq7RttTRTdyxhLMZ/gPYChwAigN/CwpEci4nngaxHxmqTVgbGSpkXE+MYnqK2t/exxTU0N\nNTU1pYjdmtC3b7o1d+ONm96+aBG8/vqSCWTaNLjnnsXJZOHCphNIw+MBAzzMiXWcXr1Su8WIEWm5\nsH3jggs6T/tGXV0ddXV17T6+nCWKrYHagqqnUUB9YYO2pBOAXhFRmy1fDvw7Im5pdK5TgQ8j4g+N\n1rtEUWXef7/paq2Gx7NnQ//+LZdKVlop77/CuorO2r5RSVVP3UmN2SOA14AJLN2Y/UXgz8AuQE/g\nUWAfYDrQLSLmSuoD3AOcFhH3NLqGE0Uns2BBqsJqKpk0LHfv3nKpZK213HvdSq8ztW9UTKLIgtmV\nxbfHXhERoyUdDhARl2b7HAccCtQDl0XE+ZI2AP6WnaY7cENEjG7i/E4UXUwEvPde8w3ur7yS7upa\ne+2Wk0mfPnn/JVbtqrl9o6ISRbk5UVhTPv00dUBsLpm8+mpKFC0lkjXWcKdEa5tq6r/hRGHWigh4\n662WbwX+4AMYOLD5ZDJwoIdHsZZVcvuGE4VZCXz8ccu3As+cmW71ba7BfeDA9EFQLXXWVl6V1r7h\nRGHWAerr4Y03mm8nmTEjlUog3Vbcp8/i3809bsv23r1dNVbN8m7fcKIwqyDz56ce7R9+mH4XPm5q\nXWvbG35/8kmq+mpvomlpu0tBHa+j2zecKMy6gPr69GFSiuTTeDu4FJS3crdvOFGY2TJpaymo2OTk\nUlD7lKN9w4nCzCpSR5WCljURVXopqBTtG04UZtblNC4FlSL5fPRR+lDu1auyS0Htad9wojAzK5G2\nlILampygPKWg2bNh3LiW2zecKMzMqkBHlIJ6905jpy1cmK7ZrRsMHw4PP1w5w4ybmVkzll8+/ZR6\nCuLCUlBzCeXhh9t2TpcozMy6mLZWPVVYe76ZmVUaJwozM2uRE4WZmbWorIlC0khJ0yQ9n0172tQ+\nNZImSnpGUl1bjjUzs/IrW6KQ1I00zelIYBNgX0kbN9pnFeBCYPeI+BLwvWKPrSbLMql5R3KcpVUN\ncVZDjOA481bOEsVw4IWImB4RC4AxwJ6N9tkPuDUiZgJExNttOLZqVMs/j+MsrWqIsxpiBMeZt3Im\nigHAjILlmdm6QhsBq0m6T9Ljkg5sw7FmZtYBytnhrpgODj2AocAIoDfwsKRHijzWzMw6QNk63Ena\nGqiNiJHZ8iigPiLOLtjnBKBXRNRmy5cD/yaVIFo8NlvvhGJm1g6VMoTH48BGkgYBrwH7APs22ufv\nwJ+zxuuewFbAucBzRRzbpj/UzMzap2yJIiIWSjoSuBvoBlwREVMlHZ5tvzQipkn6N/A0UA9cFhFT\nAJo6tlyxmplZ86p6rCczMyu/qu2ZXQ0d8iQNzO7o+m/WofCovGNqjqRuWcfHf+QdS3MkrSLpFklT\nJU3J2sEqjqRR2Ws+WdKNknrmHROApCslzZY0uWDdapLGSnpO0j1Z36ZcNRPnOdnr/pSkv0laxlmj\nl11TcRZsO1ZSvaTV8oitII4mY5T08+z5fEbS2c0d36AqE0UVdchbABwTEUOArYGfVWicAL8AplDZ\nd5ydB9wZERsDmwEVVx2ZtasdBgyNiE1JVac/yDOmAleR3jOFTgTGRsRgYFy2nLem4rwHGBIRm5Pa\nMEd1eFRLaypOJA0EvgG80uERLW2pGCV9HdgD2Czr6Pz71k5SlYmCKumQFxFvRMSk7PGHpA+2tfON\nammS1gF2Ay4HKvIGgewb5HYRcSWkNrCIeD/nsJryAekLQm9J3Um3fc/KN6QkIsYD7zVavQdwTfb4\nGmCvDg2qCU3FGRFjI6I+W3wUaGKCz47VzPMJ6Yac4zs4nCY1E+NPgdHZZycR8VZr56nWRFF1HfKy\nb5pbkP7JK80fgV+RbiioVOsDb0m6StKTki6T1DvvoBqLiHeBPwCvku7YmxMR9+YbVYv6R8Ts7PFs\noH+ewRTph8CdeQfRFEl7AjMj4um8Y2nBRsD2kh6RVCfpK60dUK2JopKrR5YiqS9wC/CLrGRRMSR9\nC3gzIiZSoaWJTHdS58yLImIo8BGVUU2yBEmfB44GBpFKj30l7Z9rUEXKZgGr6PeWpJOB+RFxY96x\nNJZ9cTkJOLVwdU7htKQ7sGpEbE36gviX1g6o1kQxCxhYsDyQVKqoOJJ6ALcC10fE7XnH04RtgT0k\nvQzcBOwo6dqcY2rKTNI3tcey5VtIiaPSfAV4KCLeiYiFwN9Iz3Glmi1pTQBJawFv5hxPsyQdQqoi\nrdTE+3nSF4SnsvfTOsATktbINaqlzST9X5K9n+olfa6lA6o1UXzWmU/S8qQOeXfkHNNSJAm4ApgS\nEX/KO56mRMRJETEwItYnNbr+JyIOyjuuxiLiDWCGpMHZqp2A/+YYUnOmAVtL6pW9/juRbhKoVHcA\nB2ePDwYq8csMkkaSvv3uGRGf5B1PUyJickT0j4j1s/fTTNJNDZWWfG8HdgTI3k/LR8Q7LR1QlYki\n+6bW0CFvCnBzhXbI+ypwAPD17NbTidk/fCWr5KqHnwM3SHqKdNfTmTnHs5SIeAq4lvRlpqGe+n/z\ni2gxSTcBDwFfkDRD0qHAWcA3JD1H+vA4K88Yock4fwhcAPQFxmbvo4tyDZIl4hxc8HwWyv291EyM\nVwIbZLfM3gS0+sXQHe7MzKxFVVmiMDOzjuNEYWZmLXKiMDOzFjlRmJlZi5wozMysRU4UZmbWIicK\nq2iSFmX3zU+W9BdJvfKOqRiS1pD0r+zx5pJ2LcE5L5G0jaQvSpok6QlJG0haavZHSWtJeiHbp2/B\n+l6S/lUwxPTogm1HSTpwWeO0zseJwirdxxGxRTZk93zgJ4UbsxFaO0Qbr3UkcHX2eAvS0BPLaivS\noJJ7AX+NiGHAusB+hTtJWhG4jdST+Rrglkax/082VPsWwFcLOoFeRerUaLYEJwqrJuOBDSXtIGm8\npL8Dz0haLpvYZkI2sc2P4bNv1Q8UlEi+mu17dbb8tKRfZPvWSRqWPe6XjdWDpEMk3SFpHKlXcG+l\nyWAezUax3aOZWL8H/CsbYua3wD5ZHN9Xmizo9izWhyVtml2rNjv3fZJelPTZh7bSPCbPAbuQ5g75\nqaT/AKOB7bJz/yJLCDcCZ0XEbRFxPmmYjssAImJeRNyfPV4APEk28nJEzAXekTSkFC+WdR4d9m3M\nbFlkH4C7sXh46S1IE9m8kiWGORExXGk2uQcl3QN8B/h3RJyZjbvUJztu7ayEgqSVsvO1NHLqFsCm\nETFH0pnAuIj4odJscI9KujciPi6IdU1gUcM6SacAwyLiqGz5AuCJiNhLaRKZa7NrAAwGvg6sBDwr\n6aKIWATsCtwVEXdJugSYGxHnStoBOC4idi+It/AxEdHkcBdZ/LsDheOQTQC2pzLH0bKcuERhla6X\npInAY8B00jg1AiZERMMMYjsDB2X7PQKsBmyYHXOopFNJs3l9CLxIGufmfEm7AHOLiGFsRMwpuNaJ\n2bXuA3qy5EjGAOsBrxcsiyWHm/4qcB1ARNwHfC6rLgrgXxGxIBuk7U0Wzw+xM/DvRucs/N0mWeK9\nCTgvIqYXbHqNNAKq2WdcorBKNy8itihckQoHfNRovyMjYmzjgyVtB3wLuFrSuRFxnaTNSVU4PwH2\nBn4ELGTxF6cVGp2m8bW+ExHPtxJ34Qd4UyWV5j7g5xc8XgR0V5rnYJVsBN1S+V/g2axqqnFcHgDO\nluAShXUGdwNHNDTYShqctSWsC7wVEZeTpnkdqjTufreI+BtwCourfKaT5pKA1L7Q0rWOaliQtEUT\n+7wCrFmwPBdYsWB5PNmcCpJqshjn0nTyEKkq6j/NxNP43K2S9DtS1dYxTWxei/RcmH3GicIqXVPf\nbhu3J1xOGm7+yWzo5ItJpeUaYJKkJ0klhz+RGm7vy6qOrgNGZef4PamB+EngcwXnb3yt04EeWUP4\nM8BpSwWXvvl3l9QnW3UfsElDYzZQCwxTGi79TBbPB9H4Wg2Pd2XJaqfCbU8Bi7LbZX/ROJbGlOZH\nPwnYmPR8TZT0o4JdhpMSmdlnPMy4WRlIqgWmRsTNJTjXE8DwrFG7bLKG/XERsWU5r2PVx4nCrAwk\nrQ5cExGl6D/RISQdBbwbEdfnHYtVFicKMzNrkdsozMysRU4UZmbWIicKMzNrkROFmZm1yInCzMxa\n5ERhZmYt+v/jvQbqw2EtYAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5d2cab0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex4-pg323"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "#calculate Primary Consolidation Sc in three parts\n",
      "Gd=14.\n",
      "Gss=18.\n",
      "Gsc=19.\n",
      "Gw=9.81\n",
      "To= 2.*Gd+4.*(Gss-Gw)+2*(Gsc-Gw)\n",
      "LL=40.\n",
      "Cc=0.009*(LL-10)\n",
      "H=4.\n",
      "T=100.\n",
      "e=0.8\n",
      "Sc= Cc*H*math.log10((To+T)/To)/(1.+e)\n",
      "print'%s %.2f %s'%('a)Primary Consolidation Sc = ',Sc,' m')\n",
      "\n",
      "\n",
      "Tc=190\n",
      "Cs=Cc/6\n",
      "Sc= Cs*H*math.log10((To+T)/To)/(1+e)\n",
      "print'%s %.2f %s'%(' b)Primary Consolidation Sc =',Sc,'m')\n",
      "\n",
      "\n",
      "Tc=170\n",
      "Sc= Cc*H*math.log10((To+T)/Tc)/(1+e)+ Cs*H*math.log10(Tc/To)/(1+e)\n",
      "print'%s %.3f %s'%(' c)Primary Consolidation Sc =',Sc,' m')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "a)Primary Consolidation Sc =  0.21  m\n",
        " b)Primary Consolidation Sc = 0.04 m\n",
        " c)Primary Consolidation Sc = 0.047  m\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex5-pg325"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#calculate The settlement in the field Sc\n",
      "Gs=18.\n",
      "Gw=9.81\n",
      "H=10.\n",
      "eo=1.1\n",
      "To=5.*(Gs-Gw)\n",
      "T1=48.\n",
      "T=To+T1\n",
      "e1=1.045 ## void ratio corresponding to T \n",
      "e=eo-e1\n",
      "Sc=H*e/(1.+eo)\n",
      "print'%s %.2f %s'%('The settlement in the field Sc = ',Sc,' m')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The settlement in the field Sc =  0.26  m\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex6-pg329"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "#calculate Total consolidation settlement of the clay\n",
      "T=8.5\n",
      "eo=0.8\n",
      "Cc=0.28\n",
      "To=2650.\n",
      "T1=970.\n",
      "C1=0.02\n",
      "t2=5.\n",
      "t1=1.5\n",
      "H=8.5*12\n",
      "epr=Cc*math.log10((To+T1)/To)\n",
      "ep=eo-epr\n",
      "C2=C1/(1.+ep)\n",
      "Sc=epr*H/(1.+eo)\n",
      "Ss=C2*H*math.log10(t2/t1)\n",
      "TS=Sc+Ss\n",
      "print'%s %.1f %s'%('Total consolidation settlement of the clay =',TS,' in')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Total consolidation settlement of the clay = 2.8  in\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex7-pg336"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "#calculate t field\n",
      "##T50 = Cvtlab /H^2 lab = Cvtfield?H^2 fiels\n",
      "tl=140.\n",
      "Hf=3.\n",
      "Hd=0.025/2.\n",
      "tf=tl*Hf**2/Hd**2\n",
      "k=tf/(3600.*24.)\n",
      "print'%s %.1f %s'%('t field = ',k,' days')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "t field =  93.3  days\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex8-pg336"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "##Tv is directly proportional to U^2\n",
      "t1=93.333\n",
      "U2=30.\n",
      "U1=50.\n",
      "t2=t1*U2**2./U1**2.\n",
      "print'%s %.2f %s'%('t2 =',t2,' days')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "t2 = 33.60  days\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex9-pg337"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "#evaluvate Cv\n",
      "#intilization variable\n",
      "t90=75.*24.*60.*60. ## time in sec\n",
      "T90=0.848\n",
      "Hd=1.5*100. ##in cm\n",
      "Cv=T90*Hd**2/t90\n",
      "print'%s %.3f %s'%('Cv =',Cv,' cm^2/sec')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cv = 0.003  cm^2/sec\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex10-pg337"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "#calculate K and t60\n",
      "To=3000. ## lb/ft^2\n",
      "eo=1.1\n",
      "e1=0.9\n",
      "e=eo-e1\n",
      "ea=(eo+e1)/2.\n",
      "T1=3000. ## lb/ft^2\n",
      "T=1. ## in\n",
      "t = 2. ## min\n",
      "m=(e/T1)/(1.+ea)\n",
      "U=50.\n",
      "Tv=0.197\n",
      "Gw=62.4 ##lb/ft^3\n",
      "Cv=Tv*(T/(2.*12.)**2)/t\n",
      "k=Cv*m*Gw *10**7\n",
      "print'%s %.3f %s'%('a)k = ',k,' x10^-7 ft/min')\n",
      "\n",
      "\n",
      "U=60\n",
      "Tv=0.286\n",
      "H=6\n",
      "t60=Tv*H**2/(Cv*60*24)\n",
      "print'%s %.1f %s'%(' b)t60 =',t60,' days')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "a)k =  3.557  x10^-7 ft/min\n",
        " b)t60 = 41.8  days\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex11-pg344"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#calculate the value of Cv\n",
      "import math\n",
      "#Cv\n",
      "t50=19\n",
      "Hd=2.24/2\n",
      "Cv=0.197*Hd**2/t50\n",
      "print'%s %.3f %s'%('Cv = ',Cv,' cm^2/min')\n",
      "%matplotlib inline\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore')\n",
      "import math\n",
      "import numpy\n",
      "from math import tan\n",
      "import matplotlib\n",
      "from matplotlib import pyplot\n",
      "#given\n",
      "t=numpy.array([.02,.1,.25,.5,1,2.,4.,8.,16.,30.,60.,120.,240.,480.,960.,1440.])\n",
      "gauge=numpy.array([3975.,4082.,4102.,4128.,4166.,4224.,4298.,4420.,4572.,4737.,4923.,5080.,5207.,5283.,5334.,5364.])\n",
      "Hdr=2.24\n",
      "t50=19.\n",
      "#calculations\n",
      "Cv=.197*(Hdr/2)**2 /t50/60.\n",
      "leng=len(t)\n",
      "logt=numpy.zeros(leng)\n",
      "for i in range(0,leng):\n",
      "\tlogt[i]=math.log(t[i])\n",
      "\n",
      "#results\n",
      "print 'The value of Cv (cm^2/sec) = ',Cv\n",
      "pyplot.plot(logt,gauge)\n",
      "pyplot.xlabel('Time(min) - log scale')\n",
      "pyplot.ylabel('Dial reading (cm)')\n",
      "pyplot.title('Graph of dial reading vs time')\n",
      "pyplot.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cv =  0.013  cm^2/min\n",
        "The value of Cv (cm^2/sec) =  0.000216769122807\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ajHffTaOyO3WC2293omhJThZm1ia8/npa5nTzzeG66zzPU0tzsjCzVm/uXNhlF/jGNzzP\nU6U4WZhZqzZrFuy6a5q6Y9Qoz/NUKW7gNrNW6+9/hwMPTGMpDjus0tF0bE4WZtYqTZoExxwDf/kL\nDB5c6WjMEwmaWatzzz1wyCFw220wcGClo2nfip1I0G0WZtaqPP00HHooXH+9E0Vr4mRhZq3GK6+k\ncRQXXQRf+1qlo7FCThZm1irUDrg76aQ0QttaF7dZmFnFLVoE++4LffqkcRTuHttyim2zcLIws4qK\nSL2e3nkHbr7ZA+5amlfKM7M24cc/hueeS5MCOlG0XmVvs5A0V9KTkmZKmpH3rSdpmqTnJU2VtE7B\n8WdJekHSs5IGF+wfIGlW/mxMueM2s/L7/e9Tr6fbboM116x0NNaYlmjgDqAqIvpHxPZ535nAtIjY\nFLgrv0dSP2AY0A8YAlwuLau9vAI4PiL6AH0kefFEszbs9tth5Mi0drbXo2j9Wqo3VN36sP2BsXl7\nLHBA3h4KXBcRiyNiLjAHGChpQ6BrRMzIx40rOMfM2phHHoHjjoMJE+DLX650NFaMlipZ3CnpUUkn\n5H3dIuK1vP0a0C1vdwfmFZw7D+hRz/75eb+ZtTH//CcMHQpXXulBd21JSzRw7xwRr0raAJgm6dnC\nDyMiJJWsC9OoUaOWbVdVVVFVVVWqS5tZM/3nP7D33qlRe//9Kx1Nx1RdXU11dfUKn9eiXWcljQQW\nAieQ2jEW5Cqm6RGxuaQzASLi/Hz8FGAk8GI+pm/efzgwKCK+U+f67jpr1kp98AHssUdawGj06EpH\nY7VaxdxQktaU1DVvfwYYDMwCJgJH58OOBibk7YnAYZJWl9Qb6APMiIgFwLuSBuYG7+EF55hZK7d0\nKXzrW6l94rzzKh2NrYxyV0N1A27JHZpWBa6JiKmSHgXGSzoemAscChARsyWNB2YDS4ARBUWFEcDV\nQBdgUkRMKXPsZlYCEWnhooULYfx4j85uqzyC28zK6oIL0poU990Ha69d6WisLo/gNrOKu/ZauOwy\neOABJ4q2ziULMyuL6dNh2LA0jceWW1Y6GmtIq2jgNrOOadaslCj++lcnivbCycLMSmrevDTd+CWX\npG6y1j44WZhZyfz3v2kBo5NPTl1lrf1wm4WZlcSiRWl0dt++8Otfu4tsW+E2CzNrUWedBZ07w5gx\nThTtkbvOmlmz3XYb3HgjPPaYFzBqr5wszKxZXnoJvv1tuOUW+NznKh2NlUvR1VCSOktao5zBmFnb\nsngxHH44nH467LRTpaOxcmqwgVvSKqQFhg4HdiIlFgFLgQeBa4AJralF2Q3cZi3rzDPhiSfgb3+D\nVdwC2iYV28DdWLK4F7iPNBPs4xHxcd6/BtCftNrdLhGxW8mibiYnC7OWM3kynHACzJzpZVHbslIk\nizVqE0QjN2nymJbkZGHWMubPhwED0iyyu7War4u2MpqdLOpcbF1gI2BZP4eIeKxZEZaBk4VZ+S1Z\nAl/7GgweDD/6UaWjseYq2ayzkn4KHAP8C6gp+MgD+c06oHPPhTXWSOMqrOMopuvsMOBLEbGo3MGY\nWet2551w1VUeT9ERFdN/4Wlg3XIHYmat26uvwlFHwbhx0K1bpaOxltZkm4Wk7YBbgaeA2sbsiIj9\nyxzbCnObhVl5LF0Ke+4Ju+6aqqGs/SjlSnnjgPNJyaK2zcK/kc06kPPOS2tp//jHlY7EKqWYZLEw\nIi5d2RtI6gQ8CsyLiG9I2h64DFgNWAKMiIhH8rFnAceRBv6dEhFT8/4BwNVAZ2BSRJy6svGY2Yqp\nroYrroB//MPtFB1ZMW0W90n6uaQdJW1T+1qBe5wKzGZ5aeQC4JyI6A/8OL9HUj9SY3o/YAhwubRs\n7sorgOMjog/QR9KQFbi/ma2k11+HI4+Eq6+G7t0rHY1VUjEli21Iv+h3qLO/ya6zknoC+wDnAafn\n3a8CtUu3rwPMz9tDgesiYjEwV9IcYKCkF4GuETEjHzeONA3JlCJiN7OVVFMDw4enRu299qp0NFZp\nTSaLiKhqxvUvBs4A1irYdyZwv6RfkUo2O+b93YGHCo6bB/QAFuftWvPzfjMro/PPhw8+gJ/8pNKR\nWGtQzKC80cAFEfFOfr8u8L2IaHTspqT9gNcjYqakqoKP/khqj7hF0iHAVcCeK/sAdY0aNWrZdlVV\nFVVVVQ0ea2b1u+8+uPRSePRRWNULGbQr1dXVVFdXr/B5xXSdfTwitq6zb2Zuc2jsvNHAcFIjdmdS\n6eJmYGhErJWPEfBORKwt6UyAiDg/fzYFGAm8CEyPiL55/+HAoIj4Tj33dNdZs2Z64w3YZpvUqL3v\nvpWOxsqtlMuqriKpc8GFuwCrN3VSRJwdEb0iojdwGHB3RAwH5kgalA/7GvB83p4IHCZpdUm9gT7A\njIhYALwraWBOLsOBCUXEbWYrqKYGjj4ahg1zorBPKqaAeQ1wl6SrSOtZHEtqZF5RtV/5TwR+k6c6\n/zC/JyJmSxpP6jlV26W29pwRpK6zXUhdZ924bVYGF14Ib70Fo0dXOhJrbYqddXZvYI/8dlpE3FHW\nqFaSq6HMVt6DD8IBB8CMGbDxxpWOxlpKKdazaPI3bzHHtKRWFo5Zm/HWW9C/f2rUHjq00tFYSypF\nm0W1pDMkbVrPxTeT9EPgnuYEaWaVFwHHHgsHHuhEYQ1rrM1iMHAEqX1hS+A9UpvFZ0nzRF0DfL3s\nEZpZWY0ZA6+8AjfcUOlIrDUrts2iE7B+fvtGRCwta1QrydVQZivmkUdSr6eHHoIvfrHS0VgllHLW\nWXJyeK3ZUZlZq/HWW3DooWk8hROFNaWokkVb4ZKFWXFqalL7xJe+BJdcUulorJJKWrIws/bll79M\nI7VvuqnSkVhb4WRh1sHcey9cfHFqr1i9ybkYzJImp/uQ9F49r3mSbpHkmk6zNuS11+Bb30rrU/Tq\nVelorC0ppmQxBngZuC6/Pwz4EjCTNGNsVVkiM7OSWro0JYpjj4UhXj7MVlAxs84+GRFfqbPv8YjY\nWtITEfHVska4AtzAbdawc86BBx6AqVO9PKotV8oG7g8kDQNqh+wcDHyUt/2b2awNmDIF/vQnr6Nt\nK6+YksWXSFVRtcuqPgT8L2nFugERcX9ZI1wBLlmYfdrLL8N228H48bDbbpWOxlqbZk8k2BY5WZh9\n0qJFMGhQmk32hz+sdDTWGpUsWUj6PHACsAnLq60iIo5rbpCl5mRh9kmnnQZz5sCtt8IqxSx1Zh1O\nKdssbgXuBaYBNXmffyObtXI33QQTJqR2CicKa66VWoO7tXLJwix54QXYaSeYPBm23bbS0VhrVso1\nuG+X5NV4zdqIDz+Egw+Gc891orDSKaZksRBYE1gELM67IyLWKnNsK8wlCzP49rfh/ffh2mtBTX5f\ntI6uZCWLiPhsRKwSEZ0jomt+FZ0oJHWSNFPSbQX7vivpGUlPSfpFwf6zJL0g6VlJgwv2D5A0K382\npth7m3U0Y8fC/ffD73/vRGGl1WADt6S+EfGMpG3q+zwiHivyHqcCs4Gu+bq7A/sDX4mIxZI2yPv7\nAcOAfkAP4E5JfXJR4Qrg+IiYIWmSpCERMaXI+5t1CLNmwfe/D9OnQ9eulY7G2pvGekOdTuoyexH1\n937avamLS+oJ7AOcl68HcBLw84hYDBAR/8n7hwLX5f1zJc0BBkp6EegaETPyceOAAwAnC7Psvffg\nkEPgwgthyy0rHY21Rw0mi4g4If9Z1YzrXwycARRWW/UBdpM0mjRtyPcj4lGgO2l0eK15pBLG4rxd\na37eb2ZABJxwAuy6Kxx1VKWjsfaqsWqog2hkPEVE3NzYhSXtB7weETMlVdW557oRsYOk7YDxQMmm\nOh81atSy7aqqKqqqqho81qw9uPxyeO65NEmgWVOqq6uprq5e4fMa7A0l6WpSsvg8sBNwd/5od+CB\niNiv0QunksNwYAnQmVS6uBlYHzg/Iu7Jx80hzTv1bYCIOD/vnwKMBF4EpkdE37z/cGBQRHynnnu6\nN5R1KI88AvvumxLFl79c6WisLWp2b6iIOCYijgVWB/pFxEERcRCwRd7XqIg4OyJ6RURv0hoYd0fE\ncGAC8LUc5KbA6hHxBjAROEzS6pJ6k6qrZkTEAuBdSQMliZSAJjR1f7P27q234NBD4be/daKw8itm\nuo9ewIKC968BG63EvWq/8l8FXCVpFmnsxlEAETFb0nhSz6klwIiCYsII4GqgCzDJPaGso6upgaOP\nThMEHnhgpaOxjqCYQXmXAZsC1wIidW99ISK+W/7wVoyroawjiIBTTkldZadO9Tra1jylnHVWwDeB\n3Uilg3sj4paSRFliThbWEfzsZ3DjjXDPPbD22pWOxto6r2dh1g797ndwwQVplPaGG1Y6GmsPSjbd\nh6QdJT0iaaGkxZJqJL1bmjDNrFg33ZQmB7zjDicKa3nFzDp7GfAt4AVSF9jjgcvLGZSZfdL06XDS\nSfC3v7nnk1VGUUuiRMQLQKeIWBoRfwKGlDcsM6s1cyYMGwZ//Sv071/paKyjKqbr7PuS1gCekHQB\nqRut57M0awFz5qRBd1dcAbs3ORubWfkUU7I4Kh93MvAB0BM4qJxBmRksWAB77QUjR8JB/h9nFVZU\nbyhJawK9IuK58oe08twbytqL//4XBg1KSeKccyodjbVnpewNtT8wE7gjv+8vaWLzQzSz+nz0EQwd\nCrvsAj/6UaWjMUuKGZT3GGkup+kR0T/veyoiWt2s+S5ZWFu3ZElal2KNNdKyqKsU1QXFbOUVW7Io\npoF7cUS8o0+u0Viz0pGZWb0iUvfYhQvh+uudKKx1KSZZPC3pCGBVSX2AUwDPnG9WYuecA48/Dnff\nnUoWZq1JMd9dTiZNS/4xcB3wLvC/5QzKrKO59FK44QaYNMnrZ1vr1GibhaRVgWkR0SZ6eLvNwtqi\na6+FH/4Q7rsPNtmk0tFYR1OS3lARsQSokbROySIzs2XuuANOOw0mT3aisNatqBHcwCxJ0/I2QETE\nKeULy6z9e/hhOPJIuOUW2LLV9S00+6RiksXN+VVbv6OCbTNbCc8+m8ZSXHVVGk9h1tp5PQuzFjZv\nHuy8c5pu/JhjKh2NdXQlG8FtZqXz1ltpvqeTT3aisLal7MlCUidJMyXdVmf/9/JCSusV7DtL0guS\nnpU0uGD/AEmz8mdjyh2zWTm8/z7stx/svTeccUalozFbMS1RsjgVmE1BO4ekXsCewIsF+/oBw4B+\npPUyLtfyYeNXAMdHRB+gjySvp2FtyhtvwJ57wuabp2VRzdqaBhu465YE6oiI2L+pi0vqCewDnAec\nXvDRRcAPgFsL9g0FrouIxcBcSXOAgZJeBLpGxIx83DjgAGBKU/c3aw3++c9UmjjoIDjvPE/jYW1T\nY72hLizB9S8GzgDWqt0haSgwLyKerDPfVHfgoYL384AewOK8XWt+3m/W6s2YAQcckKbyOOmkSkdj\ntvIaTBYRUd2cC0vaD3g9ImZKqsr71gTOJlVBLTu0Ofepa9SoUcu2q6qqqKqqKuXlzYp2221w3HHw\nxz/C/k2Ww81aRnV1NdXV1St8XjFTlG8KjCbND9U5746I+GIT540GhgNL8nlrAZOBXUkr7kFadW8+\nMBA4Nl/4/Hz+FGAkqV1jekT0zfsPBwZFxHfquae7zlqrcMUV8JOfwK23wvbbVzoas4aVsuvsn4Df\nkqqDqoCxwDVNnRQRZ0dEr4joDRwG3B0RB0dEt4jonffPA7aJiNeAicBhklaX1BvoA8yIiAXAu5IG\n5gbv4cCEIuI2a3E1NXDWWXDxxXD//U4U1n4UM4K7S0TcqfS1/UVgVF4QaUUXe6zvK/+yfRExW9J4\nUs+pJcCIgmLCCOBqoAswKSLcuG2tzscfp2qnf/8bHngA1l+/0hGZlU4x1VAPkKqObgTuAl4Bfh4R\nm5U/vBXjaiirlHfegQMPhHXWgWuugS5dKh2RWXFKWQ31v8CapEWPtgWOBI5uXnhm7cfLL6f5nbba\nKq1J4URh7ZHnhjJrhieeSKOyTzstvVTSvn1m5dfsNbgljYmIUxsYnFfUoDyz9mzaNDjiCLjsMjj0\n0EpHY1ZejTVwj8t/1jc4z1/frUMbOxZ+8AO46SbYdddKR2NWfkVVQ0naACAi/lP2iJrB1VBWbhHw\ns5+ldSggMJazAAASJ0lEQVQmTYK+fSsdkVnzNLuBW8koSW8AzwPPS3pD0shSBmrWVixeDCeeCBMm\npK6xThTWkTTWG+o0YGdgu4hYNyLWBbYHdpZ0eiPnmbU7CxemKTvmzYN77oENN6x0RGYtq8FqKEmP\nA3vWrXrKVVLTImLrFohvhbgaysphwQLYd1/o3z9N47HaapWOyKx0SjHOYtX62ijyvmJGfpu1ec88\nAzvumGaO/cMfnCis42rsl/7ilfzMrM174w0YPTr1erroIjjaw1Ctg2usZPEVSe/V9wK2aqkAzVrS\nwoWpt9Pmm6e5np56yonCDBpfz6JTSwZiVkmLFqVqpp/9DKqq4KGH4MtfrnRUZq2H2x6sQ6upgeuv\nTyvZ9emTxk7071/pqMxaHycL65AiYMqUtPbEGmvAlVfC7rtXOiqz1svJwjqchx6CM89MXWJHj4Zv\nftMTAJo1pZgpys3ahWeeSYnhkENg+PDUeH3ggU4UZsVwsrB27+WX4fjjYdAg2GkneP759H5Vl6vN\niuZkYe3Wm2/C978PW28N3bqlJHHGGV6cyGxlOFlYu/P++6ktYrPN0vasWen9OutUOjKztqvsyUJS\nJ0kzaxdRkvRLSc9IekLSzZLWLjj2LEkvSHpW0uCC/QMkzcqfjSl3zNY2vfoqnHtu6gL75JPw4INp\nLqfu3SsdmVnb1xIli1OB2SxfMGkqsEVEfJU09flZAJL6AcOAfsAQ4HJpWdPjFcDxEdEH6CNpSAvE\nbW1ABNx7LwwbBv36pR5OU6emsRN9+lQ6OrP2o6zJQlJPYB/gSkAAETEtImryIQ8DPfP2UOC6iFgc\nEXOBOcBASRsCXSNiRj5uHHBAOeO21m/hQvjd7+CrX01rTOy8M8ydm0oSW25Z6ejM2p9y9we5GDgD\nWKuBz48Drsvb3YGHCj6bB/QgTVo4r2D//LzfOqDnnoPLL4e//AV22y1N8rfHHu7+alZuZUsWkvYD\nXo+ImZKq6vn8/4BFEXFtKe87atSoZdtVVVVUVX3q1tbGLFkCt98Ov/lNaov49rdh5kzYaKNKR2bW\n9lRXV1NdXb3C5xW1BvfKkDQaGA4sATqTShc3RcRRko4BTgD2iIiP8vFnAkTE+fn9FGAk8CIwPSL6\n5v2HA4Mi4jv13NOLH7Ujr7+epuH47W+hZ0/4n/+Bgw9O03OYWWmUYvGjZomIsyOiV0T0Bg4D7s6J\nYgipampobaLIJgKHSVpdUm+gDzAjIhYA70oamBu8hwMTyhW3VVZE6sV05JGp6+u//rV8zesjjnCi\nMKuUlhrDKpb3hvo1sDowLXd2ejAiRkTEbEnjST2nlgAjCooJI4CrgS7ApIiY0kJxWwv54IPUg+my\ny+Ddd+Gkk+DSS2G99SodmZlBGauhKsHVUG3Lm2/CXXelrq633goDB6aqpr32glU8XNSsRRRbDeVk\nYS1m8eI04+sdd6QE8eyzqUfT4MHwjW9A796VjtCs43GysIqLgDlzUmKYOhWqq9Pqc3vtlRLEjju6\nDcKs0pwsrCLeeWd51dLUqWm50sGD0+vrX4cNNqh0hGZWyMnCWsSSJTBjxvLkMGsW7LLL8gTRr58H\nzJm1Zk4WVhbvvZcGxs2cCXffDdOnw8YbL08Ou+wCnTtXOkozK5aThTVLBLzyCjz++Cdfr7wCW2yR\n5mTabTfYc0/4whcqHa2ZrSwnCyvakiVpYaDahDBzZvoToH//tHhQ7WvTTb3CnFl74mRh9Vq4MFUj\nFZYWnn4aevT4ZFLYemvYcEO3N5i1d04WHVBEGug2b94nX/Pnpz///e+0QNAWW3wyKWy1FXTtWuno\nzawSnCzamZqaNLFe3URQNymsuWYqJfTs+enXRhulcQ6uRjKzWk4WbcjSpanhuG5JoPD16qtpDen6\nkkDtq0ePlCzMzIrlZNHKLV6cBq/dcEOaF2mNNRpPBN27e7SzmZWek0UrtGjRJxPEZpvBIYfAQQd5\nIR8zqwwni1aiNkGMHw8TJy5PEAcfDL16VTo6M+vonCwqaNEiuPPOVIKYOBE233x5CcIJwsxaEyeL\nFrZoEUybtjxB9Ou3PEH07FmRkMzMmuRk0QI+/nh5grjtNicIM2t7nCzKpDZBjB8Pt9+eBrjVJoge\nPcp6azOzknOyKKGPP07Tb99wQ0oQW265PEF0717y25mZtZhik0XZVzqW1EnSTEm35ffrSZom6XlJ\nUyWtU3DsWZJekPSspMEF+wdImpU/G1PumAE++ii1PQwfnmZV/dWvYPvt4amn4N574bvfdaIws46j\n7MkCOBWYDdR+5T8TmBYRmwJ35fdI6gcMA/oBQ4DLpWXT2F0BHB8RfYA+koaUI9DaBHHkkWkSvQsv\nhIEDYfZsuOceOPlkJwgz65jKmiwk9QT2Aa4Ean/x7w+MzdtjgQPy9lDguohYHBFzgTnAQEkbAl0j\nYkY+blzBOc320UdpgFxtgrjoorQ2dGGC2HDDUt3NzKxtKveUchcDZwBrFezrFhGv5e3XgG55uzvw\nUMFx84AewOK8XWt+3r/SPvoIpkxJbRCTJqWZVw85JFU1eSEfM7NPK1uykLQf8HpEzJRUVd8xERGS\nStoiPWrUqGXbVVVVVFWlW3/44ScTxDbbpARx0UXQrVv91zIza2+qq6uprq5e4fPK1htK0mhgOLAE\n6EwqXdwMbAdURcSCXMU0PSI2l3QmQEScn8+fAowEXszH9M37DwcGRcR36rnnJ3pD1SaI8eNh8uTl\nCeLAA50gzMyglXWdlTQI+H5EfEPSBcCbEfGLnCDWiYgzcwP3tcD2pGqmO4Ev59LHw8ApwAzgb8Cl\nETGlnvvEBx8EkyenEsTkyTBgQEoQ3/ymE4SZWV3FJouWXAanNiudD4yXdDwwFzgUICJmSxpP6jm1\nBBhRUEwYAVwNdAEm1Zcoap1/Pvz97ylBjBkDn/98WZ7FzKxDaXeD8mpqwutGm5kVqdUMymtpThRm\nZqXX7pKFmZmVnpOFmZk1ycnCzMya5GRhZmZNcrIwM7MmOVmYmVmTnCzMzKxJThZmZtYkJwszM2uS\nk4WZmTXJycLMzJrkZGFmZk1ysjAzsyY5WZiZWZOcLMzMrElOFmZm1iQnCzMza1LZkoWkzpIelvS4\npNmSfp73by9phqSZkh6RtF3BOWdJekHSs5IGF+wfIGlW/mxMuWI2M7P6lS1ZRMRHwO4RsTXwFWB3\nSbsAvwDOiYj+wI+BCwAk9QOGAf2AIcDl0rJFUq8Ajo+IPkAfSUPKFXdrVV1dXekQysrP17b5+dq/\nslZDRcQHeXN1oBPwNrAAWDvvXweYn7eHAtdFxOKImAvMAQZK2hDoGhEz8nHjgAPKGXdr1N7/sfr5\n2jY/X/u3ajkvLmkV4DHgS8AVEfG0pDOB+yX9ipSsdsyHdwceKjh9HtADWJy3a83P+83MrIWUu2RR\nk6uhegK7SaoC/gicEhEbAacBV5UzBjMzaz5FRMvcSDoH+BD4cUSslfcJeCci1s4lDiLi/PzZFGAk\n8CIwPSL65v2HA4Mi4jv13KNlHsbMrB2JCDV1TNmqoSStDyyJiHckdQH2BH4CzJE0KCLuAb4GPJ9P\nmQhcK+kiUjVTH2BGRISkdyUNBGYAw4FL67tnMQ9sZmYrrpxtFhsCY3O7xSrAnyPiTkknAr+RtAap\npHEiQETMljQemA0sAUbE8mLPCOBqoAswKSKmlDFuMzOro8WqoczMrO1qlyO4JX1PUo2k9SodSylJ\n+qWkZyQ9IelmSWs3fVbrJ2lIHoj5gqQfVjqeUpLUS9J0SU9LekrSKZWOqdQkdcqDbG+rdCylJmkd\nSTfm/3ezJe1Q6ZhKKQ+EfjoPer421/jUq90lC0m9SO0jL1Y6ljKYCmwREV8ltfWcVeF4mk1SJ+Ay\n0kDMfsDhkvpWNqqSWgycFhFbADsA/9POng/gVFL1cXusphhDqvruSxpc/EyF4ykZSZsAJwDbRMRW\npLFwhzV0fLtLFsBFwA8qHUQ5RMS0iKjJbx8mdUlu67YH5kTE3IhYDFxPGqDZLkTEgoh4PG8vJP2y\n6V7ZqEpHUk9gH+BKoF11MMkl910j4iqAiFgSEf+tcFil9C7py8yaklYF1mT5IOlPaVfJQtJQYF5E\nPFnpWFrAccCkSgdRAj2Alwve1w7GbHfyN7n+pETfXlwMnAHUNHVgG9Qb+I+kP0l6TNIfJK1Z6aBK\nJSLeAi4EXgJeIQ1juLOh49tcspA0Ldev1X3tT6qWGVl4eIXCXGmNPN83Co75P2BRRFxbwVBLpT1W\nXXyKpM8CNwKn5hJGmydpP+D1iJhJG/y/VoRVgW2AyyNiG+B94MzKhlQ6kr4E/C+wCam0+1lJRzR0\nfFmn+yiHiNizvv2StiR9E3gizz/YE/iHpO0j4vUWDLFZGnq+WpKOIRX792iRgMpvPtCr4H0vPjm9\nS5snaTXgJuAvETGh0vGU0E7A/pL2AToDa0kaFxFHVTiuUplHqql4JL+/kXaULIBtgQci4k0ASTeT\n/k6vqe/gNleyaEhEPBUR3SKid0T0Jv1Fb9OWEkVT8my7ZwBD86y+7cGjpJmEN5G0Omnm4YkVjqlk\n8iwFfwRmR8QllY6nlCLi7Ijolf+/HQbc3Y4SBRGxAHhZ0qZ519eBpysYUqk9C+wgqUv+d/p1UkeF\nerW5ksUKaI/VG78mzeA7LZeeHoyIEZUNqXkiYomkk4E7SL0x/hgR7abHCbAzcCTwpKSZed9Z7XRg\naXv8P/dd4Jr8ReafwLEVjqdkIuIJSeNIX9hqSJO+/r6h4z0oz8zMmtRuqqHMzKx8nCzMzKxJThZm\nZtYkJwszM2uSk4WZmTXJycLMzJrkZGGtkqTP5WmvZ0p6VdK8vP2epMtKeJ9f5bXhiz2+u6Qbijju\nLkldVzCWTSTNWpFzyknS3PY2zb+tPI+zsFZP0kjgvYi4qMTX7QrcFRHbl/K6+donAF1XJOY80eBt\nebroipP0b2BAnnDOOjiXLKytEICkqtpFdiSNkjRW0r35W/CBuaTwpKTJedplJA2QVC3pUUlTJH0h\nX3MosGyWzXyN0bkE86ikbSRNlTRH0v/Lxyz79i/pGKVFqCZLel7SLwrinUgjawM0+bBS5zzb6ZN5\nxtOqvH9NSePzgjU3S3pI0oB6zj8/H/OEpF/mfd0k3SLp8fzaIe+/JT/vUznJ1RfPkZIezj+b3yot\nl2wdiP/Cra3rDewO7A/8BZgWEV8hre++b57E79fAQRGxLfAn4Lx87i6kqQ5qBfBiRPQH7iWt+/5N\n0qJF5zZw/68ChwJbAcOU1ncgIl4D1pf0mZV8rv8BluZnOZy0nv0apPXo38yLKZ0DDKDONBuSPgcc\nEBG1C2X9NH90KTA9IrYmzaZaOw/Qcflnsx1wiqR161yvb37GnfLPpgZocHZSa5/a89xQ1v4FMDki\nlkp6ClglIu7In80iTb28KbAFcGeeT6sTae5+gI2AV+tcc2LB+Z+JiPeB9yV9LGmtemK4KyLeA5A0\nG9iY5bPmvkaaRffZlXi2nUm/3ImI5yS9mJ9lZ+CSvP9pSfWt3fIO8JGkPwK35xekpHpkPreGtPgN\nwKmSDsjbvYA+wIz8XqQZjgcAj+afYRdgwUo8k7VhThbW1i2C9MtP0uKC/TWkf98Cno6InRo4v27p\n+uOC8xfVc726Pi7YXkpKRrXEp7/1H8DyNVeOj4jHGoir9vwV2Q9ATp7bk37JHwyczPIp7T9xbq7e\n2gPYISI+kjSdNN14XWMj4uzG7mvtm6uhrC0rZsGd54ANCurnV5PUL3/2IvCFBs5b2cV8Cs/rRp21\nOSJiQkT0z6/GEsV95KqePEX2RqRn+TupSoj8HJ9qDM9VX+tExGTgdFJVGcBdwEn5mE65pLQW8HZO\nFJuTqtw+EXI+72BJG+Rz15O0UeM/BmtvnCysrYiCP+vbhk9PkR15Xe+DgV9IehyYCeyYP7+ftABM\nfec3du2G7r/ss9yI/mauxloRtde7HFglVzNdDxwdEYvy/g0kPU1qi3gaqLsudFfgNklPkJLOaXn/\nqcDu+ZqPAn2BKcCquQrt58CDnwooTRn/I2BqvuZUGk6y1k6566x1WEpLnU6PiO3KcO0TSW0eF5f4\nuqsAq0XEx0rLYk4DNo2IJaW8j1ldbrOwDisiFkqaLmn3iJhe4ssPI3XNLbXPAHfnXl4CTnKisJbg\nkoWZmTXJbRZmZtYkJwszM2uSk4WZmTXJycLMzJrkZGFmZk1ysjAzsyb9f7OvE0+0Y33YAAAAAElF\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5dbcf50>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex12-pg346"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "#calculate SC\n",
      "LL=40.\n",
      "Cc=0.009*(LL-10)\n",
      "H=10.*12.\n",
      "eo=1.0\n",
      "Gss=120.\n",
      "Gsc=110.\n",
      "Gd=100.\n",
      "To=10.*Gd +10.*(Gss-62.4)+10.*(Gsc-62.4)/2.\n",
      "\n",
      "Tt=0.408\n",
      "Tm=0.232\n",
      "Tb=0.019\n",
      "Tav= (Tt+4.*Tm+Tb)/6.\n",
      "Sc=Cc*H*math.log10((To+Tav*1000.)/To)/(1.+eo)\n",
      "print'%s %.3f %s'%('Sc =',Sc,' in')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Sc = 0.826  in\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex13-pg356"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#intilization variable\n",
      "#Calculate total primary\n",
      "import math\n",
      "H = 6.\n",
      "Cc = 0.28\n",
      "eo = 0.9\n",
      "Cv = 0.36\n",
      "To=210.\n",
      "Tp=115.\n",
      "Sc= Cc*H*math.log10((To+Tp)/To)/(1+eo)\n",
      "t2=9.\n",
      "Hd=3.\n",
      "Tv=Cv*t2/Hd**2\n",
      "U=0.67\n",
      "Tf=0.677*Tp\n",
      "print'%s %.1f %s'%('Tf =',Tf,' kN/m^2')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Tf = 77.9  kN/m^2\n"
       ]
      }
     ],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}