{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import plotly.graph_objects as go\n", "import plotly.express as px\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn import datasets\n", "import sympy as sym\n", "from sympy import symbols\n", "from scipy.optimize import fsolve" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "#b, psi, xi, d, fcd, beta, As, fyd, d2, MEd, mylambda, x, A1s, Es, epsilon_su = symbols('b psi xi d fcd beta As fyd d2 MEd mylambda x A1s Es, epsilon_su')\n" ] }, { "cell_type": "code", "execution_count": 245, "metadata": {}, "outputs": [], "source": [ "def design():\n", " b = float(input('b = [mm]'))\n", " fck = float(input('fck = [MPa]'))\n", " fcd = round(fck*.85/1.5, 2)\n", " fyk = float(input('fyk = [MPa]'))\n", " fyd = round(fyk/1.15, 2)\n", " d2 = float(input('d2 = [mm]'))\n", " Es = float(input('Es = [GPa]'))*10**3\n", " epsilon_se = fyd/Es\n", " epsilon_su = float(input('epsilon_su = [per thousand]'))/1000\n", " epsilon_c2 = float(input('epsilon_c2 = [per thousand]'))/1000\n", " epsilon_cu = float(input('epsilon_cu = [per thousand]'))/1000\n", " beta = float(input('beta = A1s/As = '))\n", " MEd = abs(float(input('MEd = [kNm]'))*10**6)\n", " d = input(f'd = [mm] (empty if unknown)') \n", " \n", " xi_23 = .2593;\n", " \n", " psi = 0.80952;\n", " mylambda = 0.416;\n", " xi = xi_23;\n", " \n", " if d == '':\n", " d = symbols('d')\n", " \n", " dd = sym.solve(b*psi*xi*d**2 *fcd*(1-mylambda*xi) + beta* ((b*psi*xi*d*fcd)/(1-beta)) *(d-d2) - MEd, d)\n", " d = list(filter(lambda val: val >= 0, dd))[0]\n", " print('\\n\\nd = ', d, 'mm\\n')\n", " As_0 = b*psi*xi*d*fcd/((1-beta)*fyd);\n", " A1s_0 = beta*As_0;\n", " print('As = ',As_0,'mm^2\\nA1s = ', A1s_0,'mm^2')\n", " \n", "#------------------As----------------------------------\n", "\n", " def steel_area(): \n", " print('\\nInsert integer number of tensed steel reinforcement (>=2): ')\n", " num_As = int(input('n.:'))\n", "\n", " \n", " if (num_As < 2):\n", " i=0;\n", " while i<3:\n", " print('\\nMinumum bars number must be greater or at least equal 2: ')\n", " num_As = int(input('n.:'))\n", " i+=1;\n", " if i==3:\n", " num_As = 2;\n", " print('\\nNumber of tensed bars has been set to ', num_As)\n", "\n", "\n", " print('\\nInsert tensed bars diameter (even number >= 12mm): ')\n", " phi_As = int(input('Phi = '))\n", "\n", "\n", " \n", " if (phi_As % 2) != 0 or phi_As < 12:\n", " i=0;\n", " while i<3:\n", " print('\\nDiameter must be even and greater than 12mm): ')\n", " phi_As = int(input('Phi = '))\n", " i+=1;\n", " if i==3:\n", " phi_As = 12;\n", " print('\\nDiameter of tensed bars has been set to ', phi_As, 'mm')\n", " \n", " As = round(num_As * np.pi * phi_As**2 /4,4)\n", " print('\\nAs = ',num_As,'ϕ',phi_As,'mm = ',As,'mm^2\\n')\n", " \n", "\n", "#--------------------------------------------------------------------------------\n", "\n", "#------------------A1s----------------------------------\n", " def steel1_area():\n", " print('Insert integer number of compressed steel reinforcement (>=2): ')\n", " num_A1s = int(input('n.:'))\n", "\n", "\n", " if (num_A1s < 2):\n", " i=0;\n", " while i<3:\n", " print('\\nMinumum bars number must be greater or at least equal 2: ')\n", " num_A1s = int(input('n.: '))\n", " i+=1;\n", "\n", " if i==3:\n", " num_A1s = 2;\n", " print('\\nNumber of compressed bars has been set to ', num_A1s)\n", "\n", "\n", " print('\\nInsert compressed bars diameter (even number >= 12mm): ')\n", " phi_A1s = int(input('Phi = '))\n", "\n", "\n", " if (phi_A1s % 2) != 0 or phi_A1s < 12:\n", " i=0;\n", " while i<3: \n", " print('\\nDiameter must be even and greater than 12mm): ')\n", " phi_A1s = int(input('Phi = '))\n", " i+=1;\n", " if i==3:\n", " phi_A1s = 12;\n", " print('\\nDiameter of compressed bars has been set to ', phi_A1s, 'mm')\n", " \n", " A1s = round(num_A1s * np.pi * phi_A1s**2 /4,4)\n", " print('\\nA1s = ',num_A1s,'ϕ',phi_A1s,'mm = ',A1s,'mm^2\\n')\n", " \n", "#----------------------------------------------------------------------------\n", "\n", " steel_area()\n", " \n", " steel1_area()\n", " \n", " \n", " \n", " if As < As_0:\n", " print(As,'<',As_0,'\\nMore bars needed')\n", " steel_area()\n", " \n", " if A1s < A1s_0:\n", " print(A1s,'<',A1s_0,'\\nMore bars needed')\n", " steel1_area()\n", "\n", " \n", " \n", " " ] }, { "cell_type": "code", "execution_count": 246, "metadata": {}, "outputs": [ { "name": "stdin", "output_type": "stream", "text": [ "b = [mm] 300\n", "fck = [MPa] 25\n", "fyk = [MPa] 450\n", "d2 = [mm] 40\n", "Es = [GPa] 210\n", "epsilon_su = [per thousand] 10\n", "epsilon_c2 = [per thousand] 2\n", "epsilon_cu = [per thousand] 3.5\n", "beta = A1s/As = .3\n", "MEd = [kNm] 234.169\n", "d = [mm] (empty if unknown) \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "d = 452.297790574483 mm\n", "\n", "As = 1473.45807437857 mm^2\n", "A1s = 442.037422313570 mm^2\n", "\n", "Insert integer number of tensed steel reinforcement (>=2): \n" ] }, { "name": "stdin", "output_type": "stream", "text": [ "n.: 4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Insert tensed bars diameter (even number >= 12mm): \n" ] }, { "name": "stdin", "output_type": "stream", "text": [ "Phi = 12\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "As = 4 ϕ 12 mm = 452.3893 mm^2\n", "\n", "Insert integer number of compressed steel reinforcement (>=2): \n" ] }, { "name": "stdin", "output_type": "stream", "text": [ "n.: 4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Insert compressed bars diameter (even number >= 12mm): \n" ] }, { "name": "stdin", "output_type": "stream", "text": [ "Phi = 12\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "A1s = 4 ϕ 12 mm = 452.3893 mm^2\n", "\n", "226.1947 < 1473.45807437857 \n", "More bars needed\n", "\n", "Insert integer number of tensed steel reinforcement (>=2): \n" ] }, { "name": "stdin", "output_type": "stream", "text": [ "n.: 6\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Insert tensed bars diameter (even number >= 12mm): \n" ] }, { "name": "stdin", "output_type": "stream", "text": [ "Phi = 18\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "As = 6 ϕ 18 mm = 1526.814 mm^2\n", "\n", "226.1947 < 442.037422313570 \n", "More bars needed\n", "Insert integer number of compressed steel reinforcement (>=2): \n" ] }, { "name": "stdin", "output_type": "stream", "text": [ "n.: 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Insert compressed bars diameter (even number >= 12mm): \n" ] }, { "name": "stdin", "output_type": "stream", "text": [ "Phi = 18\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "A1s = 2 ϕ 18 mm = 508.938 mm^2\n", "\n" ] } ], "source": [ "design()" ] }, { "cell_type": "code", "execution_count": 240, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "226.1947" ] }, "execution_count": 240, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A1s = round(num_A1s * np.pi * phi_A1s**2 /4,4)\n", "A1s" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "from mpl_toolkits.mplot3d import Axes3D\n", "from matplotlib import cm\n", "from matplotlib.ticker import LinearLocator, FormatStrFormatter" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = np.arange(1, 2.5, .5)\n", "Y = 1/np.tan([np.pi/4, np.pi/3, np.pi/2])\n", "\n", "func = (X+ Y) * 1/(1+X**2)\n", "\n", "len(func)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = fig.gca(projection='3d')\n", "\n", "ax.plot_trisurf(X,Y, func)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "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": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 4 }