def fragility_process(an_type, T, Gamma, w, EDPlim, dcroof, EDPvec, RDvec, SPO, bUthd, noBlg, g, MC, Sa_ratios, plot_feature, N, Tc, Td):
plotflag, linew, fontsize, units, iml = plot_feature[0:5]
allSa, allbTSa, allLR50, allbLR = [],[],[],[]
for blg in range(0,noBlg):
# Derive median Sa value (median of Sa) of capacity for each Limit State and corresponding overall dispersion std(log(Sa))
if an_type==0: # Ruiz-Garcia Miranda's method
[SaT50, bTSa] = simplified_bilinear(T[blg], Gamma[blg], EDPlim[blg], dcroof[blg], EDPvec[blg], RDvec[blg], SPO[blg], bUthd[blg], g, MC)
elif an_type==2: # Dolsek and Fajfar's method
[mc,a,ac,r,mf] = get_spo2ida_parameters(SPO[blg], T[blg], Gamma[blg]) # Convert MDoF into SDoF
[SaT50,bTSa] = DFfragility(T[blg], Gamma[blg], EDPlim[blg], dcroof[blg], EDPvec[blg], RDvec[blg], SPO[blg], bUthd[blg], mc, r, g, Tc, Td, MC)
else: # Vamvatsikos and Cornell's method
[mc,a,ac,r,mf] = get_spo2ida_parameters(SPO[blg], T[blg], Gamma[blg]) # Convert MDoF into SDoF
[idacm, idacr] = spo2ida_allT(mc,a,ac,r,mf,T[blg],pw,plotflag[1],filletstyle,N,linew,fontsize) # apply SPO2IDA procedure
[SaT50,bTSa] = spo2ida(idacm, idacr, mf, T[blg], Gamma[blg], g, EDPlim[blg], dcroof[blg], EDPvec[blg], RDvec[blg], SPO[blg], bUthd[blg], MC)
# Converting the Sa(T1) to Sa(Tav), the common IM
SaTlogmean_av, bTSa_av = np.log(SaT50)*Sa_ratios[blg], np.array(bTSa)*Sa_ratios[blg]
if blg<20:
plot_fragility(iml,np.exp(SaTlogmean_av),bTSa_av,0.5,fontsize,units,'off')
allSa.append(SaTlogmean_av)
allbTSa.append(bTSa_av)
# Combine the fragility of each building in a single lognormal curve with
# mean = weighted_mean(means) and std = SRSS(weighted_std(means),weighted_mean(stds))
log_meanSa, log_stSa = [],[]
for i in range(0,len(dcroof[0])):
SaLS = [ele[i] for ele in allSa]
StdSaLS = [ele[i] for ele in allbTSa]
log_meanSa.append(np.average(SaLS,weights = w)) # weighted log-mean mean(log(Sa))
log_stSa.append(np.sqrt(np.sum(w*(np.power((SaLS-log_meanSa[i]),2)+np.power(StdSaLS,2))))) # weighted log-std (dispersion)
return [log_meanSa, log_stSa]
def vulnerability_process(an_type, T, Gamma, w, EDPlim, dcroof, EDPvec, RDvec, SPO, bUthd, noBlg, g, MC, Sa_ratios, plot_feature, N, Tc, Td):
plotflag,linew,fontsize,units,iml = plot_feature[0:5]
LR50s = []
bLRs = []
for blg in range(0,noBlg):
LRs = []
# Vamvatsikos and Cornell's method
if np.array(bUthd[blg]).any()>0:
# If some damage criteria have uncertainty
# Monte Carlo realisations of damage criteria e derivation of fragility curves
# for each sample, mean loss ratios are computed at each IML from all samples
for i in range (0,MC):
dc_sample = np.zeros_like(dcroof[blg])
EDP_sample = np.zeros(len(dcroof[blg])+1)
for j in range(0,len(dcroof[blg])):
if bUthd[blg][j]>0:
EDP_sample[j+1] = np.random.lognormal(np.log(EDPlim[blg][j]),bUthd[blg][j],1)
while (EDP_sample[j+1]<=EDP_sample[j]):
EDP_sample[j+1] = np.random.lognormal(np.log(EDPlim[blg][j]),bUthd[blg][j],1)
else:
EDP_sample[j+1] = EDPlim[blg][j]
dc_sample[j] = np.interp(EDP_sample[j+1],EDPvec[blg],RDvec[blg]);
EDP_sample = EDP_sample[1::]
if an_type == 0:
[SaT50, bTSa] = simplified_bilinear(T[blg], Gamma[blg], EDP_sample, dc_sample, EDPvec[blg], RDvec[blg], SPO[blg], np.zeros_like(bUthd[blg]), g, MC)
elif an_type==2: # Dolsek and Fajfar's method
[mc,a,ac,r,mf] = get_spo2ida_parameters(SPO[blg], T[blg], Gamma[blg]) # Convert MDoF into SDoF
[SaT50,bTSa] = DFfragility(T[blg], Gamma[blg], EDP_sample, dc_sample, EDPvec[blg], RDvec[blg], SPO[blg], np.zeros_like(bUthd[blg]), mc, r, g, Tc, Td, MC)
else:
[mc,a,ac,r,mf] = get_spo2ida_parameters(SPO[blg], T[blg], Gamma[blg])
[idacm, idacr] = spo2ida_allT(mc,a,ac,r,mf,T[blg],pw,[0],filletstyle,N,linew,fontsize)
[SaT50,bTSa] = spo2ida(idacm, idacr, mf, T[blg], Gamma[blg], g, EDP_sample, dc_sample, EDPvec[blg], RDvec[blg], SPO[blg], np.zeros_like(bUthd[blg]), MC)
# Conversions
SaTlogmean_av, bTSa_av = np.log(SaT50)*Sa_ratios[blg], np.array(bTSa)*Sa_ratios[blg]
LRs.append(damage_to_loss(SaTlogmean_av,bTSa_av,iml,cd+'/NSP/inputs'))
else:
# If any damage criteria have uncertainty
if an_type == 0:
# Ruiz-Garcia Miranda's method the uncertainty in the damage criteria is already included in the total dispersion in a simplified way
[SaT50, bTSa] = simplified_bilinear(T[blg], Gamma[blg], EDPlim[blg], dcroof[blg], EDPvec[blg], RDvec[blg], SPO[blg], bUthd[blg], g, MC)
elif an_type == 2:
[mc,a,ac,r,mf] = get_spo2ida_parameters(SPO[blg], T[blg], Gamma[blg]) # Convert MDoF into SDoF
[SaT50,bTSa] = DFfragility(T[blg], Gamma[blg], EDPlim[blg], dcroof[blg], EDPvec[blg], RDvec[blg], SPO[blg], bUthd[blg], mc, r, g, Tc, Td, MC)
else:
[mc,a,ac,r,mf] = get_spo2ida_parameters(SPO[blg], T[blg], Gamma[blg])
[idacm, idacr] = spo2ida_allT(mc,a,ac,r,mf,T[blg],pw,[0],filletstyle,N,linew,fontsize)
[SaT50,bTSa] = spo2ida(idacm, idacr, mf, T[blg], Gamma[blg], g, EDPlim[blg], dcroof[blg], EDPvec[blg], RDvec[blg], SPO[blg], bUthd[blg], MC)
# Conversions
SaTlogmean_av, bTSa_av = np.log(SaT50)*Sa_ratios[blg], np.array(bTSa)*Sa_ratios[blg]
LR50 = damage_to_loss(SaTlogmean_av,bTSa_av,iml,cd+'/NSP/inputs')
LRs.append(LR50)
# Define Vulnerability curve for each building
LR50s.append(np.mean(LRs,axis = 0))
bLRs.append(np.std(LRs, axis = 0))
# Combine the Loss Ratios of each building in a single LR for each iml,
# with mean = mean(LRs) and std = std(LRs)
lr = np.array(LR50s)
bLRs = np.array(bLRs)
LR50 = np.average(lr, axis=0, weights=w) # weighted mean
w2 = np.array([np.repeat(ele,len(iml)) for ele in w])
bLR = np.sqrt(np.sum(np.multiply(np.power(bLRs,2)+np.power(lr-LR50,2),w2),axis=0))
return [LR50, bLR]
EDPlim = [[0.002, 0.005, 0.01, 0.02, 0.04, 0.06, 0.08], dcroof[0]]
with open(cd + '/inputs/EDPvec-RDvec.csv', 'rb') as f:
reader = csv.reader(f)
newlist = [row for row in reader]
EDPvec = [np.array([float(ele[0]) for ele in newlist]), [0, 1000]]
RDvec = [np.array([float(ele[1]) for ele in newlist]), [0, 1000]]
noBlg = 1
w = 1
dispersion = [np.repeat(0., len(dcroof[0])), np.repeat(0.25, len(dcroof[0]))]
g = 9.81
MC = 25
data = pd.DataFrame(columns=['Sa50-EdpRd0-beta0', 'bTSa50-EdpRd0-beta0'],
index=np.arange(len(dcroof[0])))
# <codecell>
for k in range(0, len(EDPvec)):
for j in range(0, len(dispersion)):
bUthd = [dispersion[j]] * noBlg
[SaT50, bTSa] = simplified_bilinear(T[0], Gamma[0], EDPlim[k],
dcroof[0], EDPvec[k], RDvec[k],
SPO[0], bUthd[0], g, MC)
data['Sa50-EdpRd' + str(k) + '-beta' + str(j)] = pd.DataFrame(SaT50)
data['bTSa50-EdpRd' + str(k) + '-beta' + str(j)] = pd.DataFrame(bTSa)
# <codecell>
data.to_csv('results/RGM_fragility.csv', header=True, index=True)
# <codecell>
def vulnerability_process(an_type, T, Gamma, w, EDPlim, dcroof, EDPvec, RDvec,
SPO, bUthd, noBlg, g, MC, Sa_ratios, plot_feature, N,
Tc, Td):
plotflag, linew, fontsize, units, iml = plot_feature[0:5]
LR50s = []
bLRs = []
for blg in range(0, noBlg):
LRs = []
# Vamvatsikos and Cornell's method
if np.array(bUthd[blg]).any() > 0:
# If some damage criteria have uncertainty
# Monte Carlo realisations of damage criteria e derivation of fragility curves
# for each sample, mean loss ratios are computed at each IML from all samples
for i in range(0, MC):
dc_sample = np.zeros_like(dcroof[blg])
EDP_sample = np.zeros(len(dcroof[blg]) + 1)
for j in range(0, len(dcroof[blg])):
if bUthd[blg][j] > 0:
EDP_sample[j + 1] = np.random.lognormal(
np.log(EDPlim[blg][j]), bUthd[blg][j], 1)
while (EDP_sample[j + 1] <= EDP_sample[j]):
EDP_sample[j + 1] = np.random.lognormal(
np.log(EDPlim[blg][j]), bUthd[blg][j], 1)
else:
EDP_sample[j + 1] = EDPlim[blg][j]
dc_sample[j] = np.interp(EDP_sample[j + 1], EDPvec[blg],
RDvec[blg])
EDP_sample = EDP_sample[1::]
if an_type == 0:
[SaT50, bTSa
] = simplified_bilinear(T[blg], Gamma[blg], EDP_sample,
dc_sample, EDPvec[blg],
RDvec[blg], SPO[blg],
np.zeros_like(bUthd[blg]), g, MC)
elif an_type == 2: # Dolsek and Fajfar's method
[mc, a, ac, r, mf] = get_spo2ida_parameters(
SPO[blg], T[blg], Gamma[blg]) # Convert MDoF into SDoF
[SaT50, bTSa] = DFfragility(T[blg], Gamma[blg], EDP_sample,
dc_sample, EDPvec[blg],
RDvec[blg], SPO[blg],
np.zeros_like(bUthd[blg]), mc,
r, g, Tc, Td, MC)
else:
[mc, a, ac, r,
mf] = get_spo2ida_parameters(SPO[blg], T[blg], Gamma[blg])
[idacm,
idacr] = spo2ida_allT(mc, a, ac, r, mf, T[blg], pw, [0],
filletstyle, N, linew, fontsize)
[SaT50,
bTSa] = spo2ida(idacm, idacr, mf, T[blg], Gamma[blg], g,
EDP_sample, dc_sample, EDPvec[blg],
RDvec[blg], SPO[blg],
np.zeros_like(bUthd[blg]), MC)
# Conversions
SaTlogmean_av, bTSa_av = np.log(
SaT50) * Sa_ratios[blg], np.array(bTSa) * Sa_ratios[blg]
LRs.append(
damage_to_loss(SaTlogmean_av, bTSa_av, iml,
cd + '/NSP/inputs'))
else:
# If any damage criteria have uncertainty
if an_type == 0:
# Ruiz-Garcia Miranda's method the uncertainty in the damage criteria is already included in the total dispersion in a simplified way
[SaT50,
bTSa] = simplified_bilinear(T[blg], Gamma[blg], EDPlim[blg],
dcroof[blg], EDPvec[blg],
RDvec[blg], SPO[blg], bUthd[blg],
g, MC)
elif an_type == 2:
[mc, a, ac, r, mf] = get_spo2ida_parameters(
SPO[blg], T[blg], Gamma[blg]) # Convert MDoF into SDoF
[SaT50, bTSa] = DFfragility(T[blg], Gamma[blg], EDPlim[blg],
dcroof[blg], EDPvec[blg],
RDvec[blg], SPO[blg], bUthd[blg],
mc, r, g, Tc, Td, MC)
else:
[mc, a, ac, r,
mf] = get_spo2ida_parameters(SPO[blg], T[blg], Gamma[blg])
[idacm,
idacr] = spo2ida_allT(mc, a, ac, r, mf, T[blg], pw, [0],
filletstyle, N, linew, fontsize)
[SaT50,
bTSa] = spo2ida(idacm, idacr, mf, T[blg], Gamma[blg], g,
EDPlim[blg], dcroof[blg], EDPvec[blg],
RDvec[blg], SPO[blg], bUthd[blg], MC)
# Conversions
SaTlogmean_av, bTSa_av = np.log(SaT50) * Sa_ratios[blg], np.array(
bTSa) * Sa_ratios[blg]
LR50 = damage_to_loss(SaTlogmean_av, bTSa_av, iml,
cd + '/NSP/inputs')
LRs.append(LR50)
# Define Vulnerability curve for each building
LR50s.append(np.mean(LRs, axis=0))
bLRs.append(np.std(LRs, axis=0))
# Combine the Loss Ratios of each building in a single LR for each iml,
# with mean = mean(LRs) and std = std(LRs)
lr = np.array(LR50s)
bLRs = np.array(bLRs)
LR50 = np.average(lr, axis=0, weights=w) # weighted mean
w2 = np.array([np.repeat(ele, len(iml)) for ele in w])
bLR = np.sqrt(
np.sum(np.multiply(np.power(bLRs, 2) + np.power(lr - LR50, 2), w2),
axis=0))
return [LR50, bLR]