{ "metadata": { "name": "chapter 14 som.ipynb" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Chapter 14:Dams and Retaining Walls" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem no 14.1,Page No.325" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "%matplotlib inline\n", "\n", "#Initilization of variables\n", "b=2 #m #width \n", "FOS=1.5 #Factor of safety\n", "#rho_mason=2.5*rho_w\n", "mu=0.5 #coeffeicient of friction\n", "\n", "#Calculations\n", "\n", "#Let L=1 m (length of dam)\n", "L=1\n", "#W=b*H*L*rho\n", "#After substituting values and Further simplifying we get\n", "#W=2*H*rho\n", "\n", "#Total Pressure\n", "#P=W*H**2*2**-1\n", "\n", "x_bar=b*2**-1 #Distance of Line of action of W from waterface\n", "\n", "#Distance of pt where resultant cuts the base measured from Line of action\n", "#x=P*W**-1*H*3**-1\n", "#After substituting values and Further simplifying we get\n", "#x=H**2*30**-1\n", "\n", "#x_bar+x=2*b*3**-1\n", "#After substituting values and Further simplifying we get\n", "#1+H**2*30**-1=2*b*3**-1\n", "H=(30*(2*b*3**-1-1))**0.5 #height of dam\n", "\n", "#Frictional Resistance offered at the base\n", "#F=mu*W\n", "#After substituting values and Further simplifying we get\n", "#F=3.16*rho\n", "\n", "#Total Lateral Pressure\n", "#P=W*H**2*2**-1\n", "#P=4.99*W\n", "\n", "#Factor of safety against sliding\n", "#FOS1=F*P**-1=3.16*4.99**-1*rho_mason*rho_w**-1\n", "FOS1=3.16*4.99**-1*2.5\n", "\n", "#FOS1>FOS\n", "\n", "#Result\n", "print\"Dam is safe against sliding\",round(FOS1,2)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Dam is safe against sliding 1.58\n" ] } ], "prompt_number": 2 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem no 14.2,Page No.327" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "%matplotlib inline\n", "\n", "#Initilization of variables\n", "D=2 #m #External Diameter\n", "d=1.5 #m #Internal Diameter\n", "P=1600 #N/m**2 #N/m**2 #Wind Pressure\n", "W=19200 #N/m**2 #Weight of masonry\n", "\n", "#Calculations\n", "\n", "#Let H be max height of dam\n", "\n", "#W2=pi*4**-1*(D**2-d**2)*H*W #weight of chimney\n", "#W2=26400*H\n", "\n", "#Eccentricrty\n", "x=(D**2+d**2)*(8*D)**-1\n", "\n", "#P2=H*D*P #Lateral thrust of wind on chimney\n", "#P2=3200*H\n", "\n", "#Now by using the relation we get P*W**-1=x*(H*2**-1)**-1\n", "#After substituting values and Further simplifying we get\n", "H=0.39*2*26400*3200**-1\n", "\n", "#result\n", "print\"The Height of Dam is\",round(H,2),\"m\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The Height of Dam is 6.44 m\n" ] } ], "prompt_number": 3 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem no 14.3,Page No.327" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "%matplotlib inline\n", "\n", "#Initilization of variables\n", "rho_w=10 #KN/m**3 #Density of water\n", "rho_mason=22.4 #KN/m**3 #Density of mason\n", "H=6 #m #height of dam\n", "a=1 #m #width of top\n", "b=4 #m #bottom width\n", "h=5.5 #m #Weight of water depth \n", "\n", "#Calculations\n", "\n", "#Let L=1 m (length of dam)\n", "L=1\n", "\n", "#weight of dam\n", "W=(a+b)*2**-1*H*a*rho_mason\n", "\n", "#Lateral thrust\n", "P=rho_w*h**2*a*2**-1\n", "\n", "#distance of Line of action of W measured from vertical face\n", "x_bar=(b**2+b*a+a**2)*(3*(b+a))**-1\n", "\n", "#distance of pt where resultant cuts the base\n", "x=P*W**-1*h*3**-1\n", "\n", "#Eccentricity\n", "e=x_bar+x-b*2**-1\n", "\n", "#Stress at Pt B\n", "sigma1=W*b**-1*(1-6*e*b**-1)\n", "\n", "#stress at Pt C\n", "sigma2=W*b**-1*(1+6*e*b**-1)\n", "\n", "#Result\n", "print\"Max stress intensities at the base is\",round(sigma2,2),\"KN/m**2\"\n", "print\"Min stress intensities at the base is\",round(sigma1,2),\"KN/m**2\"\n", "\n", "#Plotting the Shear Force Diagram\n", "\n", "X1=[0,L,L]\n", "Y1=[sigma2,sigma1,0]\n", "Z1=[0,0,0]\n", "plt.plot(X1,Y1,X1,Z1)\n", "plt.xlabel(\"Length x in m\")\n", "plt.ylabel(\"Shear Force in kN\")\n", "plt.show()\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Max stress intensities at the base is 112.38 KN/m**2\n", "Min stress intensities at the base is 55.62 KN/m**2\n" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEPCAYAAABY9lNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "" ] } ], "prompt_number": 29 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem no 14.4,Page No.329" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "%matplotlib inline\n", "\n", "#Initilization of variables\n", "H=10 #m #height od dam\n", "a=2 #m #top width\n", "b=5 #m #bottom width\n", "W=25 #KN/m**3 #weight of mason\n", "rho_w=10 #KN/m**3 #density of water\n", "\n", "#Calculations\n", "\n", "#Let L=1 m (length of dam)\n", "L=1\n", "\n", "#weight of dam\n", "W2=(b+a)*H*L*W*2**-1\n", "\n", "##Lateral thrust\n", "P=rho_w*H**2*L*2**-1 \n", "\n", "#Resultant thrust\n", "R=(P**2+W**2)**0.5\n", "\n", "#Distance of Line of action from vertical base\n", "x_bar=(b**2+b*a+a**2)*(3*(b+a))**-1\n", "\n", "##distance of pt where resultant cuts the base\n", "x=P*W2**-1*H*3**-1\n", "\n", "#Eccentricity\n", "e=x_bar+x-b*2**-1\n", "\n", "#Stress at Pt B\n", "sigma1=W2*b**-1*(1-6*e*b**-1)\n", "\n", "#stress at Pt C\n", "sigma2=W2*b**-1*(1+6*e*b**-1)\n", "\n", "#Result\n", "print\"The Resultant Thrust on the base is\",round(R,2),\"KN\"\n", "\n", "#Plotting the Shear Force Diagram\n", "\n", "X1=[0,L,L]\n", "Y1=[-sigma2,-sigma1,0]\n", "Z1=[0,0,0]\n", "plt.plot(X1,Y1,X1,Z1)\n", "plt.xlabel(\"Length x in m\")\n", "plt.ylabel(\"Shear Force in kN\")\n", "plt.show()\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The Resultant Thrust on the base is 500.62 KN\n" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEPCAYAAABsj5JaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "" ] } ], "prompt_number": 28 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem no 14.5,Page No.330" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "%matplotlib inline\n", "\n", "#Initilization of variables\n", "H=30 #m #height of Dam\n", "h=29 #m #height of water\n", "rho_w=9810 #N/m**3 \n", "rho_mason=22560 #N/m**3\n", "sigma1=0 #KN/m**3\n", "sigma2=880 #KN/m**3\n", "\n", "#Calculations\n", "\n", "#Let L=1 m (length of dam)\n", "L=1\n", "\n", "#weight of dam\n", "#W=(a+b)*2**-1*L*H*rho_mason*10**-3\n", "#After substituting values and Further simplifying we get\n", "#W=338.4*(a+b) #equation1\n", "\n", "#Pressure at B=0, Sinc etension at base has just been avoided\n", "\n", "#Eccentricity\n", "e=b*6**-1 #as sigma1=0\n", "\n", "#Pressure at C\n", "#sigma2=W2*b**-1*(1+6*e*b**-1)\n", "#After substituting values and Further simplifying we get\n", "#W=440*b\n", "\n", "#From equation1,440*b=338*(a+b)\n", "#b=3.33*a\n", "\n", "#the distance of C.Gof dam\n", "#x_bar=(b**2+b*a+a**2)*(3*(b+a))**-1\n", "#After substituting values and Further simplifying we get\n", "#x_bar=1.187*a\n", "\n", "#distance of pt where resultant cuts the base\n", "#x=P*W2**-1*H*3**-1\n", "#After substituting values and Further simplifying we get\n", "#x=27.214*a**-1\n", "\n", "#Now x_bar+x=2*3**-1*b\n", "#After substituting values and Further simplifying we get\n", "a=(27.17*(2.22-1.187)**-1)**0.5\n", "b=3.33*a\n", "\n", "#Result\n", "print\"The top width dam is\",round(a,2),\"m\"\n", "print\"The bottom width dam is\",round(b,2),\"m\"\n", "\n", "\n", "#Plotting the Shear Force Diagram\n", "\n", "X1=[0,L,L]\n", "Y1=[sigma2,sigma1,0]\n", "Z1=[0,0,0]\n", "plt.plot(X1,Y1,X1,Z1)\n", "plt.xlabel(\"Length x in m\")\n", "plt.ylabel(\"Shear Force in kN\")\n", "plt.show()\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The top width dam is 5.13 m\n", "The bottom width dam is 17.08 m\n" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEPCAYAAAC3NDh4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtclHXax/HPIGh5Pg8JFiYgIQp4QisLD6DmymNlFLaF\numqba237tBlrbeHWClbbI1luW6mxHTTLXaWDpqZYrhpWrJaURzTOWyJrRobA/fxx2+QJGZGZe4Dv\n+/Xi1cDMfc/F/cq5uO7f73f9bIZhGIiIiJyDl9UBiIiI51KSEBGRGilJiIhIjZQkRESkRkoSIiJS\nIyUJERGpkUuTRFpaGn369CEsLIy0tDQASktLiYmJITg4mNjYWMrKyhyvT0lJISgoiJCQENauXevK\n0ERExAkuSxJffPEFL730Etu3b2fHjh2888477N+/n9TUVGJiYtizZw8jRowgNTUVgJycHN544w1y\ncnJYs2YNM2bMoLq62lXhiYiIE1yWJL766iuioqK45JJLaNasGddffz0rVqwgIyODxMREABITE1m5\nciUAq1atIiEhAR8fHwICAggMDCQrK8tV4YmIiBNcliTCwsL46KOPKC0tpby8nPfee4/8/HxKSkqw\n2+0A2O12SkpKACgsLMTf399xvL+/PwUFBa4KT0REnODtqhOHhITw4IMPEhsbS6tWrYiIiKBZs2an\nvcZms2Gz2Wo8x/meExER13NZkgCYMmUKU6ZMAeChhx7C398fu91OcXExvr6+FBUV0bVrVwD8/PzI\ny8tzHJufn4+fn99Z5wwMDGT//v2uDFtEpNHp2bMn+/btu/ADDRcqKSkxDMMwDh06ZISEhBhlZWXG\nAw88YKSmphqGYRgpKSnGgw8+aBiGYezatcsIDw83fvzxR+PAgQPGlVdeaVRXV591TheH3KA8+uij\nVofgMXQtfqZr8TNdi5/V9bPTpZXEhAkTOHz4MD4+PixcuJB27dqRlJREfHw8ixYtIiAggOXLlwMQ\nGhpKfHw8oaGheHt7s3DhQt1uEhGxmEuTxIcffnjWzzp27Mj69evP+frZs2cze/ZsV4YkIiIXoEGu\nuP7iC6sj8AzR0dFWh+AxdC1+pmvxM12Li2c7ea+qwbDZbHTubDBzJvzhD9C8udURiYh4PpvNRl0+\n7htkJZGdDZ98Av37w/btVkcjItJ4Ncgk4e8PGRkwezaMGwcPPADl5VZHJSLS+DTIJAFgs0FCAnz+\nOeTnQ3g4nGOcXERELkKDHJM4V8gZGTBjBsTFQWoqtG1rQXAiIh6qSY1JnEtcnDnrqaIC+vSB1aut\njkhEpOFrNJXEqdavh2nTYOhQmD8fOnZ0U3AiIh6qyVcSpxo50hyr6NgRwsLgrbesjkhEpGFqlJXE\nqbZsgV/9CkJD4bnnwNfXhcGJiHgoVRI1uPpqc11FSIg5Ayo9HRpWWhQRsU6jryROlZ0NU6aA3Q4v\nvACXX17PwYmIeChVEk6IjISsLLjuOnO19sKFoG20RURq1qQqiVN99ZU5VtGsGbz0EgQH10NwIiIe\nSpXEBQoJMVdoT5hgjls88QRUVlodlYiIZ2mylcSpcnPNdRVlZbB4MfTtW6+nFxGxnEdWEikpKfTu\n3Zs+ffowceJEfvzxR0pLS4mJiSE4OJjY2FjKyspOe31QUBAhISGsXbvWlaGdpkcPWLcO7r7bXGPx\n6KPw449ue3sREY/lskri4MGDDB8+nC+//JIWLVpw6623csMNN7Br1y46d+7MrFmzmDdvHkeOHCE1\nNZWcnBwmTpzI9u3bKSgoYOTIkezZswcvr9PzmCsqiVMVFprJYv9+WLQIoqJc9lYiIm7jcZVE27Zt\n8fHxoby8nMrKSsrLy+nWrRsZGRkkJiYCkJiYyMqVKwFYtWoVCQkJ+Pj4EBAQQGBgIFlZWa4Kr0bd\nusHKlfDHP8L//A/cf7/akItI0+WyJNGxY0fuv/9+Lr/8crp160b79u2JiYmhpKQEu90OgN1up6Sk\nBIDCwkL8/f0dx/v7+1NQUOCq8M7LZoNbbzVbexQXm2MUmZmWhCIiYilvV514//79zJ8/n4MHD9Ku\nXTtuueUWXn311dNeY7PZsNlsNZ6jpueSk5Mdj6Ojo122j22XLvDaa/D223DHHTB2rDkLSm3IRcTT\nZWZmklkPf926LEl88sknXH311XTq1AmAm266ia1bt+Lr60txcTG+vr4UFRXRtWtXAPz8/MjLy3Mc\nn5+fj5+f3znPfWqScIdx48wFeA88YDYMfP55uOEGt4YgInJBzvwDes6cOXU6j8tuN4WEhLBt2zZ+\n+OEHDMNg/fr1hIaGMm7cONLT0wFIT09n/PjxAMTFxbFs2TIqKirIzc1l7969DBo0yFXhXbB27cxW\nHi+/DPfcA7/8JRw+bHVUIiKu5bJKIjw8nDvvvJMBAwbg5eVFv379mD59Ot999x3x8fEsWrSIgIAA\nli9fDkBoaCjx8fGEhobi7e3NwoULz3sryirDh8POnfDww2ZVkZYGt9xijmOIiDQ2Wkx3EbZtMxsG\n9upl9oG67DKrIxIROTePmwLbFAwebHaWDQsz25AvWaI25CLSuKiSqCc7dphVRadO5thFQIDVEYmI\n/EyVhMXCw+Hjj80xi4ED4dln1YZcRBo+VRIusHu32YYczNYevXpZG4+IiCoJD9Krl9mG/Lbb4Jpr\nIDVVbchFpGFSJeFiBw/C9OnmmorFi83bUiIi7qZKwkMFBMD778PMmRATYzYOVBtyEWkolCTcwGaD\nyZPNGVBffGHutb1tm9VRiYjUTreb3Mww4K234N57zTGLxx+HVq2sjkpEGjvdbmogbDazjcfnn8O3\n35ptyDdssDoqEZFzUyVhsXffNXfCGz0annzSbCQoIlLfVEk0UGPHmuMUXl5me4933rE6IhGRn6mS\n8CCZmTB1qrmvdloadO5sdUQi0liokmgEoqPNNuS+vmZVsWyZGgaKiLVUSXiojz82W3v07Al//St0\n62Z1RCLSkKmSaGSiouDTTyEiwvxatEhVhYi4n0uTxO7du4mMjHR8tWvXjmeeeYbS0lJiYmIIDg4m\nNjaWsrIyxzEpKSkEBQUREhLC2rVrXRmex2vRAubMgfXrzWoiNhZyc62OSkSaErfdbqqursbPz4+s\nrCwWLFhA586dmTVrFvPmzePIkSOkpqaSk5PDxIkT2b59OwUFBYwcOZI9e/bg5fVzLmsqt5vOVFkJ\nTz8NTzwBjzwCv/kNNGtmdVQi0lB4/O2m9evXExgYSPfu3cnIyCAxMRGAxMREVq5cCcCqVatISEjA\nx8eHgIAAAgMDycrKcleIHs3bG2bNgi1bzBXb110HX35pdVQi0ti5LUksW7aMhIQEAEpKSrDb7QDY\n7XZKSkoAKCwsxN/f33GMv78/BQUF7gqxQQgONqfK3n47DB0Kc+fCiRNWRyUijZW3O96koqKCt99+\nm3nz5p31nM1mw2az1XjsuZ5LTk52PI6OjiY6Oro+wmwwvLxgxgxzId5dd8Gbb5ptyCMjrY5MRDxF\nZmYmmZmZF30etySJ1atX079/f7p06QKY1UNxcTG+vr4UFRXRtWtXAPz8/MjLy3Mcl5+fj5+f31nn\nOzVJNGVXXAGrV8Pf/w6jRsG0aWYr8ksusToyEbHamX9Az5kzp07nccvtpqVLlzpuNQHExcWRnp4O\nQHp6OuPHj3f8fNmyZVRUVJCbm8vevXsZNGiQO0JssGw2SEw0F+F99ZVZTWzZYnVUItJYuHx20/ff\nf88VV1xBbm4ubdq0AaC0tJT4+Hi+/vprAgICWL58Oe3btwdg7ty5LF68GG9vb9LS0hg1atTpATfR\n2U3O+qkN+S23wJ//DK1bWx2RiHiCun52asV1I3T4MPzv/8JHH8ELL8DIkVZHJCJWU5KQs6xeDb/+\ntblt6lNPwcliTUSaII9fJyHuN2aMublR8+Zmw8CMDKsjEpGGRpVEE7Fpk9mGfMAAeOYZODnRTESa\nCFUScl7XXw87doC/P/TpA0uXqmGgiNROlUQTtH07TJkCAQHw/PNwjqUoItLIqJIQpw0caLYhHzDA\nbEP+4ouqKkTk3FRJNHGff25ubtSmjZksrrzS6ohExBVUSUid9OljrtAeMwYGDYL586GqyuqoRMRT\nqJIQh717zRlQFRXmTnihoVZHJCL1RZWEXLSgINi4Ee6805wN9fjjakMu0tSpkpBz+vprsw15UZHZ\nhrxfP6sjEpGLoUpC6tXll8N778H995vjFX/4Axw/bnVUIuJuShJSI5sN7rjDbEO+bx+Eh8PmzVZH\nJSLupNtN4rR//ANmzoSbb4aUFLUhF2lIdLtJXO6mm+CLL+DYMXPq7Lp1VkckIq5WYyUxbNiwcx9w\ncs/pDRs2uC6q81Al4Rnef98c2B4+HP7yF+jQweqIROR86n0/iU8++eS0kwNs27aNefPm0bVr19Oe\nP5+ysjKmTp3Krl27sNlsLFmyhKCgIG699VYOHTp01s50KSkpLF68mGbNmvHMM88QGxtbL7+o1L/v\nvjMHtP/5T3juOTi5C62IeCCXbjqUmZnJ448/zg8//MDDDz/MmDFjnH6DxMRErr/+eqZMmUJlZSXf\nf/89f/7zn+ncuTOzZs1i3rx5HDlyhNTUVHJycpg4cSLbt2+noKCAkSNHsmfPHry8fr4rpiTheT76\nyGztERkJCxZA165WRyQiZ6rzZ6dxHqtXrzauvfZaY/jw4caGDRvO99JzKisrM3r06HHWz3v16mUU\nFxcbhmEYRUVFRq9evQzDMIy5c+caqampjteNGjXK2Lp162nH1hKyWKS83DBmzTIMu90wXn3VMKqr\nrY5IRE5V189O75qSx8CBA/nmm2/4/e9/z5AhQwD47LPPHM/3c2J1VW5uLl26dGHy5Mns2LGD/v37\nM3/+fEpKSrDb7QDY7XZKSkoAKCwsZPDgwY7j/f39KSgouPDMJ2536aUwbx7ccovZhnzpUvjrX6F7\nd6sjE5GLUWOSaNWqFa1atWLFihWsWLHirOc3btxY68krKyv57LPPePbZZxk4cCD33Xcfqampp73G\nZrM5xjzO5VzPJScnOx5HR0cTHR1dayziHgMGwCefQGqquUr78cdh2jTw0jw6EbfKzMwkMzPzos9T\n65hEdXX1aWMCAMePH+eSSy6p9eTFxcUMGTKE3NxcADZv3kxKSgoHDhxg48aN+Pr6UlRUxLBhw/jq\nq68cCSQpKQmA0aNHM2fOHKKion4OWGMSDcauXWZV0bIlvPQS9OxpdUQiTZfL1klMnTr1tO+PHTvG\nDTfc4NTJfX196d69O3v27AFg/fr19O7dm3HjxpGeng5Aeno6409Oi4mLi2PZsmVUVFSQm5vL3r17\nGTRo0AX9QuI5evc225CPGwdRUfD002pDLtLQ1FpJ/PGPf+Tw4cMsXLiQI0eOMHbsWKZNm8bkyZOd\neoMdO3YwdepUKioq6NmzJ0uWLKGqqor4+Hi+/vrrs6bAzp07l8WLF+Pt7U1aWhqjRo06PWBVEg3S\nvn3mbafycrNhYO/eVkck0rS4dArsAw88wNGjR/n0009JSkpiwoQJdQqyPihJNFzV1ebudw8/DPfc\nA0lJ0Ly51VGJNA31niR+Gqz+6cSPPfYYAwcOZPTo0dhsNm666aaLi7iOlCQavrw8+PWvzf8uXmwO\ndouIa9V7kpg0adJpM4sMwzjt+yVLltQhzIunJNE4GAa8/jr87//CpEmQnGxOoxUR13Dp7SZPoiTR\nuPznP+atp+xsc8vUoUOtjkikcVKSkAZt5Ur4zW/gxhvNNuRt2lgdkUjjolbh0qCNH2+2IS8vN9uQ\nv/++1RGJCKiSEA+0di1Mnw7R0ebaio4drY5IpOFz2e2m48ePs2LFCg4ePEhlZaXjzR555JG6RXqR\nlCSahmPHYPZseOstePZZc8MjEak7lyWJUaNG0b59e/r370+zZs0cP7///vsvPMp6oCTRtGzeDFOn\nmregnn0WTvaFFJEL5LIkERYWxhdffFHnwOqbkkTTc/w4zJljrql46in45S/hPD0hReQcXDZwffXV\nV7Nz5846BSVSHy65xJzxtHq1uVXq2LHw9ddWRyXSNNRaSVx11VXs27ePHj160KJFC/Mgm82yxKFK\nomk7ccLctyItDf70J3OfbbUhF6mdy243HTx48Jw/DwgIuOA3qw9KEgKQk2Numdq8udmGPCjI6ohE\nPFu93246evQoAG3btj3nl4iVQkPNQe0bb4QhQ8yxipOT70SkHtVYSYwdO5Z3332XgICAs3aHs9ls\nHDhwwC0BnkmVhJzpwAGzDfl335mtPfr0sToiEc+jthzSpBmGedtp9myzvcfs2WpDLnIqj23LERAQ\nQN++fYmMjHTsMldaWkpMTAzBwcHExsZSVlbmeH1KSgpBQUGEhISwdu1aV4cnjYTNZlYT2dnw6afQ\nvz9s3251VCINn8uThM1mIzMzk+zsbLKysgBITU0lJiaGPXv2MGLECMfe1jk5Obzxxhvk5OSwZs0a\nZsyYQXV1tatDlEbE3x8yMsxKYtw4eOABsx+UiNSNWyYPnlniZGRkkJiYCEBiYiIrV64EYNWqVSQk\nJODj40NAQACBgYGOxCLiLJsNEhJg507Iz4fwcNi0yeqoRBomp5LERx995Nhk6JtvviE3N9fpN7DZ\nbIwcOZIBAwbw4osvAlBSUoL9ZH8Fu91OSUkJAIWFhfj7+zuO9ff3p6CgwOn3EjlV166wdKm5AO/2\n22HGDDg5aU9EnFRrkkhOTuaJJ54gJSUFgIqKCn75y186/Qb/+te/yM7OZvXq1Tz33HN89NFHpz1v\ns9nOmj115vMiFyMuzmxDXlFhznxavdrqiEQaDu/aXvDPf/6T7Oxs+vfvD4Cfnx/fffed029w2WWX\nAdClSxduvPFGsrKysNvtFBcX4+vrS1FREV27dnWcOy8vz3Fsfn4+fn5+Z50zOTnZ8Tg6Opro6Gin\n45GmqX17c/bT+vXmAPfQofB//wedOlkdmYhrZGZmkpmZedHnqXUK7KBBg8jKyiIyMpLs7Gy+//57\nhgwZ4lRbjvLycqqqqmjTpg3ff/89sbGxPProo6xfv55OnTrx4IMPkpqaSllZGampqeTk5DBx4kSy\nsrIoKChg5MiR7Nu377RqQlNg5WIdOwYPPwzLl8Mzz8CECVZHJOJ6df3srLWSuOWWW7jrrrsoKyvj\nhRdeYPHixUydOtWpk5eUlHDjjTcCUFlZye23305sbCwDBgwgPj6eRYsWERAQwPLlywEIDQ0lPj6e\n0NBQvL29WbhwoW43Sb1r3Rrmz4f4eLO1x9Kl8Nxz4OtrdWQinsepxXRr1651rFkYNWoUMTExLg+s\nJqokpD4dPw6PPQYvvghPPgl33qk25NI4uWzFdW5uLr6+vlx66aUA/PDDD5SUlKjBnzQq2dkwZYq5\nqdHf/gZXXGF1RCL1y2UrridMmHDajnReXl5M0E1caWQiIyErC667zlyt/dxzoHWcIk4kiaqqKpqf\n0gSnRYsWnDhxwqVBiVjBx8dcqb15M7z+OkRHw549VkclYq1ak0Tnzp1ZtWqV4/tVq1bRuXNnlwYl\nYqWQEPjwQ3PW09VXwxNPqA25NF21jkns27eP22+/ncLCQsBcBf3KK68QGBjolgDPpDEJcafcXHNd\nRVmZucd2375WRyRSNy6ZAltVVcXzzz/Pxx9/7FhA16ZNm7pFKNIA9egB69aZCWLECLj7bnjoITi5\nk69Io3fe203NmjVj8+bNGIZBmzZtlCCkSbLZzPUUO3aYX/36wccfWx2ViHvUervp17/+NYWFhdxy\nyy20bNnSPMhm46abbnJLgGfS7SaxkmGYK7V/+1uzaeBjj8HJfxYiHs1l6yQmTZrkeINT/dQV1t2U\nJMQTfPMN3HefWVG89JI5E0rEk2n7UhELvP222YJ87FhzFlTbtlZHJHJuLltMl5eXx4033kiXLl3o\n0qULN998M/n5+XUKUqSxGTfObENeXQ1hYfDee1ZHJFK/ak0SkydPJi4ujsLCQgoLCxk3bhyTJ092\nR2wiDUK7dvDCC/Dyy3DPPfDLX8K331odlUj9qDVJfPPNN0yePBkfHx98fHyYNGkS//nPf9wRm0iD\nMny4uWVqly7m5kbLl5sD3SINWa1JolOnTrzyyitUVVVRWVnJq6++qhXXIjVo1crczOif/4TkZLjp\nJigqsjoqkbqrNUksXryY5cuX4+vry2WXXcabb75p2cwmkYZi8GCzs2xYGISHw5IlqiqkYapxdtO2\nbdsYPHiwu+OplWY3SUPz73+bi/E6dTLHLizqsi9NXL3Pbrr77rsdj4cMGVK3qDBbe0RGRjJu3DgA\nSktLiYmJITg4mNjYWMrKyhyvTUlJISgoiJCQEMcmRyINXUSEuZ5i+HAYMAAWLFAbcmk4ar3dBHD8\n+PE6v0FaWhqhoaGOxXipqanExMSwZ88eRowYQWpqKgA5OTm88cYb5OTksGbNGmbMmEG1/iVJI+Ht\nDUlJ8K9/wRtvmPtW7N5tdVQitasxSVRVVVFaWsrhw4cdj0/9ckZ+fj7vvfceU6dOdZQ5GRkZJCYm\nApCYmMjKlSsBswV5QkICPj4+BAQEEBgYSFZW1sX+fiIepVcvsw35bbfBNddAaqrakItnq7EL7NGj\nR+nfvz8AhmE4HoN5b+vAgQO1nvx3v/sdTz75JEePHnX8rKSkBLvdDoDdbqekpASAwsLC08ZA/P39\nKSgouMBfR8TzeXnBzJnwi1/A9Onw5puwaJF5W0rE09SYJA4ePHhRJ37nnXfo2rUrkZGRZGZmnvM1\nNpvtrJ5QZz4v0lgFBMD775uL8GJjzYTxxz+qDbl4lvPuJ3ExtmzZQkZGBu+99x7Hjx/n6NGj3HHH\nHdjtdoqLi/H19aWoqIiuXbsC4OfnR15enuP4/Px8/Pz8znnu5ORkx+Po6Gii1V1NGiibDSZPhtGj\nzR5QkZHm3hUeOLFQGpjMzMwa/0C/EG5p8Ldp0yaeeuop3n77bWbNmkWnTp148MEHSU1NpaysjNTU\nVHJycpg4cSJZWVkUFBQwcuRI9u3bd1Y1oSmw0lgZhnnr6be/NccsHn/cXJwnUh9c1uCvvvz0YZ+U\nlMS6desIDg5mw4YNJCUlARAaGkp8fDyhoaGMGTOGhQsX6naTNCk2G8THw+efm72f+vaFDRusjkqa\nuvNWEpWVlfTu3ZvdHjRXT5WENBXvvmtulzp6NDz5pNlIUKSuXFJJeHt7ExISwqFDh+ocmIjUzdix\nZhtyLy+zvcc771gdkTRFtY5JDB06lOzsbAYNGkSrkzdIbTYbGRkZbgnwTKokpCnauBGmTYNBgyAt\nzew0K3IhXLYzXU2j41bNKFKSkKaqvNycIvvaazB/Ptx6qzmOIeIMbV8q0kR8/LHZMLBnT/jrX6Fb\nN6sjkobAZbObtm7dysCBA2ndujU+Pj54eXnRVhv5ilgmKgo+/dRcoR0RYa7W1t9N4iq1JomZM2fy\n+uuvExQUxPHjx1m0aBEzZsxwR2wiUoMWLWDOHFi/3qwmYmIgN9fqqKQxcmqdRFBQEFVVVTRr1ozJ\nkyezZs0aV8clIk7o2xe2bTPbegwcaA5qV1VZHZU0JrUmiVatWvHjjz8SHh7OrFmzePrppzUmIOJB\nvL1h1izYsgXeeguGDoUvv7Q6Kmksak0Sf//736murubZZ5+lZcuW5Ofns2LFCnfEJiIXIDgYNm2C\n2283E8XcuXDihNVRSUPn1Oym8vJy8vLy6NWrlztiOi/NbhKp3aFDcNddUFJiNgyMjLQ6IrGay2Y3\nZWRkEBkZyahRowDIzs4mLi7uwiMUEbe54gpYvRruuw9GjYLZs+EiNpiUJqzWJJGcnMzHH39Mhw4d\nAIiMjHRqwyERsZbNBomJsHOnuVVqZKQ5biFyIWpNEj4+PrRv3/70g7zc1jxWRC6Sry+sWAGPPQYT\nJpityI8dszoqaShq/bTv3bs3r732GpWVlezdu5d77rmHq6++2h2xiUg9mjDBbENeVmZOnV2/3uqI\npCGoNUksWLCAXbt20aJFCxISEmjbti3z5893R2wiUs86dYL0dHjuOZgyBaZONZOGSE3Uu0mkiTp6\nFJKSICMDFi4EzUdp3Fw2u2n37t1MmzaNmJgYhg0bxrBhwxg+fHitJz5+/DhRUVFEREQQGhrKH/7w\nBwBKS0uJiYkhODiY2NhYyk75MyYlJYWgoCBCQkJYu3btBf8yIuK8tm3N5PDaa3D//eaWqd98Y3VU\n4mlqrST69u3L3XffTb9+/WjWrJl5kM1G//79az15eXk5LVu2pLKykmuvvZannnqKjIwMOnfuzKxZ\ns5g3bx5Hjhw5bY/r7du3O/a43rNnz1mD5KokROpfeTk8+ii88go8/TQkJKgNeWNT189O79pe4OPj\nw913312noFq2bAlARUUFVVVVdOjQgYyMDDZt2gRAYmIi0dHRpKamsmrVKhISEvDx8SEgIIDAwECy\nsrIYPHhwnd5bRJzXsqW5RWp8vDlWsXQpPP88+PlZHZlYrcbbTaWlpRw+fJhx48bx3HPPUVRURGlp\nqePLGdXV1URERGC32xk2bBi9e/empKQEu90OgN1up6SkBIDCwkL8/f0dx/r7+1NQUHAxv5uIXKCB\nA8025P37m23IX3xRbcibuhoriX79+mE7pd586qmnHI9tNptTC+q8vLz497//zX//+19GjRrFxo0b\nT3veZrOd9h5nqum55ORkx+Po6GjLdskTaYyaN4fkZLj5ZnNzo6VLzWTRs6fVkcmFyMzMrHFn0QtR\nY5I4ePDgRZ/8J+3atWPs2LF8+umn2O12iouL8fX1paioiK5duwLg5+dHXl6e45j8/Hz8aqh1T00S\nIuIaffqYK7Tnzzc3OnroIbj3Xjg5NCke7sw/oOfMmVOn89R4u2n79u0UFRU5vk9PTycuLo57773X\nqdtN3377rWPm0g8//MC6deuIjIwkLi6O9PR0xznHjx8PQFxcHMuWLaOiooLc3Fz27t3LoEGD6vRL\niUj98PaG3/8etm6FlSvh2mshJ8fqqMSdakwS06dPp0WLFgB8+OGHJCUlkZiYSNu2bZk+fXqtJy4q\nKmL48OF1Qa+hAAAS7ElEQVREREQQFRXFuHHjGDFiBElJSaxbt47g4GA2bNhAUlISAKGhocTHxxMa\nGsqYMWNYuHDheW9FiYj7BAXBxo1w551w/fXw+ONqQ95U1DgFNjw8nB07dgDwm9/8hi5dujhu85z6\nnLtpCqyItb7+2mxDXlRktiHv18/qiMQZ9b6YrqqqihMn/1RYv349w4YNczxXWVlZhxBFpDG4/HJ4\n7z1zAd6YMeaq7R9+sDoqcZUak0RCQgLXX389cXFxtGzZkqFDhwKwd+/es7rCikjTYrPBHXeYbcj3\n7zeny27ebHVU4grnXXG9detWiouLiY2NpVWrVgDs2bOHY8eO0c+iGlO3m0Q8zz/+ATNnmtNmU1Kg\ndWurI5Iz1fWzUw3+RKRelJaat6AyM+Fvf4PYWKsjklMpSYiIR3j/fZg+HUaMgL/8BU5uaikWc1kX\nWBGRCzFqFHzxhdkPKizMXF8hDZcqCRFxmY8+Mlt7RETAggVwsm2bWECVhIh4nKFDYccO6NHD3DL1\n1VfVMLChUSUhIm7xySdmG/Lu3c025N27Wx1R06JKQkQ82oABZqKIijJXaf/tb1BdbXVUUhtVEiLi\ndrt2mVVFy5ZmG/LAQKsjavxUSYhIg9G7t9mGfNw4GDzYnCpbVWV1VHIuqiRExFL79sG0aeY+24sX\nmwlE6p8qCRFpkAID4YMPzNtP0dHwpz9BRYXVUclPlCRExHJeXmb78c8+g48//nmQW6ynJCEiHqN7\nd3jnHXjwQRg7FmbNUhtyq7k0SeTl5TFs2DB69+5NWFgYzzzzDAClpaXExMQQHBxMbGysY5tTgJSU\nFIKCgggJCWHt2rWuDE9EPJDNBrffDp9/DocOQXi4uXJbrOHSgevi4mKKi4uJiIjg2LFj9O/fn5Ur\nV7JkyRI6d+7MrFmzmDdvHkeOHCE1NZWcnBwmTpzI9u3bKSgoYOTIkezZswcvr59zmQauRZqWlSvh\nN7+B8eMhNRXatLE6oobJIweufX19iYiIAKB169ZcddVVFBQUkJGRQWJiIgCJiYmsPNkBbNWqVSQk\nJODj40NAQACBgYFkZWW5MkQR8XDjx5sNA3/4wWwY+P77VkfUtLhtTOLgwYNkZ2cTFRVFSUkJ9pOd\nvux2OyUlJQAUFhbi7+/vOMbf35+CggJ3hSgiHqpDB3N67IsvmgPckyaZ+1eI63m7402OHTvGzTff\nTFpaGm3OqBVtNhs2m63GY8/1XHJysuNxdHQ00dHR9RWqiHiw2Fizqpg926wqnn0WbrrJ6qg8U2Zm\nJpmZmRd9HpcniRMnTnDzzTdzxx13MH78eMCsHoqLi/H19aWoqIiuXbsC4OfnR15enuPY/Px8/Pz8\nzjrnqUlCRJqW1q3hmWcgPh6mToXXXzeTha+v1ZF5ljP/gJ4zZ06dzuPS202GYfCrX/2K0NBQ7rvv\nPsfP4+LiSE9PByA9Pd2RPOLi4li2bBkVFRXk5uayd+9eBg0a5MoQRaSBuvZa+Pe/ISjInAH197+r\nDbkruHR20+bNm7nuuuvo27ev47ZRSkoKgwYNIj4+nq+//pqAgACWL19O+/btAZg7dy6LFy/G29ub\ntLQ0Ro0adXrAmt0kImf49FNzc6PLLjO7y15+udUReR7tcS0iTdqJEzBvHqSlma097rrLXMktJiUJ\nEREgJ8esKpo3h5deMm9HiYeukxARcbfQUNi8GW68EYYMgSefhMpKq6NquFRJiEijdeCA2Yb86FFz\nnUWfPlZHZB1VEiIiZ7jySli/HqZPh+HDITlZbcgvlJKEiDRqNptZTWRnm7Og+veH7dutjqrhUJIQ\nkSbB3x8yMuAPf4Bf/AJ+/3tzNzw5PyUJEWkybDaYONFsQ15QYC7C27TJ6qg8mwauRaTJysiAGTNg\n3DhzjUXbtlZH5DoauBYRuUBxcWbDwBMnzIaBq1dbHZHnUSUhIoI5C2raNBg6FP7v/6BTJ6sjql+q\nJERELsLIkeZYRceO5nqKt96yOiLPoEpCROQMW7aYrT1CQ8025JddZnVEF0+VhIhIPbn6anNdRUiI\nOQPq5ZebbhtyVRIiIueRnQ1TpoDdbrYhv+IKqyOqG1USIiIuEBkJWVlw3XXmau3nnoPqaqujch9V\nEiIiTvryS3OsolkzWLQIgoOtjsh5HllJTJkyBbvdTp9TWi+WlpYSExNDcHAwsbGxlJWVOZ5LSUkh\nKCiIkJAQ1q5d68rQREQu2FVXwUcfwYQJ5rjFvHmNvw25S5PE5MmTWbNmzWk/S01NJSYmhj179jBi\nxAhSU1MByMnJ4Y033iAnJ4c1a9YwY8YMqptSTSciDUKzZvDb35pNAtetg8GDYedOq6NyHZcmiaFD\nh9KhQ4fTfpaRkUFiYiIAiYmJrFy5EoBVq1aRkJCAj48PAQEBBAYGkpWV5crwRETqrEcPM0ncfTeM\nGAGPPAI//mh1VPXP7QPXJSUl2O12AOx2OyUlJQAUFhbi7+/veJ2/vz8FBQXuDk9ExGk2mzlGsWOH\n+dWvH3z8sdVR1S9vK9/cZrNhs9nO+/y5JCcnOx5HR0cTHR1dz5GJiDivWzdYuRLeeAP+53/MTrOP\nPw4tW1oXU2ZmJpmZmRd9HrcnCbvdTnFxMb6+vhQVFdG1a1cA/Pz8yMvLc7wuPz8fPz+/c57j1CQh\nIuIJbDa47Tbz1tN995mtPV56CYYNsyaeM/+AnjNnTp3O4/bbTXFxcaSnpwOQnp7O+PHjHT9ftmwZ\nFRUV5ObmsnfvXgYNGuTu8ERELkqXLvDaazB/Ptx5J9x1F/z3v1ZHVXcuTRIJCQlcffXV7N69m+7d\nu7NkyRKSkpJYt24dwcHBbNiwgaSkJABCQ0OJj48nNDSUMWPGsHDhwvPeihIR8WTjxpltyA3DbEP+\n7rtWR1Q3WkwnIuJiGzaYbciHDDErjM6d3R+DRy6mExERGD7cXEvRpYs5VrF8ecNpGKhKQkTEjbZu\nNafN9upl9oHq1s0976tKQkSkARgyxOwsGxYGERGweLFnVxWqJERELPLvf5tVRadO8MILEBDguvdS\nJSEi0sBERJgrtIcPhwEDYMECz2tDrkpCRMQD7N5tVhVgtiHv1at+z69KQkSkAevVCz78EG69Fa65\nBlJS4MQJq6NSJSEi4nEOHoTp0+Hbb82B7YiIiz+nKgkRkUYiIADefx/uuQdiY+Hhh61rQ64kISLi\ngWw2mDzZbEG+a5e51/bWrRbEodtNIiKezTDgzTfNHfFuvRX+/Gdo1erCzqHbTSIijZTNBvHx8Pnn\ncPiw2drjgw/c9N6qJEREGpZ334Vf/xpGj4annoJ27Wo/RpWEiEgTMXas2Ya8WTOzvcfbb7vuvVRJ\niIg0YBs3mm3IBw2CtDSz0+y5NJpKYs2aNYSEhBAUFMS8efOsDkdExKMNG2a2Ib/sMnOsYtmy+m0Y\n6FFJoqqqipkzZ7JmzRpycnJYunQpX375pdVheaz62OS8sdC1+Jmuxc+ayrVo2RL+8hdYtQoeewzG\nj4eCgvo5t0cliaysLAIDAwkICMDHx4fbbruNVatWWR2Wx2oq/wCcoWvxM12LnzW1axEVBZ99BuHh\n5irtl166+KrCo5JEQUEB3bt3d3zv7+9PQX2lQxGRJqBFC/jTn8wpss8/DzExkJtb9/N5VJKw2WxW\nhyAi0ij07QvbtpltPQYOvIgTGR5k69atxqhRoxzfz50710hNTT3tNT179jQAfelLX/rS1wV89ezZ\ns06fyx41BbayspJevXrxwQcf0K1bNwYNGsTSpUu56qqrrA5NRKRJ8rY6gFN5e3vz7LPPMmrUKKqq\nqvjVr36lBCEiYiGPqiRERMSzeNTA9amcWVR37733EhQURHh4ONnZ2W6O0H1quxavvfYa4eHh9O3b\nl2uuuYadO3daEKV7OLvYcvv27Xh7e/OPf/zDjdG5lzPXIjMzk8jISMLCwoiOjnZvgG5U27X49ttv\nGT16NBEREYSFhfHyyy+7P0g3mDJlCna7nT59+tT4mgv+3KzzKLMLVVZWGj179jRyc3ONiooKIzw8\n3MjJyTntNe+++64xZswYwzAMY9u2bUZUVJQVobqcM9diy5YtRllZmWEYhrF69eomfS1+et2wYcOM\nsWPHGm+99ZYFkbqeM9fiyJEjRmhoqJGXl2cYhmF88803VoTqcs5ci0cffdRISkoyDMO8Dh07djRO\nnDhhRbgu9eGHHxqfffaZERYWds7n6/K56ZGVhDOL6jIyMkhMTAQgKiqKsrIySkpKrAjXpZy5FkOG\nDKHdyTaQUVFR5OfnWxGqyzm72HLBggVMmDCBLjU1sWkEnLkWr7/+OjfffDP+/v4AdO7c2YpQXc6Z\na3HZZZdx9OhRAI4ePUqnTp3w9vaoIdl6MXToUDp06FDj83X53PTIJOHMorpzvaYxfjhe6ALDRYsW\nccMNN7gjNLdz9v+LVatWcffddwONd+2NM9di7969lJaWMmzYMAYMGMArr7zi7jDdwplrMW3aNHbt\n2kW3bt0IDw8nLS3N3WF6hLp8bnpkKnX2H7Zxxph7Y/xAuJDfaePGjSxevJh//etfLozIOs5ci/vu\nu4/U1FRHx8sz/x9pLJy5FidOnOCzzz7jgw8+oLy8nCFDhjB48GCCgoLcEKH7OHMt5s6dS0REBJmZ\nmezfv5+YmBh27NhBmzZt3BChZ7nQz02PTBJ+fn7k5eU5vs/Ly3OUzDW9Jj8/Hz8/P7fF6C7OXAuA\nnTt3Mm3aNNasWXPecrMhc+ZafPrpp9x2222AOVi5evVqfHx8iIuLc2usrubMtejevTudO3fm0ksv\n5dJLL+W6665jx44djS5JOHMttmzZwkMPPQRAz5496dGjB7t372bAgAFujdVqdfrcrLcRk3p04sQJ\n48orrzRyc3ONH3/8sdaB661btzbawVpnrsWhQ4eMnj17Glu3brUoSvdw5lqcatKkScaKFSvcGKH7\nOHMtvvzyS2PEiBFGZWWl8f333xthYWHGrl27LIrYdZy5Fr/73e+M5ORkwzAMo7i42PDz8zMOHz5s\nRbgul5ub69TAtbOfmx5ZSdS0qO5vf/sbAHfddRc33HAD7733HoGBgbRq1YolS5ZYHLVrOHMt/vSn\nP3HkyBHHfXgfHx+ysrKsDNslnLkWTYUz1yIkJITRo0fTt29fvLy8mDZtGqGhoRZHXv+cuRazZ89m\n8uTJhIeHU11dzRNPPEHHjh0tjrz+JSQksGnTJr799lu6d+/OnDlzOHHiBFD3z00tphMRkRp55Owm\nERHxDEoSIiJSIyUJERGpkZKEiIjUSElCRERqpCQhIiI1UpKQRqN169YuPf/8+fP54Ycf6v393n77\n7fO2PRexktZJSKPRpk0bvvvuO5edv0ePHnzyySd06tTJLe8n4glUSUijtn//fsaMGcOAAQO47rrr\n2L17NwCTJk3it7/9Lddccw09e/ZkxYoVAFRXVzNjxgyuuuoqYmNjGTt2LCtWrGDBggUUFhYybNgw\nRowY4Tj/ww8/TEREBEOGDOE///nPWe9/33338dhjjwHw/vvvc/3115/1mpdffpl77rnnvHGd6uDB\ng4SEhDB58mR69erF7bffztq1a7nmmmsIDg5m+/btF3/hRH5Sbw1DRCzWunXrs342fPhwY+/evYZh\nmJusDB8+3DAMw0hMTDTi4+MNwzCMnJwcIzAw0DAMw3jzzTeNG264wTAMs8dPhw4dHP2fAgICTuv3\nY7PZjHfeeccwDMOYNWuW8fjjj5/1/uXl5Ubv3r2NDRs2GL169TIOHDhw1mtefvllY+bMmeeN61S5\nubmGt7e38cUXXxjV1dVG//79jSlTphiGYRirVq0yxo8fX+u1EnGWR/ZuEqkPx44dY+vWrdxyyy2O\nn1VUVABme+Tx48cDcNVVVzk2Xtm8eTPx8fEA2O12hg0bVuP5mzdvztixYwHo378/69atO+s1l156\nKS+++CJDhw4lLS2NHj16nDfmmuI6U48ePejduzcAvXv3ZuTIkQCEhYVx8ODB876HyIVQkpBGq7q6\nmvbt29e4j2/z5s0dj42TQ3M/7UNx5s/PxcfHx/HYy8uLysrKc75u586ddOnS5bybRdUW15latGhx\n2nv/dMz54hCpC41JSKPVtm1bevTowVtvvQWYH7g7d+487zHXXHMNK1aswDAMSkpK2LRpk+O5Nm3a\nOLbAdNahQ4d4+umnyc7OZvXq1efsznu+RCRiNSUJaTTKy8vp3r2742v+/Pm89tprLFq0iIiICMLC\nwsjIyHC8/tQduX56/NOe0KGhodxxxx3069fPsX/49OnTGT16tGPg+szjz9zhyzAMpk6dyl/+8hd8\nfX1ZtGgRU6dOddzyqunYmh6feUxN3zfGHRrFOpoCK3KG77//nlatWnH48GGioqLYsmULXbt2tTos\nEUtoTELkDL/4xS8oKyujoqKCRx55RAlCmjRVEiIiUiONSYiISI2UJEREpEZKEiIiUiMlCRERqZGS\nhIiI1EhJQkREavT/G7kEgAv8kjUAAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 35 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem no 14.6,Page No.332" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "%matplotlib inline\n", "\n", "#Initilization of variables\n", "H= 4 #m #height of dam\n", "a=1 #m #Top width\n", "b=3 #m #bottom width\n", "rho1=9810 #N/m**3 #weight of water\n", "rho2=19620 #n/m**3 #Weight of mason\n", "\n", "#Calculations\n", "\n", "#Let L=1 m (length of dam)\n", "L=1\n", "\n", "#weight of dam\n", "W=(a+b)*2**-1*L*H*rho2*10**-3\n", "\n", "##Lateral thrust\n", "P=rho1*H**2*L*2**-1*10**-3 \n", "\n", "#Distance of Line of action from vertical base\n", "x_bar=(b**2+b*a+a**2)*(3*(b+a))**-1\n", "\n", "#distance of pt where resultant cuts the base\n", "x=P*W**-1*H*3**-1\n", "\n", "#Eccentricity\n", "e=x_bar+x-b*2**-1\n", "\n", "#Stress at Pt B\n", "sigma1=W*10**3*b**-1*(1-6*e*b**-1)\n", "\n", "#stress at Pt C\n", "sigma2=W*10**3*b**-1*(1+6*e*b**-1)\n", "\n", "#Stresses at the base when resorvoir is empty\n", "\n", "e2=x_bar-b*2**-1\n", "\n", "#Minus sign indicates sigma_b>sigma_c\n", "\n", "#Stress at C\n", "sigma2_2=W*10**3*b**-1*(1+6*round(e2,2)*b**-1)\n", "\n", "#Stress at Pt B\n", "sigma1_2=W*10**3*b**-1*(1-6*round(e2,2)*b**-1)\n", "\n", "#result\n", "print\"When the Reservoir is full :sigma1\",round(sigma1,2),\"KN/m**2\"\n", "print\" :sigma2\",round(sigma2,2),\"KN/m**2\"\n", "print\"When the Reservoir is empty:sigma1_2\",round(sigma1_2,2),\"KN/m**2\"\n", "print\" :sigma2_2\",round(sigma2_2,2),\"KN/m**2\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "When the Reservoir is full :sigma1 26160.0 KN/m**2\n", " :sigma2 78480.0 KN/m**2\n", "When the Reservoir is empty:sigma1_2 96268.8 KN/m**2\n", " :sigma2_2 8371.2 KN/m**2\n" ] } ], "prompt_number": 21 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem no 14.7,Page No.333" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "%matplotlib inline\n", "\n", "#Initilization of variables\n", "H=8 #m #height of dam\n", "h=7.5 #m #Height of water\n", "a=1 #m #top width\n", "mu=0.6 #Coeffeicient of friction\n", "rho_mason=22.4 #KN/m**3 #weight of mason\n", "rho_w=9.81 #KN/m**3 #density of water\n", "\n", "#Calculations\n", "\n", "#weight of dam\n", "#W=(a+b)*2**-1*L*H*rho2*10**-3\n", "#After substituting values and further simplifying we get\n", "#W=89600*(b+1)\n", "\n", "#Distance of Line of action from vertical base\n", "#x_bar=(b**2+b*a+a**2)*(3*(b+a))**-1\n", "#After substituting values and further simplifying we get\n", "#x_bar=(1+b+b**2)*(3*(1+b))**-1\n", "\n", "#Lateral thrust\n", "P=rho_w*h**2*2**-1\n", "\n", "#Min width to avoid tension at base\n", "#Z=x_bar+P*W**-1*h*3**-1\n", "#Z=2*3**-1*b\n", "#After substituting values and further simplifying we get\n", "#b**2+b-24.09=0\n", "a=1\n", "b=1\n", "c=-24.09\n", "\n", "X=b**2-4*a*c\n", "\n", "b1=(-b+X**0.5)*(2*a)**-1\n", "b2=(-b-X**0.5)*(2*a)**-1\n", "\n", "#Thus width cannot be negative,b1 is considered\n", "\n", "#Min width to avoid sliding\n", "#mu*W>P\n", "#After substituting values and further simplifying we get\n", "b=P*10**3*(mu*89600)**-1-1\n", "\n", "#Result\n", "print\"The Min bottom width is\",round(b,2),\"m\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The Min bottom width is 4.13 m\n" ] } ], "prompt_number": 30 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem no 14.8,Page No.334" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "%matplotlib inline\n", "\n", "#Initilization of variables\n", "H=10 #m #height of dam\n", "a=1 #m #top width\n", "b=7 #m #Bottom width\n", "rho_mason=19620 #N/m**3 #weight of mason\n", "rho_w=9810 #N/m**3 #density of water\n", "\n", "#Calculations\n", "\n", "#Lateral thrust\n", "P=rho_w*H**2*2**-1\n", "\n", "#weight of dam\n", "W=(rho_w*H*2**-1*a)+(rho_mason*(a+b)*2**-1*H)\n", "\n", "#Taking Moment at B,M_B=0\n", "x_bar=((rho_w*H*2**-1*1*3**-1)+(rho_mason*H*2**-1*2*3**-1)+(rho_mason*H*1.5)+(rho_mason*H*5*11*2**-1*3**-1))*W**-1\n", "\n", "#Now using relation we get\n", "x=P*W**-1*H*3**-1\n", "\n", "#Eccentricity\n", "e=x_bar+x-b*2**-1\n", "\n", "#Max stress\n", "sigma_max=W*b**-1*(1+6*round(e,2)*b**-1)\n", "\n", "#Min stress\n", "sigma_min=W*b**-1*(1-6*round(e,2)*b**-1)\n", "\n", "#Result\n", "print\"The Max stresses on the base is\",round(sigma_max,2),\"N/m**2\"\n", "print\"The Min stresses on the base is\",round(sigma_min,2),\"N/m**2\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The Max stresses on the base is 228372.8 N/m**2\n", "The Min stresses on the base is 9870.06 N/m**2\n" ] } ], "prompt_number": 47 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem no 14.10,Page No.337" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "%matplotlib inline\n", "\n", "#Initilization of variables\n", "H=6 #m #height of dam\n", "a=1 #m #top width\n", "b=3 #m #Bottom width\n", "rho_mason=22 #KN/m**3 #weight of mason\n", "rho_earth=16 #KN/m**3 #density of water\n", "phi=30 #Degree #angle of repose\n", "mu=0.5 #Coeffecient of friction\n", "\n", "#Calculations\n", "\n", "#Let Length of dam ,L=1 m\n", "L=1 #m\n", "\n", "#weight of dam\n", "W=(a+b)*2**-1*L*H*rho_mason\n", "\n", "#Lateral thrust\n", "P=rho_earth*H**2*L*2**-1*((1-sin(30*pi*180**-1))*(1+sin(phi*pi*180**-1))**-1)\n", "\n", "#Distance of Line of action from vertical base\n", "x_bar=(b**2+b*a+a**2)*(3*(b+a))**-1\n", "\n", "#distance of pt where resultant cuts the base\n", "x=P*W**-1*H*3**-1\n", "\n", "#Eccentricity\n", "e=x_bar+x-b*2**-1\n", "e_max=b*6**-1\n", "\n", "#stress at toe\n", "sigma2=W*10**3*b**-1*(1+6*round(e,2)*b**-1)*10**-3\n", "\n", "#Safe agaainst bearing\n", "\n", "#Frictional Resistance \n", "F=mu*W\n", "\n", "#Hence F>P,so it is safe against sliding\n", "\n", "#Result\n", "print\"Safe against bearing as well as sliding\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Safe against bearing as well as sliding\n" ] } ], "prompt_number": 69 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem no 14.11,Page No.338" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "%matplotlib inline\n", "\n", "#Initilization of variables\n", "H=8 #m #height of dam\n", "a=1 #m #top width\n", "b=4.5 #m #Bottom width\n", "rho_mason=24 #KN/m**3 #weight of mason\n", "rho_earth=20 #KN/m**3 #density of water\n", "phi=30 #Degree #angle of repose\n", "mu=0.5 #Coeffecient of friction\n", "BC=120 #KN/m**2\n", "\n", "\n", "#Calculations\n", "\n", "#Let Length of dam ,L=1 m\n", "L=1 #m\n", "\n", "#weight of dam\n", "W=(a+b)*2**-1*L*H*rho_mason\n", "\n", "#Rankine's coeff earth pressure\n", "K=((1-sin(30*pi*180**-1))*(1+sin(phi*pi*180**-1))**-1)\n", "\n", "#Lateral thrust\n", "P=rho_earth*H**2*L*2**-1*round(K,2)\n", "\n", "#Distance of Line of action from vertical base\n", "x_bar=(b**2+b*a+a**2)*(3*(b+a))**-1\n", "\n", "#distance of pt where resultant cuts the base\n", "x=P*W**-1*H*3**-1\n", "\n", "#Eccentricity\n", "e=x_bar+x-b*2**-1\n", "\n", "#Pressure at heel B\n", "sigma1=W*b**-1*(1-6*round(e,2)*b**-1)\n", "\n", "#Pressure at heel C\n", "sigma2=W*b**-1*(1+6*round(e,2)*b**-1)\n", "\n", "#sigma2>120 #KN/m**2,so it is unsafe against bearing capacity of the soil\n", "\n", "#result\n", "print\"Unsafe against the bearing capacity of soil\" \n", "\n", "#Plotting the Shear Force Diagram\n", "\n", "X1=[0,L,L]\n", "Y1=[sigma2,sigma1,0]\n", "Z1=[0,0,0]\n", "plt.plot(X1,Y1,X1,Z1)\n", "plt.xlabel(\"Length x in m\")\n", "plt.ylabel(\"Shear Force in kN\")\n", "plt.show()\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Unsafe against the bearing capacity of soil\n" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEPCAYAAABY9lNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJSUIERFxSQlCRERc+v9brQ7wu4yTKgAAAABJRU5ErkJggg==\n", "text": [ "" ] } ], "prompt_number": 86 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem no 14.12,Page No.340" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "%matplotlib inline\n", "\n", "#Initilization of variables\n", "H=6 #m #height of dam\n", "a=1.5 #m #top width\n", "b=3.5 #m #Bottom width\n", "rho_s=16 #KN/m**3 #density of soil\n", "rho_mason=22.5 #KN/m**3 #density of mason\n", "phi=30 #Degree #angle of repose\n", "\n", "#Calculations\n", "\n", "#Let Length of dam ,L=1 m\n", "L=1 #m\n", "\n", "#weight of dam\n", "W=(a+b)*2**-1*L*H*rho_mason\n", "\n", "#Rankine's coeff earth pressure\n", "K=((1-sin(30*pi*180**-1))*(1+sin(phi*pi*180**-1))**-1)\n", "\n", "#Lateral thrust\n", "P=rho_s*H**2*L*2**-1*K\n", "\n", "#Distance of Line of action from vertical base\n", "x_bar=(b**2+b*a+a**2)*(3*(b+a))**-1\n", "\n", "#distance of pt where resultant cuts the base\n", "x=P*W**-1*H*3**-1\n", "\n", "#Eccentricity\n", "e=x_bar+x-b*2**-1\n", "\n", "#Pressure at heel B\n", "sigma1=W*b**-1*(1-6*round(e,3)*b**-1)\n", "\n", "#Pressure at heel C\n", "sigma2=W*b**-1*(1+6*round(e,3)*b**-1)\n", "\n", "#Result\n", "print\"The Max Intensities of soil at the wall is\",round(sigma2,2),\"KN/m**2\"\n", "print\"The Min Intensities of soil at the wall is\",round(sigma1,2),\"KN/m**2\"\n", "\n", "#Plotting the Shear Force Diagram\n", "\n", "X1=[0,L,L]\n", "Y1=[sigma2,sigma1,0]\n", "Z1=[0,0,0]\n", "plt.plot(X1,Y1,X1,Z1)\n", "plt.xlabel(\"Length x in m\")\n", "plt.ylabel(\"Shear Force in kN\")\n", "plt.show()\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The Max Intensities of soil at the wall is 118.91 KN/m**2\n", "The Min Intensities of soil at the wall is 73.95 KN/m**2\n" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEPCAYAAABY9lNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "" ] } ], "prompt_number": 113 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Problem no 14.13,Page No.341" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "%matplotlib inline\n", "\n", "#Initilization of variables\n", "H=6 #m #height of dam\n", "a=1 #m #top width\n", "b=3 #m #Bottom width\n", "rho_s=18 #KN/m**3 #density of soil\n", "rho_mason=24 #KN/m**3 #density of mason\n", "alpha=20\n", "phi=30\n", "\n", "#Calculations\n", "\n", "#Let Length of dam ,L=1 m\n", "L=1 #m\n", "\n", "a2=cos(alpha*pi*180**-1)\n", "b2=(cos(alpha*pi*180**-1)-((cos(alpha*pi*180**-1)**2-cos(phi*pi*180**-1)**2))**0.5)\n", "c2=(cos(alpha*pi*180**-1)+((cos(alpha*pi*180**-1)**2-cos(phi*pi*180**-1)**2)**0.5))\n", "\n", "X=a2*b2*c2**-1\n", "\n", "#Total Pressue on the wall\n", "P=rho_s*H**2*2**-1*X\n", "\n", "#The Horizontal component of pressure\n", "P_H=P*cos(20*pi*180**-1)\n", "\n", "#The Vertical component of pressure\n", "P_V=P*sin(20*pi*180**-1)\n", "\n", "#weight of wall\n", "W=(a+b)*H*rho_mason*2**-1\n", "\n", "#TotaL Weight \n", "W1=W+P_V\n", "\n", "#Taking moment of vertical Loads about B,M_B=0\n", "x_bar=(rho_mason*a*H*0.5+rho_mason*H*2)*W1**-1\n", "\n", "x=P_H*W1**-1*H*3**-1\n", "\n", "#eccentricity\n", "e=x_bar+x-b*2**-1\n", "\n", "#Stress at the toe at C\n", "sigma_max=W1*b**-1*(1+6*round(e,2)*b**-1)\n", "\n", "#Stress at the heel at B\n", "sigma_min=W1*b**-1*(1-6*round(e,2)*b**-1)\n", "\n", "#Result\n", "print\"Pressure at the base of the wall:Pressure at the heel\",round(sigma_min,2),\"KN/m**2\"\n", "print\" :Pressure at the toe\",round(sigma_max,2),\"KN/m**2\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Pressure at the base of the wall:Pressure at the heel 37.84 KN/m**2\n", " :Pressure at the toe 184.76 KN/m**2\n" ] } ], "prompt_number": 155 } ], "metadata": {} } ] }