seismi risk analysis of l’ aquila - vce risk analysis of l’ aquila gas ... to provide a measure...

Esposito S., Iervolino I. Paper ID: 2999

SEISMIC RISK ANALYSIS OF L’ AQUILA GAS DISTRIBUTION NETWORK

Introduction Results Framework Application


Simona Esposito, Iunio Iervolino

Department of Structural Engineering, University of Naples Federico II

Paper ID. 2999




Past earthquakes have caused a significant amount of damage to gas networks, especially to its main component, that is buried pipelines.

A gas distribution system comprises two main categories of components:

• point-like critical facilities (reduction stations and groups where gas is pressurized/depressurized and/or measured) • pipelines constituting the distribution network

The causes of earthquake damage to components of gas systems include:

• Permanent ground deformation (PGD) hazard produced by fault displacements, landslides, liquefaction of sandy soils •Transient ground deformation (TGD) hazard associated with travelling seismic waves.




Impact of L`Aquila 2009 earthquake (Mw 6.3) on the performance of the local medium- and low-pressure gas distribution networks

Damage to stations: repairs to the input/output network of Onna M/R and inclusion of stop-system; -RG housed in a masonry kiosk closed to building and damaged following the 6th April 2009 earthquake;

Damage to gas pipes ; gas welded joint of a LP steel pipe pulled apart in Paganica (AQ); gas pipe connected to a damaged bridge in Onna (AQ) replaced with a stand-alone pipe

Assessment of the damage occurred on pipes Resilience-related curve for the L’Aquila gas network

following the 2009 event.




ACCORDING TO THE PBEE FRAMEWORK

To evaluate the seismic risk of the medium pressure portion of L’Aquila (central Italy) gas distribution system that includes:

• Probabilistic characterization of seismic input

• Definition of vulnerability of the network’s components

• Analysis of the system’s seismic performance measures

• Probabilistic simulation for risk assessment.

OBJECTIVE




1.Transient ground deformation (TGD) •Seismic zone characterization •Ground motion estimation Estimation of peak parameters through ground motion prediction equations considering:

oRandom fields of peak parameters and spatial correlation oCross -correlation between intensity measures (IMs) oConditional hazard approach

•Site amplification

2.Permanent Ground Deformation (PGD) The principal forms of permanent ground deformation are:

oCoesismic rupture

oLandsliding

oSeismic settlement and lateral spreading

due to soil liquefaction.




3.Vulnerability assessment •Characterization •Fragility analysis

•4.Perfomance

•Network modelling •Perfomance and loss

Re.Mi

STATION

Regulators

First Regulator

Second Regulator

Building

MechanicalEquipment

Boilers

Alimentation

REPAIR RATIO FAULT TREE ANALYSIS

Model Analysis tools Connectivity •Graph : nodes and

edges •Functionality

Graph theory algorithms

Flow-based •Graph : nodes and edges •Functionality

•Flow equation •Newton Nodal and Loops methods

Performance indicators: To provide a measure of the impact of the earthquake on the system functionality and serviceability.

Connectivity Loss

0

1

1

n

i

i

n

i

i

Q

SSI

Q

,

,

1i

source dam

i

source orig i

NCL

N

Serviceability index




Filter

RegulatorMonitor

Safety-

relief

valveShut-

off

valve

L’Aquila Gas distribution system •Gas distribution via a 621 km pipeline (STEEL /HDPE ) network: 234 Km at Medium Pressure; 387 Km at Low Pressure .

•The MP network connection to HP network through: 3 Metering / Pressure Reduction M/R Stations (Re.Mi. in Italian).

•The transformation of the MP into the LP through Reduction Groups (RGs).

SOFTWARE IMPLEMENTATION (OOFIMS)




•Simulation of the event on the considered seismic fault

The Paganica fault (normal fault type) , characteristic earthquakes of

moment magnitude Mw 6.3.

•Simulation of the random field of the primary IM at bedrock

(PGAr);

Akkar and Bommer (2010) GMPE and Esposito and Iervolino (2011)

spatial correlation model

•Conditional simulation of the cross-correlated secondary IMs at

bedrock (PGVr);

Start iteration i

Computation of Jooyner-Boore distance (Rjb) for the regular grid

Interpolation of log(PGAr) at the vulnerable sites

Sampling of epicenter location Uniform PDF

on the Paganica fault

Computation of the mean of the logarithm of primary IM on the rock (PGAr) at each site of the regular grid,

conditional to MW 6.3 and Rjb, mlog(PGAr)|Mw,Rjb

Sampling of inter-event residual of log(PGAr), hlog(PGAr)

Computation of intra-event residual of log(PGAr) at each site of the regular grid sampling from a GRF,

elog(PGAr)

GMPE of Akkar and Bommer

(2010)

Normal PDF from Akkar and Bommer (2010)

Spatial correlation model from Esposito and Iervolino

(2011) and standard deviation from Akkar and Bommer

(2010)

log(PGAr)= mlog(PGAr)|Mw,Rjb + elog(PGAr) + hlog(PGAr)

Computation of IMs at surface for the vulnerable sites, log(PGAS), log(PGVS)

Amplification factors according to site classification of Akkar and Bommer (2010)

Computation of the mean of the logarithm of secondary IM on the rock (PGVr) at the vulnerable

sites, mlog(PGVr)|Mw,Rjb

GMPE of Akkar and Bommer

(2010)

Computation of the conditional mean and standard deviation of the logarithm of PGVr at the vulnerable sites, mlog(PGVr)|log(PGAr),Mw,Rjb, slog(PGVr)|log(PGAr), Mw,Rjb

Correlation coefficient estimated, standard deviation

from Akkar and Bommer (2010)

Computation of the logarithm of PGVr at the vulnerable sites, log(PGVr)

According to condtional

hazard approach of Iervolino et al. (2010)




•Amplification due to local site conditions to get PGAs and PGVs

that are the IMs at the surface;

According to the site classification scheme adopted by the GMPE

•Simulation of displacement

consequential to PGD (landslide)

•Critical acceleration map according to

Hazus (FEMA, 2004) methdology

•Fragility assessment

Poisson repair rates function of PGVS and

PGD (pipelines) – ALA (2001) according to

Esposito et al (2013) and lognormal

fragility curves for un-anchored

compressor stations (FEMA, 2004)

•Connectivity analysis

Two performance indicators (Connectvity

Loss and Serviceability ratio)

Computation of the landslide displacement for the vulnerable sites, PGD

Displacement model of Saygili and Rathje

(2008)

Kc<PGAS

Pland<P*la

nd

Yes

Yes

No

No

PGD=0

Landslide Critical Acceleration Map

Map area proportion from Hazus(2004)

Stop iteration i

Computation of the damage state of M/R stations as a

function of PGAS

Fragility curve form Hazus (2004)

Computation of repair ratio RR for pipelines as a function of PGVS and PGD

Fragility curve from ALA (2001)

Computation of the performance indicators, CL, SR

CL, mCL sCL, SR, mSR sSR

Performance indicators from Adachi and

Ellingwood (2008), Poljanšek et al. (2012)

Sampling of probability of displacement, Pland Uniform PDF




Disaggregation of network performance, which indicated a clear influence, on the earthquake loss, of the damage state of the M/R stations

Complementary cumulative distribution function (CCDF). Different behavior due to the different definition of the two performance indicators and the network’s configuration.

seismi risk analysis of l’ aquila - vce risk analysis of l’ aquila gas ... to provide a measure...

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