SEISMIC ZONATION FOR LIFELINES AND UTILITIES T.D. O

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SEISMIC ZONATION FOR LIFELINES AND UTILITIES
T.D. O’Rourke1 and S.-S. Jeon2
ABSTRACT
This paper focuses on the four principal uses of seismic zonation for lifelines and
utilities: 1) hazard delineation, 2) physical loss estimation, 3) assessment of economic and social
consequences, and 4) planning for emergency response and recovery. Emphasis is given to
geographic information systems (GIS) and their application to pipeline networks in evaluating
the spatial characteristics of earthquake effects. The paper examines the GIS databases for water
supply performance obtained for the 1994 Northridge and 1995 Kobe earthquakes.
Relationships among buried lifeline damage and various seismic parameters are examined, and
the parameters that are statistically most significant are identified. Using GIS data from the
Northridge earthquake, the relationships among pipeline repair rate, type of pipe, diameter, and
various seismic parameters are assessed. The effects of permanent ground deformation (PGD) on
buried lifelines are discussed. Air photo measurements of PGD and the spatial characteristics of
ground deformation revealed by these measurements are described. Mapping procedures for
PGD hazards, including probabilistic mapping techniques, are reviewed. Recent studies of
economic losses from earthquake damage to lifelines in Shelby County, TN are used to explore
the direct and indirect economic losses of lifeline systems.
Introduction
Seismic zonation has been defined in broad terms as the geographic delineation of
variations in the potential for various earthquake hazards (Cluff and Pecker, 1995). From a
geotechnical perspective, Finn (1991) refers to seismic microzonation as a procedure for
improving estimates of seismic hazard for design by taking the effects of local site conditions
into account. By virtually any definition, seismic zonation involves treatment of the spatial
variability of seismic excitation, geotechnical conditions, and the characteristics of the built
environment and communities affected by earthquakes.
Lifelines are often grouped into six principal types of systems (in alphabetical order):
electric power, gas and liquid fuels, telecommunications, transportation, wastewater facilities,
and water supply. These systems share three common characteristics: geographical dispersion,
interconnectivity, and diversity (O’Rourke, 1998). Lifelines are geographically dispersed over
broad areas, and are exposed to a wide range of seismic and geotechnical hazards, community
1, 2
– Thomas R. Briggs Professor of Engineering and Graduate Research Assistant, respectively, School of Civil &
Environmental Engineering, Cornell University, Ithaca, NY 14853-3501
uses, and interactions with other sectors of the built environment. They are interconnected and
interdependent. Each lifeline system is composed of many interconnected facilities and is
influenced by the performance of other lifeline systems. Lifeline performance is related to the
characteristics of many diverse components; most lifeline networks have been built over many
years and function with parts produced according to different construction and/or manufacturing
techniques, standards, and design procedures.
Given the characteristics of lifelines and the goals of seismic zonation, it is natural to
combine the two and apply zonation for more effective engineering and management of critical
transportation and utility systems. There has been substantial effort in recent years in applying
seismic zonation to lifeline networks with many examples available in the proceedings of
previous seismic zonation conferences (e.g., Earthquake Engineering Research Institute, 1991
and 1995).
This paper focuses on the four principal uses of seismic zonation for lifelines and
utilities: 1) earthquake hazard delineation, 2) physical loss estimation, 3) assessment of economic
and social consequences, and 4) planning for emergency response and recovery. Pipeline
systems, in particular, are used to illustrate current trends in seismic zonation and lifeline
engineering. Pipeline networks are an essential part of water, wastewater, gas, and liquid fuel
conveyance systems, and are used extensively in telecommunication and electric power
networks. The paper begins with an examination of GIS, followed by a discussion of buried
pipeline response to earthquakes. The principal uses of seismic zonation are covered by
describing recent investigations that are relevant for each use and summarizing the important
lessons learned for future applications.
Geographic Information Systems
GIS has been defined as “a set of tools for the input, storage and retrieval, manipulation
and analysis, and output of spatial data” (Marble, et al., 1984). It is also a problem solving
process, organized with respect to spatial and attribute data, that can be integrated with various
geographic technologies (e.g., remote sensing, global positioning systems, computer-aided
design, etc.) for support of decision making (Malezewski, 1999). GIS can be viewed, therefore,
as a system for integrating data from various disciplines and sources to guide planning and
management about a specific geographic area or site.
It is advantageous to think of GIS in terms of the different types of databases used to
characterize civil infrastructure systems (CIS). As proposed by Uy and O’Rourke (2000), CIS
involve both the physical environment and the social and economic characteristics of
communities that are located within the physical environment. The different aspects of CIS can
be divided into the two broad categories of societal and physical environment, which are shown
in Fig. 1.
Data about the societal environment is divided into demographic and economic
information. Demographic information includes data on political boundaries, population,
housing, and human hazards/security/vulnerability. Economic information includes personal and
CIVIL INFRASTRUCTURE SYSTEMS
The Societal Environment
Demographic
Economic
The Physical Environment
Natural
Environment
Built
Environment
Figure 1. Civil Infrastructure Systems
governmental income, personal and governmental spending, land and housing rent/value, trade
(manufacturing, retail, wholesale), and other types of industries.
The physical environment consists of the natural and built environments. The natural
environment includes land use and land cover, topography, geology and seismology, water and
air quality, sources of pollutants, wildlife/plant habitat, and natural hazards. Key elements of the
built environment are public and private buildings and lifeline systems.
The rapid development of computer mapping and visualization tools, embodied in GIS,
provides a powerful basis for evaluating earthquakes effects on lifelines, as well as the
consequences of these interactions on communities. It is not surprising, therefore, that GIS has
become an engine for driving new methodologies and decision support systems focused on the
spatial variation of potential earthquake effects. GIS, for example, is the backbone of the
National Loss Estimation Methodology sponsored by FEMA and implemented through HAZUS
computer software (National Institute for Building Science, 1997; Whitman, et al., 1997). GIS
also has been harnessed to explore the engineering and socioeconomic impacts of earthquakes
through multidisciplinary studies of the losses incurred by disruptions of water supply and
electric power systems (Chang, et al., 1996; Shinozuka, et al., 1998).
GIS Lifeline Earthquake Databases
Advances in seismic zonation for lifelines and utilities have been influenced in a
profound way by records of lifeline performance acquired after recent earthquakes, most notably
the 1989 Loma Prieta, 1994 Northridge, and 1995 Kobe earthquakes. Data from the Northridge
and Kobe earthquakes have been compiled in GISs of unprecedented size and complexity that
allow for a detailed examination of the spatial relationships among lifeline damage, permanent
and transient ground deformation, and the surface, subsurface, and groundwater conditions.
A comprehensive investigation of water supply pipeline damage after the 1995 Kobe
earthquake undertaken by the Japan Water Works Association (1996) has been described by
Shirozu, et al. (1996). The study was concentrated on seven water distribution systems, totaling
over 12,000 km of pipelines and 2,885 earthquake related repairs. Data on location, mode of
damage, material, diameter, and year of installation were collected and entered into a GIS
database with approximately 13,000 photos. Digitized maps of the water distribution pipelines
were made part of the GIS. Data on surficial geology, degree of liquefaction, seismic intensity,
and vectors of PGD determined by air photo measurements (Hamada and Wakamatsu, 1996)
were also included.
Statistics were compiled for each of six types of pipe composition, summarizing the
number of repairs, length of affected pipeline, repair rate (repairs divided by affected pipe
length), and damage mode observed in either the pipe body or joints. The highest repair rates
were incurred by steel pipelines with threaded couplings, asbestos cement (AC) pipelines, and
cast iron (CI) pipelines. The overall repair rates for ductile iron (DI) and welded steel pipelines
were approximately one third of that for CI mains, and the predominant mode of failure for DI
pipelines was pullout at mechanical joints. DI pipelines equipped with earthquake-resistant
restrained joints were not damaged, even in areas of liquefaction-induced PGD. Pipeline repair
rates were inversely proportional to diameter and increased in direct proportion to peak ground
acceleration. Repair rates in areas of liquefaction-induced PGD were 6 to 10 times higher than
repair rates in areas of comparable peak ground acceleration with no PGD effects.
The GIS database for the Kobe earthquake provides an exceptionally detailed and
comprehensive assessment of earthquake performance in a large, geographically dispersed
system. GIS for the 1994 Northridge earthquake provides a similar database for U.S. water
distribution performance, and is discussed in detail in forthcoming sections of this paper.
Pipeline Response to Earthquakes
Earthquakes cause transient ground deformation (TGD) and permanent ground
deformation (PGD), both of which affect underground pipelines. TGD is the dynamic response
of the ground, and PGD is the irrecoverable movement that persists after shaking has stopped.
PGD often involves large displacements, such as those associated with surface fault rupture and
landslides. TGD can cause soil cracks and fissures triggered by pulses of strong motion that
develop localized shear and tensile strains exceeding the strength of surficial soils.
The principal causes of PGD have been summarized and discussed by O’Rourke (1998).
They are faulting, tectonic uplift and subsidence, and liquefaction, landslides, and densification
of loose granular deposits. Liquefaction is the transformation of saturated cohesionless soil into
a liquefied state or condition of substantially reduced shear strength (Youd, 1973). Liquefactioninduced pipeline deformation can be caused by lateral spread, flow failure, local subsidence,
post-liquefaction consolidation, buoyancy effects, and loss of bearing (Youd, 1973; O’Rourke,
1998). It is widely accepted that the most serious pipeline damage during earthquakes is caused
by PGD. Furthermore, it is well recognized that liquefaction-induced PGD, especially lateral
spread, is one of the most pervasive causes of earthquake-induced lifeline damage (Hamada and
O’Rourke, 1992; O’Rourke and Hamada, 1992).
Ground displacement patterns associated with earthquakes depend on PGD source, soil
type, depth of ground water, slope, earthquake intensity at a given site, and duration of strong
ground shaking (O’Rourke, 1998). It is not possible to model with accuracy the soil
displacement patterns at all potentially vulnerable locations. Neverthless, it is possible to set
upper bound estimates of deformation effects on buried lifelines by simplifying spatially
distributed PGD as movement concentrated along planes of soil failure.
Various modes of pipeline distortion caused by PGD are illustrated in Fig. 2. Pipelines
crossing a fault plane subjected to oblique slip are shown in Fig. 2a. Reverse and normal faults
promote compression and tension, respectively. Strike slip may induce compression or tension,
depending on the angle of intersection between the pipeline and fault. Fig. 2b shows a pipeline
crossing a lateral spread or landslide perpendicular to the general direction of soil movement. In
this orientation, the pipeline is subject to bending strains and extension. As shown in Fig. 2c, the
pipeline will undergo bending and either tension or compression at the margins of the slide when
the crossing occurs at an oblique angle. Fig. 2d shows a pipeline oriented parallel to the general
direction of soil displacement. At the head of the zone of soil movement, the displacements
resemble normal faulting; under these conditions, the pipeline will be subjected to both bending
and tensile strains. At the toe of the slide, the displaced soil produces compressive strains in the
pipeline.
Strike
slip
Fault
plane
Pipeline subject
mainly to bending
ss
sv
Legend
sd - Dip slip
ss - Strike slip
sv - Vertical displacement
sh - Thrust displacement
sd
sh
Dip
slip
b) Perpendicular Crossing
a) Three-Dimensional View
Pipeline subject to
compression and bending
Pipeline subject to
tension and bending
c) Oblique Crossing
Pipeline subject to
tension and bending
Pipeline subject to
compression and bending
d) Parallel Crossing
Figure 2. Principal Modes of Soil-Pipeline Interaction Triggered by Earthquake-Induced PGD
(O’Rourke, 1998)
TGD generally induces much smaller levels of pipeline strain and deformation than PGD.
Neverthless, TGD covers a broader area than PGD. Pipeline systems involve many different
components, some of which are invariably weakened by corrosion and/or residual stress
concentrations. It follows, therefore, that the effects of widespread TGD, rather than localized
PGD, are more likely to overlap with weaker components.
At least four principal types of TGD have been identified: 1) traveling P and S waves, 2)
surface wave generation in large sedimentary basins, 3) vibration of relatively narrow soil-filled
valleys, and 4) ground oscillation (O’Rourke, 1998). This latter phenomenon was first described
by Youd and Keefer (1994). It involves transient lateral shear strains and oscillatory horizontal
movement of liquefiable soil relative to adjacent and underlying competent ground. Ground
oscillation has been shown to be the principal cause of extensive pipeline damage in the Marina
of San Francisco during the 1989 Loma Prieta earthquake (Pease and O’Rourke, 1997).
One of the principal challenges for seismic zonation and lifeline engineering is the
identification of landforms, subsurface conditions, and wave propagation scenarios that can
trigger PGD and various levels of TGD. During post-earthquake investigations, it is often
difficult to collect sufficient data over broad geographic areas to distinguish all PGD zones from
zones predominately influenced by TGD. As a planning and predictive exercise, the
identification of PGD vs. TGD zones is especially problematic because of the spatial variability
of soil and groundwater and the lack of specific information at most locations about the
subsurface nature of the ground.
A practical way to learn about the spatial variability of TGD and PGD effects is to study
the relationships among pipeline damage, strong motion, and locations of observed permanent
deformation for large, geographically dispersed systems. Recently, severe earthquakes have
occurred in urbanized areas with large pipeline systems and relatively dense networks of strong
motion instruments. In the next two sections, data on pipeline system behavior during the 1994
Northridge earthquake are examined and analyzed to illustrate regional patterns of TGD and
PGD, and their corresponding influence on water distribution system performance.
Earthquake Hazard Delineation
This section is divided into two subsections dealing with liquefaction hazards and
pipeline damage patterns. Liquefaction is a principal cause of PGD, and therefore potentially
disruptive for underground facilities. Mapping and characterizing liquefaction hazards allow for
the prediction of lifeline damage as well as the distribution of damage at liquefaction areas
throughout the system. Conversely, the pattern of post-earthquake damage allows for the
identification of seismic hazards at the locations of concentrated pipeline repair. In this section,
earthquake hazard delineation is examined through the identification of liquefiable soils and their
potential for lifeline damage, and through actual lifeline damage patterns as a means of
identifying local PGD hazards, including liquefaction.
Liquefaction Hazards
As mentioned previously, pipeline damage can be caused by various types of PGD
triggered by liquefaction. The delineation of liquefaction hazards, therefore, is of primary
interest in assessing the potential vulnerability of lifelines during an earthquake.
Youd and Perkins (1978) developed procedures for identifying sedimentary deposits
susceptible to liquefaction and combining maps of these deposits with the spatial delineation of
liquefaction opportunity. To quantify opportunity, it is necessary to identify earthquake sources,
provide estimates of the number and magnitude of earthquakes in the source zones, and apply
source-distance relationships that link moment magnitude Mw, and distance from source with
the occurrence of liquefaction.
On the basis of historical evidence of liquefaction-induced ground deformation, Youd
and Perkins (1987) also developed a technique for compiling liquefaction hazard maps by
mapping a parameter called the liquefaction severity index (LSI). The LSI represents the general
maximum differential ground movement (in inches) associated with lateral spread that can be
anticipated in active flood plains, deltas, or other areas of gently sloping Holocene fluvial
deposits. By means of statistical correlations, Youd and Perkins developed an equation relating
LSI, earthquake magnitude, and distance from the seismic energy source for data pertaining to
western U.S. earthquakes. The equation, a model of seismic sources, and a published seismic
risk algorithm were used to compile probabilistic LSI maps for southern California.
Bartlett and Youd (1995) developed an empirical model on the basis of multiple linear
regression (MLR) analyses for predicting the horizontal ground displacement resulting from
liquefaction-induced lateral spread. They used data from Japanese and U.S. earthquakes, and
distinguished two general types of lateral spread: 1) lateral spread towards a free face, and 2)
lateral spread down a gentle slope where a free face is absent. An equation was proposed for
predicting the magnitude of horizontal displacement as a function of six variables.
Using a similar database, Bardet, et al. (1996a and b) proposed a four-parameter MLR
model of the form:
Log (D + 0.01) = b0 + b off + b1Mw + b2 Log (R) + b3R
+ b4 Log (W) + b5 Log (S) +b6 Log (T15)
(1)
in which D is the horizontal displacement (m); Mw the moment magnitude; R the nearest
horizontal distance (km) to seismic energy source or fault rupture; S the slope (%) of the ground
surface; W the free face ratio (%) defined as height of the free face divided by the distance from
the free face to the location of displacement, T15 the thickness (m) of saturated cohesionless
soils (excluding depth > 20 m and clay content > 15%) with corrected standard penetration test
values N160 < 15. In free-face cases, the term Log (S) is zero. In ground slope cases, the term
Log (W) is zero. The b-coefficients were derived from MLR analyses, and are summarized by
Bardet, et al. (1999a).
In Fig. 3 the horizontal displacements predicted by the MLR model of Bardet, et al.
(1999a) are compared with liquefaction-induced lateral movements measured by means of air
photos for Kawagishi-cho area of Niigata after the 1964 Niigata earthquake. The two sitespecific variables of the four-parameter MLR model are the thickness of liquefiable deposits,
T15, and the free-face ratio, W. The spatial distribution of T15 is evaluated by interpolation
procedures, as explained by Bardet, et al. (1999a). Figs. 3d and e show the borings and contours
of T15 determined from the borings, respectively. Fig. 3f shows the spatial variation of W, and
Fig. 3c provides a three-dimensional plot of the ground surface taken from topographic maps of
the area.
The values of T15 and W were calculated at 50 by 34 grid points evenly spaced on a
10.5-m grid interval. The predicted displacement contours are illustrated in Fig. 3b, which can
be compared with the displacement contours determined from air photo measurements in Fig. 3a.
The irregular pattern of lateral movement at site, with a locally high concentration of
displacement, is not duplicated by the MLR model. The model is influenced strongly by W, and
this parameter in Eq. 1 tends to control the pattern of predicted displacement, resulting in
contours of predicted movement that are characteristically parallel to the free face. Even though
local concentrations of displacement were not predicted by the model, the average magnitude of
predicted displacements are nonetheless consistent with the measured movements.
The MLR four-parameter model predicts mean values, and probabilistic methods were
applied to determine confidence limits on lateral movement and the probability of exceeding
some level of ground deformation (Bardet, et al., 1999b). Fig. 4 presents a map of the predicted
probability of liquefaction-induced lateral spread larger than 2 m in the Kawagishi-cho area for
an earthquake with similar magnitude and source-to-site characteristics as the 1964 Niigata
earthquake. The parallel pattern of probability contours reflects the influence of W. Probabilitybased maps for liquefaction-induced displacements are consistent with the probabilistic
procedures frequently followed for seismic hazard characterization, and are valuable for
supporting decisions by lifeline operators regarding the earthquake risk incurred at key facilities.
Lifeline Damage Patterns
Seismic zonation generally involves the identification of seismic and geotechnical
hazards followed by an assessment of the potential damage associated with such hazards. The
process can be inverted for pipeline systems. When there are many repair locations, the pipeline
network can be used as a grid with which to characterize the local densities of repair so that
subsurface hazards can be located. Pipeline repair patterns for the 1989 Loma Prieta and 1906
San Francisco earthquakes were used successfully to supplement soil boring information and
characterize liquefaction hazards in San Francisco (Pease and O’Rourke, 1997; O’Rourke and
Pease, 1997). In fact, the combination of pipeline repair locations and soil borings was quite
effective in delineating zones vulnerable to liquefaction at a scale consistent with the size of a
city block. As previously mentioned, pipeline repair records in the Marina of San Francisco
were evaluated relative to liquefiable soils at the site. The pattern of pipeline repairs was
instrumental in identifying ground oscillation as the principal cause of damage, and in
developing simplified models to estimate the deformation that can be triggered by this type of
liquefaction phenomenon.
SHINAN
RIVER
(a) Observed
Displacement (cm)
(d) Borehole
Location
(b) Predicted
Displacement (cm)
(e) T15 (m)
(c) Ground
Surface (m)
(f) Free-face
Ratio H/L (%)
Figure 3. Representation of Measured and Predicted Lateral Displacements and Key Surface and
Subsurface Characteristics for MLR Model Prediction of Liquefaction-Induced
Movement During the 1964 Niigata Earthquake (Bardet, et al., 1999b)
ECHIGO RAILWAY
LINE
211950
211900
211850
211800
ECHIGO
RAILWAY
BRIDGE
KAWAGISHI CHO
211750
211700
SHINANO RIVER
211650
47000
47100
47200
47300
47400
47500
Figure 4. Probability of Liquefaction-Induced Lateral Spread Larger Than 2 m in the Kawagishicho Area Predicted by MLR Model (Bardet, et al., 1999b)
In this paper, the GIS database for water distribution system performance during the 1994
Northridge earthquake is used to delineate geotechnical hazards in the Los Angeles region. The
earthquake-induced damage to water pipelines and the database developed to characterize this
damage have been described elsewhere (O’Rourke, et al., 1998; O’Rourke and Toprak, 1997),
and only the salient features of this work are summarized herein. GIS databases for repair
locations, characteristics of damaged pipe, and lengths of distribution (pipe diameter < 600 mm)
and trunk (pipe diameter ≥ 600 mm) lines according to pipe composition and size were
assembled with ARC/INFO software. Nearly 10,000 km of distribution lines and over 1,000 km
of trunk lines were digitized.
Fig. 5 shows the portion of the Los Angeles water supply system most seriously affected
by the Northridge earthquake superimposed on the topography of Los Angeles. The figure was
developed from the GIS database, and shows all water supply pipelines plotted with a geospatial
precision of ± 10 m throughout the San Fernando Valley, Santa Monica Mountains, and Los
Angeles Basin. The rectilinear system of pipelines is equivalent to a giant strain gage. Seismic
intensity in the form of pipeline damage can be measured and visualized by plotting pipeline
repair rates and identifying the areas where the largest concentrations of damage rate occur. The
resulting areas reflect the highest seismic intensities as expressed by the disruption to
underground piping.
To develop a properly calibrated strain gage, it is necessary to select a measurement grid
with material having reasonably consistent properties and a damage threshold sensitive to the
externally imposed loads being measured. Fig. 6 presents charts showing the relative lengths of
trunk and distribution lines according to pipe composition. As shown by the pie chart, the most
pervasive material in the Los Angeles distribution system is CI. The 7,800 km of CI pipelines
have the broadest geographic coverage with sufficient density in all areas to qualify as an
appropriate measurement grid. Moreover, CI is a brittle material subject to increased rates of
damage at tensile strains on the order 250 to 500 µε. It is therefore sufficiently sensitive for
monitoring variations in seismic disturbance.
Fig. 7 presents a map of distribution pipeline repair locations and repair rate contours for
CI pipeline damage. The repair rate contours were developed by dividing the map into 2 km x 2
km areas, determining the number of CI pipeline repairs in each area, and dividing the repairs by
the distance of CI mains in that area. Contours then were drawn from the spatial distribution of
repair rates, each of which was centered on its tributary area. A variety of grids were evaluated,
and the 2 km x 2 km grid was found to provide a good representation of damage patterns for the
map scale of the figure (Toprak, et al., 1999).
The zones of highest seismic intensity are shown by areas of concentrated contours. In
each instance, areas of concentrated contours correspond to zones where the geotechnical
conditions are prone either to ground failure or amplification of strong motion. Each zone of
concentrated damage is labeled in Fig. 8 according to its principal geotechnical characteristics.
In effect, therefore, Fig. 7 is a seismic hazard map for the Los Angeles region, calibrated
according to pipeline damage during the Northridge earthquake.
Figure 5. Map of Los Angeles Water Supply System Affected by Northridge
Earthquake (O’Rourke and Toprak, 1997)
Cast Iron
11%
Concrete
18%
Ductile
Iron
1%
Trunk Lines : 1014 km
Distribution Lines : 10750 km
Riveted
Steel
14%
Steel
56%
a) Trunk Lines
Asbestos
9%
Steel
11%
Length (km)
10000
Concrete
Riveted Steel
Cast Iron
Steel
Asbestos Cement
Ductile Iron
1000
Ductile
Iron
4%
100
Cast Iron
76%
b) Distribution Lines
LADWP
LADWP
MWD
c) Combined Lines
Figure 6. Composition Statistics of Water Trunk and Distribution Lines in the City of
Los Angeles (O’Rourke and Toprak, 1997)
Figure 7. Cast Iron Pipeline Repair Rate Contours for the Northridge Earthquake
(O’Rourke and Toprak, 1997)
Figure 8. Geotechnical Characteristics of the Areas of Concentrated Pipeline Damage
After the Northridge Earthquake
Of special interest is the location of concentrated repair rate contours in the west central
part of San Fernando Valley (designated in Fig. 8 as the area of soft clay deposits). This area
was investigated by USGS researchers, who found it to be underlain by local deposits of soft,
normally consolidated clay (Holzer, et al., 1999). Field vane shear tests disclosed clay with
uncorrected, vane shear undrained strength, Suvst = 20-25 kPa, at a depth of 5 m, just below the
water table. USGS investigators concluded that the saturated sands underlying this site were not
subjected to liquefaction during the Northridge earthquake. Newmark sliding block analyses
reported by O’Rourke (1998) provide strong evidence that near source pulses of high
acceleration were responsible for sliding and lurching on the soft, normally consolidated clay
deposit. The results of GIS analysis and site investigations have important ramifications because
they show a clear relationship between PGD, concentrated pipeline damage, and the presence of
previously unknown deposits of normally consolidated clay.
With GIS, it is easy to divide a spatially distributed data set into arbitrarily sized areas. If
the areas are delineated by a framework of equally spaced, vertical and horizontal lines, the
resulting grid can be characterized by a single dimension representing one side of each area, n,
and the number, N, of areas comprising the total area of the system, Nn2. The choice of n can be
regarded as a means of visually resolving the distribution of damage.
Some practical questions emerge. Is there a useful relationship between n and the
visualization of zones with high damage? What values of n represent the best choices for
visualizing damage patterns?
Toprak, et al. (1999) found a relationship between the area of the map covered by repair
rate contours and the grid size, n, used to analyze the repair statistics. If the contour interval is
chosen as the average repair rate for the entire system or portion of the system covered by the
map, then the area in the contours represents the zones of highest (greater than average)
earthquake intensity as reflected in pipeline damage. The area within the contour lines divided
by an area closely related to the total area of the map, AC, is referred to as the threshold area
coverage, TAC (Toprak, et al., 1999). Alternate thresholds may also be defined on the basis of
the mean plus some measure of the variance.
The relationship between TAC and grid size was explored for a variety of map sizes. Fig.
9 presents a map showing the location of pipeline damage in the City of Los Angeles on which
are superimposed the rectangular outlines of several smaller areas. The visualization of damage
patterns for the entire area of the LADWP system affected by the Northridge earthquake and for
the smaller rectangular areas in Fig. 9 was investigated as a function of grid size. These data
were supplemented by pipeline and damage databases for the San Francisco Marina after the
1989 Loma Prieta earthquake. A map of San Francisco and the Marina are shown at the same
scale as the Los Angeles study area in Fig. 9.
A hyperbolic relationship was shown to exist between TAC and the dimensionless grid
size, defined as the square root of n2, the area of an individual cell, divided by the total map area,
AT. This relationship is illustrated in Fig. 10, for which a schematic of the parameters is
provided by the inset diagram. The relationship was found to be valid over a wide range of
different map scales spanning 1,200 km2 for the entire Los Angeles water distribution system
Figure 9. LADWP and Marina Study Area (Toprak, et al., 1999)
0.6
Threshold Area Coverage,TAC
0.5
0.4
Fit Curve
0.3
All LA
All SFV
0.2
Section of SFV
Sherman Oaks
North of LA Basin
0.1
San Francisco
TAC = DGS / ( 1.68 * DGS + 0.11)
0.0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Dimensionless Grid Size, DGS
0.8
Figure 10. Hyperbolic Fit for Threshold Area Coverage and Dimensionless Grid Size
(Toprak, et al., 1999)
affected by the Northridge earthquake to 1 km2 of the San Francisco water distribution system in
the Marina affected by the Loma Prieta earthquake (Toprak, et al., 1999). The data points refer
to maps of various dimensions from which the relationship was developed.
Physical Loss Estimation
In this section, physical loss estimation will be examined with emphasis on the
Northridge earthquake database. Loss estimation is addressed with respect to TGD and PGD
effects.
Transient Ground Deformation Effects
The records from 241 Northridge earthquake strong motion instruments were examined,
and the data from 164 corrected records were selected for regression analyses (O’Rourke and
Torprak, 1997). In this paper, additional studies were performed with data from 142 selected
Northridge earthquake records processed and catalogued by Silva (2000), and made available
online by the Pacific Engineering Earthquake Center. All records were chosen to represent free
field motion.
Fig. 11 shows the CI pipeline repair rate contours (see Fig. 7) superimposed on peak
ground velocity (PGV) zones. The PGV zones were developed by interpolating the larger of the
two horizontal components associated with each of 164 corrected motion sites. Using the GIS
database, a pipeline repair rate was calculated for each PGV zone, and correlations were made
between the repair rate and average PGV for each zone. As explained by O’Rourke (1998),
similar correlations were investigated for pipeline damage relative to spatially distributed peak
ground acceleration, spectral acceleration and velocity, Arias Intensity, Modified Mercalli
Intensity (MMI), and other indices of seismic response. By correlating damage with various
seismic parameters, regressions were developed between repair rate and measures of seismic
intensity.
The most statistically significant correlations for both distribution and trunk line repair
rates were found for PGV. The parameter, however, can be defined in several different ways. In
attenuation relationships, PGV is commonly defined as the geometric mean of the two largest
horizontal components (e.g., Campbell, 1997). PGV is also defined as the larger of the two
horizontal components (e.g., Boore, et al., 1997), which is the value used in Fig. 11. PGV may
also be defined as the maximum vector magnitude of the two horizontal components.
Figs. 12a, b, and c show the CI repair rates for the Northridge earthquake regressed
against the geometric mean PGV, maximum PGV, and maximum vector magnitude of PGV.
The data from the 142 records processed and catalogued by Silva were used to develop these
regressions. The plots indicate that the choice of PGV makes little difference in the statistical
significance of the regressions. All are characterized by r2 that are comparable, although the
highest r2 is associated with maximum PGV. Fig. 12d shows the maximum PGV regressed
against the geometric mean for 162 strong motion records for the Northridge earthquake.
In the remainder of this paper, the PGV cited will refer to the maximum PGV. If the
forthcoming regression equations for PGV are to be used in conjunction with attenuation
Figure 11. Pipeline Repair Rate Contours Relative to Northridge Earthquake Peak
Ground Velocity (O’Rourke and Toprak, 1997)
relationships based on the geometric mean, Fig. 12d can be applied to estimate maximum PGV,
from predicted geometric means.
As reported by O’Rourke and Jeon (1999), statistics were compiled for the repair rates of
CI, DI, and AC distribution lines, and regressions were developed for repair rate vs. PGV. The
compilation of statistics for steel distribution pipelines showed that there were many different
types of steel pipeline grouped within this category. Figs. 13a and b present pie charts for the
lengths and earthquake repairs associated with the different types of steel pipelines. At least six
different kinds of pipeline were identified after a careful review of the records.
Matheson and Mannesman steel pipelines were installed primarily in the 1920s and
1930s. Most were installed without cement linings and with minimal coating. In addition, their
wall thickness is generally less than that of other steel pipes with similar diameter. Matheson
and Mannesman steel pipelines are vulnerable to corrosion, as are steel pipelines with threaded
couplings. In the latter case, corrosion tends to concentrate at the threaded cross-sections. These
types of pipelines did not perform well during previous U.S. and Japanese earthquakes
(Katayama and Isoyama, 1980; Eguchi, 1982; O’Rourke, et al., 1985).
4
3
3
Repair Rate (Number of Repairs/km)
Repair Rate (Number of Repairs/km)
4
2
0.10
9
8
7
6
5
4
3
Fit Equation:
log(Y) = 1.09 * log(X) - 6.12
R-squared = 0.82
2
0.01
2
0.10
9
8
7
6
5
4
3
Fit Equation:
log(Y) = 1.21 * log(X) - 6.78
R-squared = 0.84
2
0.01
10
100
10
Geometric Mean PGV (cm/sec)
Maximum PGV (cm/sec)
(a) Geometric Mean
100
(b) Maximum
200
4
Fit Equation:
Y = 1.21 X
R-squared = 0.98
2
Maximum PGV (cm/sec)
Repair Rate (Number of Repairs/km)
3
0.10
9
8
7
6
5
4
3
150
1
1
100
50
Fit Equation:
log(Y) = 1.30 * log(X) - 7.21
R-squared = 0.77
2
n = 162
0
0.01
10
100
Maximum Vector Magnitude of PGV (cm/sec)
(c) Maximum Vector Magnitude
0
50
100
150
200
Geometric Mean PGV (cm/sec)
(d) Maximum vs. Geometric Mean
Figure 12. CI Repair Rate Regression with Geometric Mean, Maximum, and Maximum Vector
Magnitude of PGV, and Relationship Between the Maximum and Geometric Mean
PGVs
Matheson
3%
Threaded
Couplings
4%
Victaulic
14%
Mannesman Riveted
3%
1%
Victaulic
7%
Threaded
Couplings
9%
Riveted Unknown
1%
10%
Mannesman
11%
Welded
75%
Total Pipeline Length: 1018 km
Total Repairs: 205
Repair Rate
a) Steel Pipeline Length
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Welded
43%
Matheson
19%
b) Steel Pipeline Repairs
1.08
0.77
0.49
0.11
Welded
0.24
0.11
Victaulic
Threaded
Couplings
Matheson
Mannesman
Riveted
c) Steel Pipeline Repair Rate
Figure 13. Pipeline Lengths, Repairs, and Repair Rates for Various Types of Steel Pipeline in
Service During the Northridge Earthquake
Victaulic couplings are bolted, segmental, clamp-type mechanical couplings whose
housings enclose a U-shaped rubber gasket (American Water Works Association, 1964). The
gasket tends to deform and lose its initial water-tight characteristics under prolonged service.
Riveted steel pipelines are older installations, which are prone to corrosion. Contact between the
rivets and laminated steel of the pipe body promotes galvanic action between the two dissimilar
metals.
A welded slip joint is fabricated by inserting the straight end of one pipe into the bell end
of another and joining the two sections with a circumferential fillet weld. The bell end is created
by the pipe manufacturer by inserting a mandrel in one end of a straight pipe section, and
expanding the steel into a flared, or bell casing. The pie charts in Fig. 13 show that this type of
steel pipeline (refered to as welded) was the predominant type operated during the earthquake.
The histogram in Fig. 13c indicates very high repair rates associated with Matheson,
Manneson, and threaded steel pipelines. The lowest repair rates are associated with welded steel
pipelines. Because of their broader coverage and greater aggregate length, regression analyses
were performed for this type of steel pipeline.
Fig. 14a shows the repair rates for steel (Steel Distr.), CI, DI, and AC distribution lines
regressed against PGV. The regressions indicate that the highest rate of damage for a given PGV
was experienced by steel pipelines. This result at first seems surprising because steel pipelines
are substantially more ductile than CI and AC pipelines. Steel distribution pipelines in Los
Angeles, however, are used to carry the highest water pressures, installed in areas of relatively
steep slopes susceptible to landslides, and are subject to corrosion that has been shown to
intensify their damage rates in previous earthquakes (Isenberg, 1979). Comparatively high repair
rates for steel pipelines were reported by Eidinger (1998) after the 1989 Loma Prieta earthquake.
Fig. 14b compares the regression equations derived from the GIS database for the
Northridge earthquake with the default regression currently used in HAZUS (National Institute
for Building Sciences, 1997). The regression for the steel trunk lines (nominal diameter ≥ 600
mm) was developed according to similar procedures followed for the distribution pipelines. The
steel trunk lines are constructed with welded slip joints. Virtually all the regressions developed
from the Northridge GIS database plot significantly below the HAZUS default. This trend is
especially true for steel trunk lines that show repair rates 10 to 20 times lower than those
estimated with the HAZUS default.
Fig. 14c presents the linear regression that was developed between CI pipeline repair rate
and PGV on the basis of data from the Northridge and other U.S. earthquakes. By taking
advantage of additional data, this regression provides a more comprehensive representation of
repair rate trends. It is not significantly different from the regression in Fig. 12b, thereby
implying a consistency between the Northridge earthquake trends and previous earthquake
statistics.
As explained by O’Rourke and Jeon (1999), The regression relationship for the repair
rate, RR, can be expressed as
Log RR = Log K + α Log VP – β Log DP
(2)
in which K, α, and β are constants, VP is PGV, and DP is the pipe diameter in cm. Eq. 2 can be
rewritten as
RR = K (VP/DPβ/α)α
(3)
in which VP/DPβ/α is referred to as the scaled velocity that represents PGV normalized with
respect to diameter. The exponent of DP is a scaling factor that accounts for the statistical
relationship embodied in the database. This relationship is, in turn, a function of the strong
motion and pipeline system characteristics.
Fig. 14d shows the linear regression between repair rate and scaled velocity. Combining
the PGV and DP into a common parameter offers a new tool for loss estimation. Scaled velocity
combines the effects of PGV and nominal diameter, and thus accounts for the two most
important variables affecting earthquake damage to pipeline of a particular composition.
Repair Rate (Number of Repairs/km)
4
Fit Equation (Steel Distr.):
log(Y) = 0.88 * log(X) - 5.28
R-squared = 0.90
3
2
0.1009
Steel
Distr.
CI
DI
AC
8
7
6
5
4
3
2
Fit Equation (CI):
log(Y) = 1.21 * log(X) - 6.78
R-squared = 0.84
Fit Equation (DI):
log(Y) = 1.83 * log(X) - 9.39
R-squared = 0.73
Fit Equation (AC):
log(Y) = 2.26 * log(X) - 11.02
R-squared = 0.71
0.0109
8
7
6
5
4
3
2
0.001
10
2
3
4
5
CI
0.10
fffdsfd
9
8
7
6
4
Steel
Trunk
3
AC
2
0.01
100
2
10
3
4 5 6 7 89
2
100
PGV (cm/sec)
(b) Regressions vs. HAZUS Default
4
4
Fit Equation:
log(Y) = 1.55 * log(X) - 8.15
R-squared = 0.85
3
2
0.10
9
8
7
6
Repair Rate (Number of Repairs/km)
Repair Rate (Number of Repairs/km)
DI
5
6 7 8 9
(a) Steel Distr., CI, DI, and AC
aaa
5
4
3
1994 Northridge
1989 Loma Prieta
2
Steel
Distr.
2
PGV (cm/sec)
3
HAZUS
3
Repair Rate (Number of Reparis/km)
4
1987 Whittier Narrows
Fit Equation:
RR = 0.036 (VP/DP1.021)0.989
R-squared = 0.60
2
0.10
9
8
7
6
5
4
3
2
1971 San Fernando (south)
0.01
0.01
10
2
3
4
5
6 7 8 9
PGV (cm/sec)
(c) Combined CI Database
100
0.1
1.0
Scaled Velocity
10.0
VP/DP1.021, ((cm/sec)/cm1.021)
(d) Scaled Velocity
Figure 14. Regression for the Repair Rates of Various Types of Pipelines vs. PGV and Scaled
Velocity
The regressions in Fig. 14 were developed after the data were screened for lengths of
pipeline that represent approximately 1.5 to 2.5 % of the total length or population for each type
of pipe affected by the earthquake (O’Rourke and Jeon, 1999). This procedure reduces the
influence of local erratic effects that bias the data derived from small lengths of pipeline. The
use of this filtering procedure leads to statistically significant trends. The regressions are
applicable only for PGV ≤ 75 cm/s. For the Northridge earthquake, zones with PGV exceeding
75 cm/sec generally correspond to locations where PGD, from sources such as liquefaction and
landsliding, was observed. Hence, this screening technique tends to remove damage associated
with PGD, resulting in correlations relevant for TGD.
Permanent Ground Deformation Effects
Among the most notable research accomplishments in recent years is the work of
Hamada and coworkers (Hamada, et al., 1986; Hamada and O’Rourke, 1992) in the use of
stereo-pair air photos before and after an earthquake to perform photogrammetric analysis of
large ground deformation. This process has influenced the way engineers evaluate soil
displacements by providing a global view of deformation that allows patterns of distortion to be
quantified and related to geologic and topographic characteristics.
After the Northridge earthquake, pre- and post- earthquake air photo measurements in the
Van Norman Complex were analyzed as part of collaborative research between U.S. and
Japanese engineers (Sano, et al., 1999; O’Rourke, et al., 1998). Air photos taken before and after
the earthquake were acquired by U.S. team members and analyzed through advanced
photogrammetric techniques by Japanese team members. Ground movements from this initial
set of measurements were corrected for tectonic deformation to yield movements caused
principally by liquefaction and landslides.
The area near the intersection of Balboa Blvd. and Rinaldi St. has been identified as a
location of liquefaction (Holzer, et al., 1999) where significant damage to gas transmission and
water trunk lines was incurred. Ground strains were calculated in this area from the air photo
measurements of horizontal displacement by superimposing regularly spaced grids with GIS
software onto the maps of horizontal displacement and calculating the mean displacement for
each grid. Grid dimensions of 100 m x 100 m were found to provide the best results (Sano, et
al., 1999).
As illustrated in Fig. 15, ground strain contours, pipeline system, and repair locations
were combined using GIS, after which repair rates corresponding to the areas delineated by a
particular contour interval were calculated. Fig. 16 shows the repair rate contours for CI mains
superimposed on the areal distribution of ground strains, identified by various shades and tones.
In the study area, there were 34 repairs to CI water distribution mains and 2 for steel water
distribution pipelines. There were 5 water trunk line repairs in the area. The repair rate contours
were developed by dividing the map into 100 m x 100 m cells, determining the number of CI
pipeline repairs in each cell, and dividing the repairs by the length of the distribution mains in
that cell. The intervals of strain and repair rate contours are 0.001 (0.1%) and 5 repairs/km,
respectively. The zones of high tensile (+) and compressive (-) strains coincide well with the
locations of high repair rate.
Surface Analysis
Contour Interpolation
Ground Strains
Ground Strain Contours
Overlay
Length in Each Strain Range
Repair Rate
(Repairs/Length)
vs. Ground Strain
Pipeline System
Overlay
Repairs in Each Strain Range
Pipeline Repairs
Figure 15. Procedure for Calculating Repair Rate in Each Strain Range (O’Rourke, et al., 1998)
Figure 16. Distributions of CI Repair Rate and Ground Strain (O’Rourke, et al., 1998)
In Fig. 17, the relationship between the absolute values of the ground strains and repair
rates is presented graphically using linear regression. The repair rate in each ground strain range,
0-0.1, 0.1-0.3, and 0.3-0.5%, was calculated as explained previously. Ground strain contours,
obtained by both air photo measurements and surface surveys, were used. The regression
analysis shows that repair rates increase linearly with ground strain. In some instances,
anomalously high repair rates were determined. Such values do not represent the actual
distribution of damage, but are a consequence of locally high concentrations of repairs within a
given cell. The occurrence of anomalously high values depends on the cell size and positioning
of cells with respect to pipeline repair locations. It is important, therefore, to incorporate
screening procedures to filter such erroneous types of data. For example, an investigation of the
locally high data point in Fig. 17 showed that this repair rate was calculated for one particular
positioning of the grid of GIS cells and not for others. As such, this locally high repair rate was
not used in the regression analyses.
Figure 17. Correlation Between Ground Strain and CI Repair Rate (O’Rourke, et al., 1998)
Hamada and Wakamatsu (1996) showed a similar strong correlation between frequency
of pipeline repairs and ground strains evaluated by air photo measurements after the 1995 Kobe
earthquake. Seismic design codes for gas pipelines in Japan have been developed on the basis of
acceptable strain levels (Japan Gas Association, 2000). These values, in turn, can be related to
the anticipated levels of ground strain triggered by an earthquake.
Economic and Social Consequences
Advances in computer technologies, particularly GIS, have led to significant
developments in computerized methodologies for estimating expected losses from future
earthquakes (e.g., National Research Council, 1999; Earthquake Engineering Research Institute,
1997). Of special interest are recent advances in estimating the economic consequences caused
by earthquake-induced damage in lifeline systems. A multi-year, multidisciplinary research
effort has been focused on the Memphis Light, Water, and Gas (MLWG) lifeline systems in
Memphis and Shelby County, TN that are at risk from earthquakes originating in the New
Madrid Seismic Zone (Chang, et al., 2000a; Shinozuka, et al., 1998; Rose, et al., 1997, and
Chang, et al., 1996).
The methodology involves the evaluation of individual earthquake scenarios for events
with different magnitudes and distances. For each scenario earthquake, Monte Carlo simulations
are performed during which earthquake effects are modeled in the lifeline system. In the case of
the MLWG electric power system, the peak ground accelerations (PGAs) were computed at 424
different sites for a scenario earthquake, spatially interpolated, and linked with various damage
states in electric substations. Ratios of power output in the damaged system to power output in
the undamaged system were evaluated for various parts of Shelby County. Assessments were
also made of the number of days for restoration of power, as illustrated in Fig. 18a. Restoration
time is an important index with respect to the economic and social consequence of lifeline
damage. The estimated restoration times are shown for various parts of Shelby County for a 7.5
Mw earthquake at Marked Tree, AR. The times are based on the average state of repair
determined from 100 simulations.
For the MLWG water distribution system, damage states were evaluated with a hydraulic
network model specifically configured for the piping and facilities operated by MLWG. For
each earthquake scenario, 100 cases of simulated system-wide damage were produced according
to procedures similar to those followed for the electric power system. Fig. 18b shows the ratio of
water flow in the damaged system, averaged over the 100 simulations, to water flow in the
undamaged system for a 7.0 Mw earthquake at Marked Tree, AR. The water flow and associated
damage states are for the week immediately following the earthquake.
The water delivery system damage is linked with economic consequences through a
methodology that correlates water losses with areas of economic activity, adjusts for business
resiliency (remaining percentage output that can still be produced by a specific industry in the
event of total water outage), and accounts for direct and indirect economic losses. Indirect
economic loss is defined as business interruption arising from interaction among regional
industries through changes in input demands or output sales. Indirect economic losses generated
by seismic damage to lifelines were initially estimated with Input-Output (I-O) analysis (Rose, et
al., 1997), which is based on tabulated economic data pertaining to purchases (inputs) and sales
(outputs) among all sectors of the regional economy. The model is based on the assumption that
a linear relationship exists between inputs and outputs. Recent modeling employs Computable
General Equilibrium (CGE) methods to assess losses. This method incorporates nonlinear
interactions through multi-market simulation models that optimize the behavior of consumers
Number of Days
for Restoration
0–1
2–3
4–7
8 – 14
(a) Electric Power Restoration
Flow Ratio
0.00 – 0.10
0.10 – 0.20
0.20 – 0.30
0.30 – 0.40
0.40 – 0.50
0.50 – 0.60
0.60 – 0.70
0.70 – 0.80
0.80 – 0.99
0.99 – 1.00
(b) Water Losses
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(c) Analyses Zones
Figure 18. GIS and Seismic Zonation for Assessing the Economic Effects of Lifeline Losses in
Shelby County, TN (after Chang, et al., 2000a; Shinozuka, et al., 1998)
and businesses, subject to economic account balances and resource constraints (e.g., Shoven and
Whalley, 1992).
It is beyond the scope of this paper to expand on the economic models, and reference to
other publications (e.g., Chang, et al., 2000a; Shinozuka, et al, 1998) should be made for further
details. It is of interest for this paper, however, to explain briefly how lifeline damage is
spatially correlated with economic activity.
Economic data for various areas of the U.S. are summarized according to census tracts,
which are divisions of land that are designed to contain 2,500 to 8,000 inhabitants with relatively
homogeneous population characteristics, economic status, and living conditions. There are 133
census tracts in Shelby County. To provide a finer grain for economic analysis, Traffic Analysis
Zones (TAZs) were used. There are 515 TAZs for Shelby County that contain information on
local employment. Such zones represent aggregated census data developed jointly by the U.S.
Bureau of Census and U.S. Department of Transportation.
Fig. 18c shows how analysis zones were created by overlaying 515 TAZs with 971
polygons. The GIS Thiessen polygons (e.g., Burrough and McDonnell, 1998) were generated to
represent the inferred service areas for all demand nodes in the water delivery system.
Assuming that there is uniform labor distribution within each industry, the employment data for
each TAZ can be linked with economic output. The amount of economic output in each TAZ
affected by water losses was estimated as being directly proportional to the fraction of the TAZ
overlapped by a polygon. In this way the economic activity and water losses in Shelby County
were correlated in 3,400 analyses zones, thereby providing a spatial framework for the
distribution of lifeline damage effects on the regional economy.
Methodologies have been proposed for probabilistic risk analyses of lifeline systems that
are suitable for estimating both physical damage and economic consequences. Site-specific
probabilistic hazard assessments may be applied to lifelines if the lifeline system under study is
treated as a single site (e.g. Ishikawa and Kameda, 1994), or if single sites within the lifeline
system are analyzed. For a lifeline network, however, the spatial correlation among earthquake
effects at many sites is critical for determining system functionality. A procedure has been
advanced recently for developing probabilistic scenarios that account for spatial variations in
earthquake-induced damage. The procedure extends traditional risk evaluation techniques to
spatially dispersed systems (Chang, et al., 2000b), and can be used for benefit-cost analyses of
lifelines. The application of this probabilistic approach has been demonstrated by Chang, et al.
(2000b) by means of an example involving the Los Angeles highway system.
Emergency Response and Recovery
Eguchi, et al. (1997) report on how rapid damage assessment tools, based on GIS, were
used to facilitate response and recovery decisions after the Northridge earthquake. Data from
near real-time broadcasts of earthquake source information, including magnitude, epicenter and
measurements of regional peak ground accelerations, served as input parameters for a
computerized methodology referred to as Early Post-Earthquake Damage Assessment Tool
(EPEDAT). Utilizing the input parameters in conjunction with ground motion and soil
amplification models, EPEDAT estimated seismic intensities that were combined through GIS
with computerized data on buildings and lifelines to estimate damage and casualties in the
impacted area. The results were used by the California Office of Emergency Services to plan
and manage emergency response and restoration activities. Modifications of the damage and
casualty estimates were made continuously as new information was obtained and integrated into
the GIS database.
Shimizu, et al. (1996) and Shimizu (2000) report on a special system of strong motion
instruments and water level monitors deployed throughout the Tokyo Gas Company network of
pipelines and facilities. The system acquires data in virtual real time at about 3,600 locations,
including acceleration, Spectrum Intensity (SI), and indirect evidence of liquefaction through
changes in acceleration wave forms. SI is defined as the area under the pseudo-velocity response
spectra curve between T = 0.1 and 2.5 seconds. It was first introduced by Housner (1952) as a
measure of the intensity of an earthquake, and has subsequently been shown to correlate
reasonably well with gas pipeline system damage during the 1995 Kobe earthquake. On the
basis of the data acquired by the sensor system, the gas flow and pressures at district regulators
can be controlled through a central command center.
Seismic zonation can provide for the identification of vulnerable portions of lifeline
systems and help delineate potential damage scenarios and operational constraints. These
conditions constitute a “virtual” framework or baseline for planning and implementing
emergency response and applying restoration strategies for system recovery. Given the ability to
acquire and process information in real time, real information about post-earthquake damage can
be compared rapidly with initial zonation scenarios. Improvements in system characterization
can be implemented on an incremental basis as additional information is obtained. Kakumoto, et
al. (2000) discuss various options that are available for collecting and wharehousing preearthquake information, and for updating pre-earthquake data with post-earthquake observations.
If GIS is used as the vehicle for visualizing and communicating post-earthquake damage
and restoration progress, then seismic zonation can be viewed as a dynamic, virtual real-time
process for tracking and managing the evolution of spatial changes in critical systems. Recent
advances in remote sensing technologies have been discussed by Eguchi, et al. (2000). Satellite
imagery, global positioning systems, and synthetic aperture radar are among the promising
technologies for rapid data acquisition and system characterization after an earthquake.
Concluding Remarks
In concluding this paper, it is appropriate to address both the limitations and opportunities
associated with seismic zonation. A brief discussion of limitations and opportunities is provided
under the subheadings that follow.
Limitations
As described previously, one of the most significant difficulties in characterizing seismic
hazards for lifelines, especially buried lifelines, is the identification of PGD and TGD zones, and
the characterization of TGD effects in areas where constructive interference of waveforms and
ground strain concentrations may occur. TGD in the form of surface waves in sedimentary
basins, vibration of relatively narrow soil-filled valleys, and liquefaction-induced ground
oscillation may induce locally high ground strains, especially near basin and valley margins
where soft sediments impinge on less deformable geologic media. In all PGD zones, there are
some TGD effects. As a consequence, PGD and TGD effects tend to merge, making a clear
distinction between the two very difficult at some locations.
To resolve accurately the locations and areal coverage of PGD zones, sufficient data are
required to characterize the subsurface deposits and delineate the potential boundaries of
movement. The spatial variability of soil, surface gradients, subsurface geometry of deposits,
and groundwater coupled with sparse information at most locations poses a substantial challenge
for the seismic zonation of lifelines. Fortunately, increased use of GIS and computerized
databases make it possible to collect and store large amounts of information for a particular
region. As site explorations are performed in the future, the ensuing information can be added to
existing databases to improve subsurface characterization.
Regression analyses for predicting pipeline damage and liquefaction-induced PGD are
performed by assuming that seismic and subsurface parameters are known without error. For
example, when the pipeline repair rate is regressed against PGV, it is assumed as a premise of
the regression analysis that PGV is known precisely. A similar assumption applies to the
thickness of liquefiable soil in MLR analysis of liquefaction-induced horizontal movement.
It should be recognized that procedures are available to account for the variability of the
different parameters in regression analyses (e.g., Snedecor and Cochran, 1980). Moreover,
methods, such as kriging (e.g., Ripley, 1987), inherently account for spatial variability, and can
be adapted to delineate confidence limits and probabilities of exceedance for a particular
parameter in two-dimensional space. In the future, it will be advantageous to explore the
variability of all regression parameters to reflect properly the spatial uncertainties of seismic and
site conditions.
Lifeline systems are composed of diverse components and facilities that may differ from
one system to another. To apply regressions or correlations that were developed with emphasis
on a particular system, one must be satisfied that the characteristics of that system are
representative of others for which the predictions are being made. The statistics of pipeline
performance from GIS investigations of water distribution systems after the Northridge and
Kobe earthquakes show several consistent trends. For both earthquakes, water main damage was
inversely proportional to nominal diameter, directly proportional to PGA, and the ratios of repair
rates for DI and CI pipelines are comparable for smaller diameter lines.
For steel distribution pipelines, however, the repair rates are significantly different. The
repair rates for steel distribution pipelines after the Northridge earthquake are higher than those
of CI pipelines, whereas the opposite trend applies for the Kobe earthquake. The relatively high
susceptibility of steel piping to damage during the Northridge earthquake may be explained in
part by utility practices, such as using steel pipe for the highest internal pressures and deploying
steel pipe in areas with relatively steep slopes. Increased susceptibility to corrosion also appears
to play a role in steel pipeline performance.
Pipeline system complexities apply to operational strategies and company procedures. It
is necessary, therefore, to be aware that practices can vary among different utilities and that local
differences may affect the suitability of empirically-based predictions.
Opportunities
Advances in seismic zonation for lifelines and utilities have been influenced to a very
significant extent by records of lifeline performance acquired in recent earthquakes, most notably
the 1989 Loma Prieta, 1994 Northridge, and 1995 Kobe earthquakes. Data from the Northridge
and Kobe earthquakes have been compiled in GIS of unprecedented size and complexity that
allow for a detailed examination of the spatial relationships among lifeline damage, permanent
and transient ground deformation, and subsurface soil and groundwater conditions.
Pipeline damage data from the Northridge earthquake are useful not only for developing
predictive regressions for different types of piping, but for identifying areas of seismic hazards.
When compared with default predictions currently embodied in the National Loss Estimation
Methodology, the regressions presented in this paper show substantially lower repair rates.
Damage statistics from recent earthquakes and improvements in GIS technology provide the
opportunity for comprehensive assessment of system performance and the characterization of
spatial variability at a level of detail previously unattainable.
One of the most significant trends in seismic zonation for lifelines and utilities is the
development of loss modeling techniques that link lifeline disruption with the economic
consequences of such damage in a given region. Research focused on water and electric power
systems in Shelby County, TN has resulted in techniques for spatially correlating lifeline damage
with economic losses. Analytical procedures, such Input-Output analysis and Computable
General Equilibrium, have been applied successfully to estimate economic losses from
earthquake damage to lifelines. This approach is well suited for benefit/cost assessments to
evaluate the most appropriate seismic retrofitting and policy initiatives. Ultimately, losses can
be characterized by economic fragility curves that indicate the likelihood of exceeding different
levels of economic loss, with and without mitigation measures. The resulting information will
assist lifeline and utility agencies in deciding on suitable mitigation procedures.
Acknowledgements
The work presented in this paper is the result of research sponsored by the
Multidisciplinary Center for Earthquake Engineering Research, Buffalo, NY; the Institute for
Civil Infrastructure Systems, New York, NY; and the National Science Foundation. The authors
gratefully acknowledge the assistance of the Los Angeles Department of Water and Power, M.
Shinozuka, C. Chang, J. P. Bardet, and S. Toprak in acquiring information presented herein.
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