from materials.ec3 import EC3_limit_state_checking as EC3lsc from materials.ec3 import EC3_materials S355JR= EC3_materials.S355JR gammaM0= 1.05 S355JR.gammaM= gammaM0 IPE400= EC3_materials.IPEShape(S355JR,"IPE_400") # Geometry k1= 1.0; k2= 1.0 #Check results page 32 L= 6.0 # Bar length (m) x= [0.0,0.25*L,0.5*L,0.75*L,1.0*L] M= [-93.7,0,114.3,0,111.4] Mi=intp.interpEquidistPoints(xi=x,yi=M,nDiv=4) mgf= EC3lsc.MomentGradientFactorC1(Mi) Mcr1= IPE400.getMcr(L,Mi) Mcr1Teor= 164.7e3 ratio1= abs(Mcr1-Mcr1Teor)/Mcr1Teor #NOTE: Here there is a big difference between the results # from Lopez-Serna method # 317 kN.m and those from the paper 164.7 kN.m # In theory results from Lopez-Serna method are safe enough. #Check results page 34 L= 3 # Bar length (m) x= [0.0,0.25*L,0.5*L,0.75*L,1.0*L] M= [-93.7e3,-93.7e3/2.0,0.0,114.3e3/2.0,114.3e3] Mi=intp.interpEquidistPoints(xi=x,yi=M,nDiv=4)
import geom import xc from materials.ec3 import EC3_limit_state_checking as EC3lsc from geom_utils import interpolation as intp __author__= "Luis C. Pérez Tato (LCPT)" __copyright__= "Copyright 2014, LCPT" __license__= "GPL" __version__= "3.0" __email__= "*****@*****.**" supportCoefs= EC3lsc.SupportCoefficients(k1= 1.0,k2= 1.0) x= [0.0,0.25,0.5,0.75,1.0] M= [50,122.5,105,-2.5,-200] Mi=intp.interpEquidistPoints(xi=x,yi=M,nDiv=4) mgf1= EC3lsc.MomentGradientFactorC1(Mi) C11= mgf1.getC1(supportCoefs) C11Teor= math.sqrt(35*200**2/(200**2+9*122.5**2+16*105**2+9*2.5**2)) x= [0.0,0.25,0.4,0.5,0.75,1.0] M= [150,260,283.5,280,210,50] Mi=intp.interpEquidistPoints(xi=x,yi=M,nDiv=4) mgf2= EC3lsc.MomentGradientFactorC1(Mi) C12= mgf2.getC1(supportCoefs) C12Teor= math.sqrt(35*283.5**2/(283.5**2+9*260**2+16*280**2+9*210**2)) ratio1= abs(C11-C11Teor)/C11Teor ratio2= abs(C12-C12Teor)/C12Teor # print 'C1= ', C11 # print 'C1Teor= ', C11Teor
x.append(xi)
M.append(float(Mi))
xi += step
return [x, M]
supportCoefs = EC3lsc.SupportCoefficients(k1=1.0, k2=1.0)
nDiv = 20
step = 2 / nDiv
psi = list()
c1 = list()
psii = -1.0
C1i = 0.0
for i in range(1, nDiv + 2):
mD = caseASampleMoments(5.0, 10.0, psii)
Mi = intp.interpEquidistPoints(xi=mD[0], yi=mD[1], nDiv=4)
mgf = EC3lsc.MomentGradientFactorC1(Mi)
C1i = mgf.getC1(supportCoefs)
psi.append(psii)
c1.append(C1i)
psii += step
C1Teor = [
2.4494897427831783, 2.5202394341199024, 2.551789158017712,
2.539514968727602, 2.485250608738542, 2.3965787580611124,
2.284160962880643, 2.158748288883464, 2.0291986247835694,
1.9017865941611045, 1.7803963942770489, 1.6670801802983037,
1.5626453420774271, 1.4671286132146033, 1.3801311186847085,
1.301036207838119, 1.229142533193964, 1.1637407889826492,
1.1041549711386875, 1.0497621901901266, 1.0
]
x.append(xi)
M.append(float(Mi))
xi+= step
return [x,M]
supportCoefs= EC3lsc.SupportCoefficients(k1= 1.0,k2= 1.0)
nDiv= 20
step= 2/nDiv
psi=list()
c1=list()
psii= -1.0
C1i= 0.0
for i in range(1,nDiv+2):
mD= caseASampleMoments(5.0,10.0,psii)
Mi=intp.interpEquidistPoints(xi=mD[0],yi=mD[1],nDiv=4)
mgf= EC3lsc.MomentGradientFactorC1(Mi)
C1i= mgf.getC1(supportCoefs)
psi.append(psii)
c1.append(C1i)
psii+= step
C1Teor= [2.4494897427831783, 2.5202394341199024, 2.551789158017712, 2.539514968727602, 2.485250608738542, 2.3965787580611124, 2.284160962880643, 2.158748288883464, 2.0291986247835694, 1.9017865941611045, 1.7803963942770489, 1.6670801802983037, 1.5626453420774271, 1.4671286132146033, 1.3801311186847085, 1.301036207838119, 1.229142533193964, 1.1637407889826492, 1.1041549711386875, 1.0497621901901266, 1.0]
ratio1= 0
sz= len(C1Teor)
for i in range(0,sz):
ratio1+= (c1[i]-C1Teor[i])**2
ratio1= math.sqrt(ratio1)
# print 'psi= ', psi
