speccon example code: speccon1d_vrc_stone_column_luetal2010_linear_depth_dependent_load.pyΒΆ
# speccon1d_vrc example (if viewing this in docs, plots are at bottom of page)
# Stone column with vertical and radial drainage in both the column and soil
# with a linear depth dependent load.
# Compare with Lu et al. (2010)
# The orignal solution of Lu et al. (2010)
# is implemented separately in
# geotecha.consolidation.luetal2010.
# Lu, Meng-Meng, Kang-He Xie, and Biao Guo. 2010. 'Consolidation
# Theory for a Composite Foundation Considering Radial and Vertical
# Flows within the Column and the Variation of Soil Permeability
# within the Disturbed Soil Zone'. Canadian Geotechnical
# Journal 47 (2): 207-17. doi:10.1139/T09-086.
# This file should be run with python. It will not work if run with the
# speccon1d_vr.exe script program.
from __future__ import division, print_function
import numpy as np
from geotecha.speccon.speccon1d_vrc import Speccon1dVRC
import matplotlib.pyplot as plt
#Expected values
#t = time values
#avp = average excess pore pressure
#z = depth values
#por = excess pore pressure at time t and depth z.
#settle = settlement
z = np.array(
[ 0. , 0.52631579, 1.05263158, 1.57894737,
2.10526316, 2.63157895, 3.15789474, 3.68421053,
4.21052632, 4.73684211, 5.26315789, 5.78947368,
6.31578947, 6.84210526, 7.36842105, 7.89473684,
8.42105263, 8.94736842, 9.47368421, 10. ])
t = np.array(
[ 0.01 , 0.02682696, 0.07196857, 0.19306977,
0.51794747, 1.38949549, 3.72759372, 10. ])
por = np.array(
[[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00],
[ 7.89228647e-01, 5.95605081e-01, 3.45180214e-01,
1.45086414e-01, 3.87034741e-02, 6.22454961e-03,
1.77078924e-04, 1.43687608e-08],
[ 7.96538802e-01, 7.36765027e-01, 5.43243863e-01,
2.64106848e-01, 7.53853307e-02, 1.23885715e-02,
3.52947563e-04, 2.86393684e-08],
[ 7.63335940e-01, 7.21032673e-01, 6.02081861e-01,
3.44060865e-01, 1.08397586e-01, 1.84334052e-02,
5.26403940e-04, 4.27143400e-08],
[ 7.29713572e-01, 6.92695455e-01, 6.00390659e-01,
3.87619399e-01, 1.36715907e-01, 2.43039516e-02,
6.96262634e-04, 5.64975294e-08],
[ 6.95775756e-01, 6.63349493e-01, 5.83533464e-01,
4.05920023e-01, 1.59999892e-01, 2.99500475e-02,
8.61362911e-04, 6.98947834e-08],
[ 6.61562468e-01, 6.33306767e-01, 5.63371982e-01,
4.10051442e-01, 1.78478629e-01, 3.53274233e-02,
1.02057668e-03, 8.28145851e-08],
[ 6.27109888e-01, 6.02658407e-01, 5.41694866e-01,
4.07122205e-01, 1.92737402e-01, 4.03982068e-02,
1.17281621e-03, 9.51686793e-08],
[ 5.92450906e-01, 5.71486570e-01, 5.18804995e-01,
4.00550761e-01, 2.03496720e-01, 4.51309920e-02,
1.31704159e-03, 1.06872675e-07],
[ 5.57615569e-01, 5.39866473e-01, 4.94874905e-01,
3.91708348e-01, 2.11448996e-01, 4.95005419e-02,
1.45226780e-03, 1.17846622e-07],
[ 5.22631498e-01, 5.07867389e-01, 4.70059586e-01,
3.81151043e-01, 2.17175591e-01, 5.34872189e-02,
1.57757144e-03, 1.28015557e-07],
[ 4.87524266e-01, 4.75553569e-01, 4.44503337e-01,
3.69184261e-01, 2.21132065e-01, 5.70762492e-02,
1.69209703e-03, 1.37310015e-07],
[ 4.52317753e-01, 4.42985091e-01, 4.18341611e-01,
3.56052829e-01, 2.23673736e-01, 6.02569148e-02,
1.79506281e-03, 1.45666508e-07],
[ 4.17034470e-01, 4.10218657e-01, 3.91702685e-01,
3.42007224e-01, 2.25094585e-01, 6.30217519e-02,
1.88576606e-03, 1.53027950e-07],
[ 3.81695874e-01, 3.77308340e-01, 3.64710698e-01,
3.27369324e-01, 2.25661289e-01, 6.53658065e-02,
1.96358784e-03, 1.59344056e-07],
[ 3.46322658e-01, 3.44306315e-01, 3.37509937e-01,
3.12652496e-01, 2.25633199e-01, 6.72859799e-02,
2.02799719e-03, 1.64571681e-07],
[ 3.10935041e-01, 3.11265280e-01, 3.10458620e-01,
2.98729021e-01, 2.25265480e-01, 6.87804759e-02,
2.07855473e-03, 1.68675114e-07],
[ 2.75553321e-01, 2.78364929e-01, 2.84983294e-01,
2.86931417e-01, 2.24796770e-01, 6.98483537e-02,
2.11491559e-03, 1.71626325e-07],
[ 2.40506692e-01, 2.48198353e-01, 2.65188774e-01,
2.78890365e-01, 2.24426139e-01, 7.04891842e-02,
2.13683177e-03, 1.73405155e-07],
[ 2.19037471e-01, 2.33939883e-01, 2.57448777e-01,
2.76020588e-01, 2.24286918e-01, 7.07028069e-02,
2.14415381e-03, 1.73999451e-07]])
pors = np.array(
[[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00],
[ 7.98265249e-01, 6.02280763e-01, 3.48846036e-01,
1.46450028e-01, 3.89711966e-02, 6.25182115e-03,
1.77803047e-04, 1.44275125e-08],
[ 8.04958469e-01, 7.44525146e-01, 5.48786745e-01,
2.66527204e-01, 7.58992543e-02, 1.24427410e-02,
3.54390855e-04, 2.87564705e-08],
[ 7.70750724e-01, 7.28024224e-01, 6.07838241e-01,
3.47083341e-01, 1.09119039e-01, 1.85137439e-02,
5.28556530e-04, 4.28889926e-08],
[ 7.36222521e-01, 6.98859182e-01, 6.05684427e-01,
3.90831257e-01, 1.37596322e-01, 2.44094094e-02,
6.99109798e-04, 5.67285394e-08],
[ 7.01466480e-01, 6.68759478e-01, 5.88249405e-01,
4.09050507e-01, 1.60988683e-01, 3.00793003e-02,
8.64885178e-04, 7.01805728e-08],
[ 6.66511565e-01, 6.38030392e-01, 5.67535104e-01,
4.12967356e-01, 1.79529598e-01, 3.54789280e-02,
1.02474996e-03, 8.31532017e-08],
[ 6.31383980e-01, 6.06754538e-01, 5.45344771e-01,
4.09776249e-01, 1.93812547e-01, 4.05702594e-02,
1.17761198e-03, 9.55578099e-08],
[ 5.96107530e-01, 5.75006275e-01, 5.21977853e-01,
4.02934712e-01, 2.04567392e-01, 4.53217848e-02,
1.32242705e-03, 1.07309661e-07],
[ 5.60703952e-01, 5.42853653e-01, 4.97602332e-01,
3.93827331e-01, 2.12495144e-01, 4.97082150e-02,
1.45820614e-03, 1.18328479e-07],
[ 5.25193221e-01, 5.10359321e-01, 4.72368874e-01,
3.83013415e-01, 2.18184090e-01, 5.37099045e-02,
1.58402207e-03, 1.28538993e-07],
[ 4.89593822e-01, 4.77581368e-01, 4.46417701e-01,
3.70798586e-01, 2.22094959e-01, 5.73121056e-02,
1.69901588e-03, 1.37871456e-07],
[ 4.53923010e-01, 4.44574094e-01, 4.19880404e-01,
3.57427248e-01, 2.24586872e-01, 6.05041516e-02,
1.80240261e-03, 1.46262116e-07],
[ 4.18197047e-01, 4.11388738e-01, 3.92881583e-01,
3.43149634e-01, 2.25956789e-01, 6.32786454e-02,
1.89347666e-03, 1.53653659e-07],
[ 3.82431431e-01, 3.78074159e-01, 3.65541867e-01,
3.28288385e-01, 2.26474003e-01, 6.56307069e-02,
1.97161656e-03, 1.59995590e-07],
[ 3.46641110e-01, 3.44677496e-01, 3.38002457e-01,
3.13360148e-01, 2.26400416e-01, 6.75573112e-02,
2.03628921e-03, 1.65244590e-07],
[ 3.10840686e-01, 3.11246556e-01, 3.10621286e-01,
2.99245141e-01, 2.25993744e-01, 6.90567301e-02,
2.08705341e-03, 1.69364802e-07],
[ 2.75044905e-01, 2.77957796e-01, 2.84838107e-01,
2.87289856e-01, 2.25495060e-01, 7.01280803e-02,
2.12356290e-03, 1.72328080e-07],
[ 2.39581254e-01, 2.47431270e-01, 2.64804745e-01,
2.79143152e-01, 2.25105505e-01, 7.07709762e-02,
2.14556867e-03, 1.74114183e-07],
[ 2.17851776e-01, 2.33001039e-01, 2.56971289e-01,
2.76235964e-01, 2.24959810e-01, 7.09852843e-02,
2.15292064e-03, 1.74710909e-07]])
porc = np.array(
[[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00],
[ 6.63005088e-02, 6.15505219e-02, 5.19144605e-02,
3.59973019e-02, 1.72856699e-02, 4.04282661e-03,
1.19149107e-04, 9.66862273e-09],
[ 1.22965433e-01, 1.15955474e-01, 9.98133525e-02,
7.04783427e-02, 3.42714449e-02, 8.05500561e-03,
2.37484183e-04, 1.92711990e-08],
[ 1.70153250e-01, 1.61708605e-01, 1.41571416e-01,
1.02262779e-01, 5.06813918e-02, 1.20063084e-02,
3.54196764e-04, 2.87421334e-08],
[ 2.08997626e-01, 1.99597326e-01, 1.76889221e-01,
1.30670788e-01, 6.62826679e-02, 1.58673244e-02,
4.68489485e-04, 3.80167299e-08],
[ 2.40517864e-01, 2.30550690e-01, 2.06258176e-01,
1.55481317e-01, 8.08966151e-02, 1.96098208e-02,
5.79581531e-04, 4.70316335e-08],
[ 2.65634664e-01, 2.55416803e-01, 2.30322161e-01,
1.76778346e-01, 9.44011160e-02, 2.32070515e-02,
6.86713977e-04, 5.57252635e-08],
[ 2.85182535e-01, 2.74967949e-01, 2.49702454e-01,
1.94798643e-01, 1.06725801e-01, 2.66340021e-02,
7.89154975e-04, 6.40382333e-08],
[ 2.99921021e-01, 2.89910174e-01, 2.64976409e-01,
2.09834718e-01, 1.17842977e-01, 2.98675715e-02,
8.86204754e-04, 7.19137571e-08],
[ 3.10544922e-01, 3.00892067e-01, 2.76680722e-01,
2.22189664e-01, 1.27757213e-01, 3.28866885e-02,
9.77200401e-04, 7.92980371e-08],
[ 3.17693646e-01, 3.08512817e-01, 2.85316521e-01,
2.32161302e-01, 1.36495718e-01, 3.56723712e-02,
1.06152039e-03, 8.61406310e-08],
[ 3.21959813e-01, 3.13329670e-01, 2.91354230e-01,
2.40038196e-01, 1.44100557e-01, 3.82077361e-02,
1.13858880e-03, 9.23947971e-08],
[ 3.23897230e-01, 3.15864865e-01, 2.95238159e-01,
2.46099306e-01, 1.50622814e-01, 4.04779696e-02,
1.20787931e-03, 9.80178130e-08],
[ 3.24028345e-01, 3.16612127e-01, 2.97390842e-01,
2.50614429e-01, 1.56118285e-01, 4.24702705e-02,
1.26891869e-03, 1.02971268e-07],
[ 3.22851280e-01, 3.16042824e-01, 2.98217176e-01,
2.53844434e-01, 1.60644109e-01, 4.41737757e-02,
1.32129011e-03, 1.07221325e-07],
[ 3.20846534e-01, 3.14611826e-01, 2.98108366e-01,
2.56040317e-01, 1.64255802e-01, 4.55794773e-02,
1.36463594e-03, 1.10738951e-07],
[ 3.18483454e-01, 3.12763174e-01, 2.97445289e-01,
2.57439400e-01, 1.67004390e-01, 4.66801399e-02,
1.39866020e-03, 1.13500118e-07],
[ 3.16226545e-01, 3.10935568e-01, 2.96598289e-01,
2.58256314e-01, 1.68933547e-01, 4.74702244e-02,
1.42313055e-03, 1.15485964e-07],
[ 3.14541767e-01, 3.09564990e-01, 2.95911072e-01,
2.58667378e-01, 1.70076867e-01, 4.79458238e-02,
1.43787992e-03, 1.16682924e-07],
[ 3.13893026e-01, 3.09047380e-01, 2.95647805e-01,
2.58790499e-01, 1.70455556e-01, 4.81046140e-02,
1.44280759e-03, 1.17082821e-07]])
avp = np.array(
[[ 5.20011862e-01, 4.91272969e-01, 4.33747613e-01,
3.29460228e-01, 1.80717037e-01, 4.59547722e-02,
1.36505235e-03, 1.10771491e-07]])
settle = np.array(
[[ 0.00867514, 0.01698889, 0.03363015, 0.06379901, 0.10682829,
0.14581308, 0.15871225, 0.15910711]])
########################################################
##luetal2010 input to generate expected data
# import numpy as np
# from geotecha.consolidation.luetal2010 import luetal2010
# rc=0.1
# re=0.9
# H=10
# rs=0.15
# ks=0.5
# kv=1.0
# kvc=2000
# kh=1.0
# khc=2
# mvs=0.1
# mvc=0.0005
# gamw=10
# utop=0.9
# ubot=0.2
# nterms=100
# z = np.linspace(0,10,20)# np.array([0, 3.0, 6.0, 9.0, 10.0])
# t = np.logspace(np.log10(0.01), np.log10(10), 8)#np.array([0.1, 0.8, 2.0])
#
# por, pors, porc, avp, settle = luetal2010(
# z,t, rc, re, H, rs, ks,
# kv, kvc, kh, khc,
# mvs, mvc, gamw, utop, ubot, nterms)
# #Note for avp and settle you'll need to add an extra [] around the existing [].
# for v in ['z', 't', 'por', 'pors', 'porc', 'avp', 'settle']:
# print('{} = np.{}\n'.format(v, repr(eval(v))).replace("array([","array(\n ["))
#########################################################
reader = ("""\
H = 10
drn = 1
#re=0.9, rc=0.1, rs=0.15, kh/ks=2, n=9, s=1.5, kap=2.0
#mu=1.87284145852, eta = 2/re**2/mu=1.3183901879341955
mvs = 0.1
mvc = 0.0005
mvref = mvs*9**2/(9**2 - 1 + mvs/mvc)
mv = PolyLine([0,1], [1,1])
khref = 1.0
etref = 1.3183901879341955
n=9
dTh = khref * etref/mvref/10 #khref/mvref/gamw * etref
kh = khc=PolyLine([0,1], [1,1])
et = PolyLine([0,1], [1,1])
khcref = 2.0
dThc = 8*khcref / (mvref * 10) /0.1**2
khc = PolyLine([0,1], [1,1])
kvref = 1.0
dTv = kvref/mvref/10/ H**2
kv = PolyLine([0,1], [1,1])
kvcref = 2000
kvc = PolyLine([0, 1], [1, 1])
dTvc=kvcref/H**2/mvref/10
neig = 40
surcharge_vs_depth = [PolyLine([0,1], [0.9,0.2])]
surcharge_vs_time = [PolyLine([0,0,10], [0,1,1])]
ppress_z = np.%s
tvals = np.%s
avg_ppress_z_pairs = [[0,1]]
settlement_z_pairs = [[0,1]]
""" % (repr(z/10), repr(t)))
a = Speccon1dVRC(reader)
a.make_all()
# custom plots
title = ("Lu et al (2010) Stone column, load is linear with depth")
fig = plt.figure(figsize=(13,6))
fig.suptitle(title)
#z vs u
ax1 = fig.add_subplot("141")
ax1.set_xlabel('Excess pore pressure \nin soil, kPa')
ax1.set_ylabel('Depth')
ax1.invert_yaxis()
ax1.plot(pors, z,
ls=".", color='Blue', marker="+", ms=5,
label='expected')
ax1.plot(a.pors, z,
ls='-', color='red', marker='o', ms=5, markerfacecolor='None',
markeredgecolor='red',
label='calculated')
# avp vs t
ax2 = fig.add_subplot("143")
ax2.set_xlabel('Time')
ax2.set_ylabel('Average excess pore press \n (soil and column), kPa')
ax2.set_xscale('log')
ax2.set_xlim((0.01, 10))
ax2.plot(t, avp[0],
ls=".", color='Blue', marker="+", ms=5,
label='expected')
ax2.plot(t, a.avp[0],
ls='-', color='red', marker='o', ms=5, markerfacecolor='None',
markeredgecolor='red',
label='calculated')
# settlement vs t
ax3 = fig.add_subplot("144")
ax3.set_xlabel('Time')
ax3.set_ylabel('Settlement')
ax3.invert_yaxis()
ax3.set_xscale('log')
ax3.set_xlim((0.01, 10))
ax3.plot(t, settle[0],
ls=".", color='Blue', marker="+", ms=5,
label='expected')
ax3.plot(t, a.set[0],
ls='-', color='red', marker='o', ms=5, markerfacecolor='None',
markeredgecolor='red',
label='calculated')
leg = ax3.legend()
leg.draggable()
ax4 = fig.add_subplot("142")
ax4.set_xlabel('Excess pore pressure \nin column, kPa')
ax4.set_ylabel('Depth')
ax4.invert_yaxis()
ax4.plot(porc, z,
ls=".", color='Blue', marker="+", ms=5,
label='expected')
ax4.plot(a.porc, z,
ls='-', color='red', marker='o', ms=5, markerfacecolor='None',
markeredgecolor='red',
label='calculated')
fig.subplots_adjust(top=0.90, bottom=0.15, left=0.1, right=0.94, wspace=0.4)
#fig.tight_layout()
plt.show()
(Source code, png, hires.png, pdf)
