PLEASE SCROLL DOWN FOR ARTICLE
This article was downloaded by: [Georgia Technology Library]On: 9 September 2010Access details: Access Details: [subscription number 918551305]Publisher Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Journal of Earthquake EngineeringPublication details, including instructions for authors and subscription information:http://www.informaworld.com/smpp/title~content=t741771161
Regional Seismic Risk Assessment of Bridge Network in Charleston, SouthCarolinaJamie E. Padgetta; Reginald DesRochesb; Emily Nilssonc
a Department of Civil & Environmental Engineering, Rice University, Houston, Texas, USA b School ofCivil & Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA c DatumEngineers, Austin, Texas, USA
Online publication date: 08 July 2010
To cite this Article Padgett, Jamie E. , DesRoches, Reginald and Nilsson, Emily(2010) 'Regional Seismic Risk Assessment ofBridge Network in Charleston, South Carolina', Journal of Earthquake Engineering, 14: 6, 918 — 933To link to this Article: DOI: 10.1080/13632460903447766URL: http://dx.doi.org/10.1080/13632460903447766
Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf
This article may be used for research, teaching and private study purposes. Any substantial orsystematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply ordistribution in any form to anyone is expressly forbidden.
The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae and drug dosesshould be independently verified with primary sources. The publisher shall not be liable for any loss,actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directlyor indirectly in connection with or arising out of the use of this material.
Journal of Earthquake Engineering, 14:918–933, 2010
Copyright � A.S. Elnashai & N.N. Ambraseys
ISSN: 1363-2469 print / 1559-808X online
DOI: 10.1080/13632460903447766
Regional Seismic Risk Assessment of BridgeNetwork in Charleston, South Carolina
JAMIE E. PADGETT1, REGINALD DESROCHES2,and EMILY NILSSON3
1Department of Civil & Environmental Engineering, Rice University, Houston,
Texas, USA2School of Civil & Environmental Engineering, Georgia Institute of Technology,
Atlanta, Georgia, USA3Datum Engineers, Austin, Texas, USA
This article presents the results of a seismic risk assessment of the bridge network in Charleston,South Carolina and the surrounding counties to support emergency planning efforts, and forprioritization of bridge retrofit. This study includes an inventory analysis of the approximately375 bridges in the Charleston area, and convolution of the seismic hazard with fragility curvesanalytically derived for classes of bridges common to this part of the country. State-of-the-artbridge fragility curves and replacement cost estimates based on region-specific data are used toobtain economic loss estimates. The distribution of potential bridge damage and economic lossesare evaluated for several scenario events in order to aid in the identification of emergency routesand assess areas for investment in retrofit. This article also evaluates the effect of uncertainty onthe resulting predicted economic losses. The findings reveal that while the risk assessment is verysensitive to both the assumed fragility curves and damage ratios, the estimate of total expectedeconomic losses is more sensitive to the vast differences in damage ratio models considered.
Keywords Seismic Risk Assessment; Loss Estimation; Bridges; Fragility; Transportation Net-work; Sensitivity Study
1. Introduction
Regional seismic risk assessments (SRAs) are becoming popular tools for evaluating the
performance of transportation networks under earthquake loading. The term seismic risk
refers to the potential for damage or losses that may be associated with a seismic event.
Such regional assessments provide a unique approach for estimating the risk to highway
infrastructure by evaluating potential bridge damage and consequences of the seismic
event, such as the estimated direct and indirect losses. This framework offers support to
decision-makers for pre-event planning and risk mitigation, emergency route identifica-
tion, retrofit selection and prioritization, among other critical tasks.
Methodologies for seismic risk assessment of transportation systems have been pre-
sented by many researchers in the field of lifeline earthquake engineering [Kiremidjian, et
al., 2007; Shinozuka et al., 1997; Luna et al., 2008; Werner et al., 2000]. These methodol-
ogies offer a potential framework for assessing likely bridge damage, direct losses due to
repair and replacement of the structures, and some extend this evaluation to include an
Received 23 March 2009; accepted 28 October 2009.
Address correspondence to Jamie E. Padgett, Department of Civil & Environmental Engineering, Rice
University, 6100 Main Street, MS 318, Houston, TX 77005, USA; E-mail: [email protected]
918
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
assessment of the impact of the event on network performance and the resulting indirect
economic losses [Kircher et al., 2006; Werner et al., 1997.]
In this article, a detailed seismic risk assessment of the bridge network in Charleston,
South Carolina is conducted. The assessment is performed for a range of hazard levels,
for an inventory of approximately 375 bridges. The seismic risk assessment uses bridge
fragility curves that represent the unique characteristics of bridges in the region, as well
as state-specific bridge repair and replacement cost data. Distribution of damage and loss
estimates are tabulated for the different hazard levels. There are numerous uncertainties
associated with the seismic risk assessment process, and the resulting damage and loss
estimation. The second half of the article will assess the effect of uncertainty on the
resulting bridge damage distribution and estimated losses in Charleston, South Carolina.
2. Risk Assessment Framework and Input Models
The seismic risk assessment framework previously proposed by researchers varies in the
extent to which hazards, damage, and losses are treated. However, the general methodolo-
gies have common threads, as was highlighted in Werner et al. [2000]. The risk assessment
approach in this study is limited to an assessment of the bridge damage due to ground
shaking, and considers only the economic losses due to physical damage, rather than indirect
losses due to operation losses or time delay in the transportation system. While these losses
are significant considerations for evaluating the consequences of an earthquake event, the
objective of the study is to assess the sensitivity of the estimated bridge damage and repair
costs to input model variation. Seismic risk assessments are sometimes classified as deter-
ministic or probabilistic, in reference to the hazard itself. Probabilistic analysis is often
carried out by developing loss estimation for multiple simulations and scenario earthquakes,
then aggregating their results. While an SRA may be deterministic in terms of assessing a
specific scenario event, the potential uncertainty in achieving different levels of damage,
economic losses, or other consequences may still be treated probabilistically in the analysis.
The general seismic risk assessment framework used in this study is presented in Fig. 1.
As illustrated in Fig. 1, the first phase of the SRA process for bridge networks is to
initialize the process and define the problem by identifying the characteristics and locations
of the bridge inventory. The bridge inventory is obtained from the National Bridge
Inventory, with supplementary data provided by the South Carolina Department of
Transportation. Scenario earthquake events are used for the example presented herein,
where the magnitude and location of the event must be specified. During the system
analysis, fragility curves for classes of bridges common to the region are utilized. These
fragility curves depict the probability of meeting or exceeding different levels of damage
conditioned upon the ground motion intensity. Thus, the level of ground shaking at the
location of each bridge in the spatially distributed region must be estimated. This facilitates
evaluation of the expected level of damage to each bridge. The bridge damage coupled with
information on the damage ratio (or fraction of replacement cost) and replacement cost data
for different bridge types permits an assessment of the losses. The following sections detail
the different input models and scenarios which will be evaluated as a part of this study.
3. Case Study
3.1. Region of Interest
Charleston, South Carolina (Fig. 2) is located in the southeast United States. Charleston
has a history of large, but infrequent earthquakes. On August 31, 1886, a large earthquake
Risk to Charleston Bridges 919
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
(approximate magnitude of 7.0) struck the Charleston region. The earthquake resulted in
60 casualties, and widespread destruction of the built environment in Charleston
[Bollinger, 1977]. The earthquake was felt over a wide area, ranging from Milwaukee,
EarthquakeScenario
Bridge and RoadwayInventory
(characteristics, location, etc.)
Estimation of GroundShaking
at Bridge Locations
Fragility Curves forBridge Classes
Bridge DamageState Evaluation
Bridge Repair Cost Ratios(fraction of replacement cost)Replacement Cost Data
Seismic Performance andConsequence Assessment
(Damage Summary,Direct Losses, etc.)
FIGURE 1 General flow chart for seismic risk assessment of bridge network.
FIGURE 2 Case study region in Charleston, South Carolina.
920 J. E. Padgett, R. DesRoches, and E. Nilsson
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
Wisconsin to Boston, Massachusetts. Summerville, South Carolina, located to the north-
west of Charleston, was subjected to extremely large ground shaking, resulting in the
collapse of many homes and widespread foundation settlement. A repeat of the 1886
earthquake could have a devastating effect on the Charleston region, as well as the local
and global economy.
3.2. Bridge Inventory
Using the National Bridge Inventory (NBI) data for the state of South Carolina [FHWA,
2005], bridges were first filtered by county and bridge identification number to limit the
case study evaluation to the region of interest in Charleston, South Carolina. All of the
bridges in Charleston County, and a select few from Berkeley, Dorchester, and
Orangeburg counties, were filtered out using Microsoft Access. The select additions
include the bridges on the I-26 corridor, along I-26 from Charleston to the Bowman
exit, as well as bridges along US 17 from Beaufort, Colleton, Georgetown, Horry, and
Jasper counties. This yielded in a revised inventory containing 375 bridges out of the
overall 10,000 in the state.
The bridges studied in the Charleston region are classified with the methodology
used by Nielson [2005], according to material and construction type. The classifications
simply identify the bridges by both their span configuration—simply supported (SS),
multi-span simply supported (MSSS), multi-span continuous (MSC)—as well as by their
girder material type—concrete or steel. An overall distribution of the bridge classes is
shown in Table 1. The ‘‘Other’’ bridge category contains all additional bridges not falling
into one of the ten major classifications (i.e., truss, moveable, segmented box girder, and
box single/spread).
3.3. Seismic Hazard
One of the first steps in evaluating the seismic risk for any region is to assess the seismic
hazard or identify the events of interest. In this study, three deterministic scenarios are
used selected based on recommendations from SCDOT: earthquakes of magnitude Mw
4.0, 5.5, and 7.0 located at 32.9� N, 80.0� W, which is approximately 14.5 km outside of
TABLE 1 Distribution of bridge classes within the study area
Bridge type Quantity Percent
MSC_Concrete 1 0.27%
MSC_Steel 31 8.27%
MSC_Slab 14 3.73%
MSC_Conc Box 6 1.60%
MSSS_Concrete 61 16.27%
MSSS_Steel 62 16.53%
MSSS_Slab 118 31.47%
MSSS_Conc Box 2 0.53%
SS_Steel 26 6.93%
SS_Concrete 19 5.07%
Other 35 9.33%
Total 375 100.00%
Risk to Charleston Bridges 921
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
the Charleston city center near Summerville, South Carolina. These hazards produce
maximum ground motion intensities of 0.28 and 0.62 g peak ground acceleration for Mw
4.0 and Mw 7.0, respectively, as shown in Fig. 3.
3.4. Input Models and Risk Assessment
Key input to the risk assessment, as previously indicated, include bridge fragility curves
and repair models. Bridge fragility curves offer the probability of meeting or exceeding a
level of damage given an intensity measure of the ground motion. For this study, the
levels of damage are qualitatively described as slight, moderate, extensive, and complete
damage. Each damage state is associated with an anticipated level of post-event function-
ality, as further discussed in Padgett and DesRoches [2007]. A brief description of the
damage states is presented in Table 2, corresponding to the fragility models incorporated.
The fragility curves adopted are those developed by Nielson and DesRoches [2007].
These fragility curves were developed specifically for nine bridge classes common to the
Central and Southeastern U.S. (CSUS) and are representative of the bridge inventory in
the Charleston region. Uncertainty in component stiffnesses, material strengths, and
geometry were propagated through the analysis. The fragility development considered
damage to multiple vulnerable components, including bearings, columns, and abutments
in the longitudinal and transverse directions. The CSUS fragility curves were developed
for evaluation of the vulnerability of general classes of bridges across a region rather than
bridge specific analysis, and are used in this study to evaluate the probability of the
bridges experiencing different levels of damage in Charleston and subsequent regional
loss estimation. Stochastic dependence between bridge failures in the spatially distributed
region is not considered in the present study. While likelihood of achieving each level of
damage is evaluated for all bridges in the region, the mean value of the damage state is
often presented graphically.
Repair cost models are also required for estimating direct losses due to repair and
replacement of the seismically damaged bridges. Bridge repair costs are assessed as a
fraction of the replacement cost using the damage ratios, D, presented by Basoz and
Mander [1999], as listed in Table 2. The normalized replacement costs for various bridge
types using historic, region specific construction data in South Carolina are show in Table 2,
as a replacement cost per area of bridge deck.
The damage and loss estimates are evaluated and aggregated for the Charleston
region using the seismic risk assessment package, MAEViz [MAEC, 2006]. Within this
FIGURE 3 Comparison of hazard for deterministic scenarios: (a) Mw 5.3 and (b) Mw 7.3.
922 J. E. Padgett, R. DesRoches, and E. Nilsson
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
framework, the damage state is determined from a mean damage ratio, mD, found as
follows:
�D ¼X4
j¼1
DjP DSj
� �; (1)
where j is the damage state, Dj is the damage ratio for damage state j, and P[DSj] is the
probability of damage state j from the difference in damage state exceedance probabilities
evaluated by entering the fragility curves at the site pga. Given the mean damage ratio, an
expected damage state is presented graphically for intermediate visual inspection. Addi-
tionally, the mean value of the losses for the bridges in the region is found in MAEViz as:
�L ¼X
n
Cn�Dn; (2)
where n is the number of bridges in the region, mDn is the mean damage ratio for bridge n,
Cn is the cost to repair the bridge computed as a function of the deck area and replace-
ment cost shown in Table 3. The replacement cost data shown in Table 3, given in dollars
per deck area, reflects the average cost of new construction in South Carolina for different
TABLE 2 Damage state definitions [Padgett and DesRoches, 2007] and damage ratios
[Basoz and Mander, 1999]
Damage
state
Damage state definition
[Padgett and DesRoches, 2007] Damage ratios [Basoz and Mander, 1999]
Functionality description
Best mean damage
ratio (D)
Range of damage
ratio
None No reduction in functionality 0.005 0–0.01
Slight Fully functional within a day 0.03 0.01–0.03
Moderate Reduced functionality for a week 0.08 0.02–0.15
Extensive Closed for a week, with partial
functionality beyond 30 days
0.25 0.1–0.4
Complete Complete closure beyond 30 days 1.0 (if n < 3) 0.3–1.0
2.0/n (if n � 3)
n = number of spans.
TABLE 3 Bridge replacement cost data based on South Carolina
statistics [SCDOT, 2007] in dollars per area of bridge deck
Type Cost ($/ft2)
Concrete Girder 67.71
Concrete Box Girder 67.98
Steel Girder 94.37
Slab 60.04
Other (truss, moveable, etc.) 72.53
Risk to Charleston Bridges 923
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
bridge types per recent construction data [SCDOT, 2007]. Additionally the standard
deviation of the losses is found as:
�L ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiX
n
Cn�Dnð Þ2r
; (3)
where the sD, the standard deviation of the damage ratio for each bridge is:
�D ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiX4
j¼1
Dj � �D
� �2P DSj
� �vuut : (4)
The input models and loss estimate approach presented above are subsequently used in
the case study risk assessment of the 375-bridge network in Charleston.
4. Results: Magnitute 5.5 Earthquake Event
4.1. Bridge Damage
The risk assessment is conducted for the Charleston case study to evaluate expected
damage and total direct losses for different scenario events. Figure 4 illustrates the
distribution of bridge damage in the downtown Charleston region due to the Mw 5.5
earthquake event. These types of maps of the anticipated spatial distribution of bridge
damage can be beneficial not only for assessing economic losses, as emphasized in this
Damage States
None Slight Moderate Extensive Complete
Damage States
None Slight Moderate Extensive Complete
FIGURE 4 Spatial distribution of damaged bridges in downtown Charleston for the Mw
5.5 event.
924 J. E. Padgett, R. DesRoches, and E. Nilsson
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
article, but can support the identification of viable emergency response routes and
identification of bridges in need of potential retrofit. While a majority of the damaged
bridges in this region may be expected to experience moderate damage, a limited number
of bridges are in the extensive damage state. A summary of the bridges by type and
damage state is shown in Table 4. The anticipated level of damage is a function of the
ground motion at the bridge site, as well as the relative vulnerability of the bridge. For
example, the MSC and MSSS Steel bridges have fragility models that reveal they are
among the most vulnerable bridge types in the region, and the results of the risk
assessment also indicate that the extensively damaged bridges are of these types. It is
also clear that there are a larger number of bridges in the higher damage state in the
location closer to the epicenter of the earthquake.
4.2. Economic Losses
The calculation of expected economic losses is based on the potential damage states and the
repair and construction data from the state of South Carolina, as described in the previous
section. For the Mw 5.5 event, the direct economic losses are approximately $40 million
(Table 5). It is interesting to note that one bridge type alone (MSC steel girder bridge)
accounts for over 64% of the total direct economic losses. This is due to several factors.
Although the MSC Steel girder bridge only accounts for less than 10% of the bridges, it
accounts for 63% of the bridges in the extensive damage state. The economic losses
associated with the extensive damage state are considerably higher than those in the
lower damage states. A bridge in the extensive damage state would have a repair cost
ratio that is three times as high as the moderate damage state, and eight times as high as the
slight damage state. The other reason for the large losses in the MSC steel bridge are due to
the fact that this bridge type tends to have longer bridge lengths and widths as compared to
the other bridge types, as well as the fact that the normalized cost to repair or replace the
steel bridges tends to be higher than other bridge classes. Since the total loss is proportional
to the area, this bridge type tends to have higher loss values. It is also observed that the
bridges that are more robust (i.e., SS steel, SS concrete, MSC concrete box) also contribute
TABLE 4 Distribution of bridges by damage state and bridge type for the Mw 5.5 event
Type
Damage state
TOTALNone Slight Moderate Extensive Complete
MSC Concrete 0 0 1 0 0 1
MSC Steel 12 0 12 7 0 31
MSC Slab 1 2 11 0 0 14
MSC Conc Box 0 1 5 0 0 6
MSSS Concrete 23 8 30 0 0 61
MSSS Steel 25 1 32 4 0 62
MSSS Slab 25 22 71 0 0 118
MSSS Conc Box 0 2 0 0 0 2
SS Concrete 6 6 7 0 0 19
SS Steel 26 0 0 0 0 26
Other 30 3 2 0 0 35
TOTAL 148 45 171 11 0 375
Risk to Charleston Bridges 925
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
less to the total direct losses. The relative contribution of bridges to the loss estimate offers
one approach to help identify and prioritize bridges in need of retrofit.
5. Results: Comparison of Different Earthquake Magnitudes
The seismic risk assessment was performed for three different hazards, Mw 4.0, 5.5, and
7.0 (epicenter in Summerville, South Carolina), using the MAEViz platform [MAEC,
2006]. The distribution of expected damage for the three hazard levels is shown in Fig. 5.
TABLE 5 Summary of direct losses by bridge type for the
Mw 5.5 event
Type Direct losses
MSC Concrete $13,000
MSC Steel $26,000,000
MSC Slab $830,000
MSC Conc Box $200,000
MSSS Concrete $2,800,000
MSSS Steel $6,000,000
MSSS Slab $1,400,000
MSSS Conc Box $60,000
SS Concrete $510,000
SS Steel $94,000
Other $2,400,000
TOTAL $40,307,000
FIGURE 5 Distribution of damage as a function of earthquake magnitude.
926 J. E. Padgett, R. DesRoches, and E. Nilsson
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
The results show that for the Mw 7.0 event, over 85% of the bridges are damaged, with
73% of the bridges having moderate to complete damage. For the Mw 5.5 event,
approximately 60% of the bridges are damaged, with nearly 50% of the bridges having
moderate to complete damage. Finally, the Mw 4.0 earthquake results in only 17% of the
bridges having damage, and less than 9% have moderate or greater damage.
It is interesting to note that a Mw 4.0 scenario results in expected damage states of
only slight or moderate damage to 65 bridges, with the remaining bridges having no
damage. This is an indication that for pre-event planning purposes the Mw 4.0 earthquake
might be a viable threshold upon which inspection teams are mobilized following an
earthquake event. However, this would depend on the location of the epicenter for the
particular earthquake. It is also important to note that as previously highlighted in the
input model and risk assessment section, while expected damage states are presented
graphically there is probability of achieving each damage state even at the lower level
events, which is further propagated through the loss estimation.
As shown in Fig. 6, for a Mw 4.0 seismic event, direct economic losses are estimated
to be close to $6.3 million. In contrast, the more severe earthquake scenario, Mw 7.0,
produces direct losses of approximately $90 million. As the earthquake scenarios increase
in intensity, the direct economic losses increase exponentially, and the error about that
estimate increases as well. While outside of the scope of the current study, indirect losses
in a transportation network due to bridge damage are often orders of magnitude greater
than the repair and replacement costs alone. For example, past studies have shown that
the indirect losses due to rerouting may be roughly 7–20 times direct losses [ATC, 1991],
revealing that for an increase of 13 times, the total losses in the Charleston region may be
on the order of $90 million to over $1 billion for the Mw 4.0 and 7.0, respectively.
Refined total loss estimates would require transportation modeling, which is outside of
the scope of this study.
6. Uncertainties and Sensitivity Study
While there have been many studies that propose and illustrate the viability of the risk
assessment framework, the results may depend heavily on the availability and reliability
of utilized tools and input models. These include such items as ground motion models,
4.00
20 × 106
40 × 106
60 × 106
80 × 106
100 × 106
120 × 106
5.5Earthquake Scenario (Mw)
Los
s ($
)
7.0
FIGURE 6 Direct economic loss estimates for three scenario earthquakes (Mw 4.0, 5.5,
and 7.0).
Risk to Charleston Bridges 927
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
fragility information on the bridge vulnerability, repair cost information, among others.
Different modeling assumptions and input tools may be classified as epistemic uncertain-
ties. An epistemic uncertainty is often defined a knowledge-based uncertainty, which
stems from incomplete data, ignorance, or modeling assumptions. The adoption of
different input models in the SRA framework could potentially have a significant effect
on the overall results and conclusions of the study.
Past studies have evaluated the sensitivity of loss estimates to input model variation
in other systems, particularly buildings. Crowley et al. [2005] assessed the impact of a
number of uncertain parameters, including ground motion modeling, structural demand,
and capacity estimates, on regional building damage. Porter et al. [2002] evaluated the
sensitivity of loss estimates for a single concrete moment-frame building and found, like
Crowley observed for regional damage, that the building capacity (limit at which damage
is expected) was the most important uncertain parameter followed by ground motion
characteristics. In a study assessing the average annual losses to a regional inventory of
low-rise wood framed buildings, Grossi [2000] compared using default models in
HAZUS to ‘‘updated’’ input models for the seismic hazard as well as the inventory
square footage and fragility. She found that models defining the seismic hazard, such
as the recurrence model for the earthquake and attenuation relationship were the most
critical updates, followed by the square footage and fragility.
While the studies listed have offered insight on the relative importance of different
loss modeling parameters for building inventories, few have assessed the impact on the
regional seismic risk to transportation networks. The relative sensitivity of the highway
bridge damage and loss estimates to different input models are evaluated as a follow-up
phase of the study in Charleston. This helps to identify critical components of the risk
assessment framework that significantly impact the overall results of a regional transpor-
tation network assessment, including bridge damage and direct economic losses due to
repair and replacement. This study emphasizes the difference due to assumed input
models, rather than variation about the estimate due to uncertainty modeled by a
particular input model. The Charleston region previously presented is used as an example
to gain insight on the effect of different input fragility curves for evaluating the perfor-
mance of bridges common to the region, as well as different estimates of the damage ratio
for repair cost modeling and loss estimation.
7. Input Parameters
Two different scenario earthquake events are considered as a part of the sensitivity study.
This permits an evaluation of whether or not the conclusions of the study are dependent
upon the level of the hazard. The characteristic scenario events assessed for Charleston
are moment magnitude 5.3 and 7.3 located 14.5 km outside of the city center near
Summerville. In order to estimate the level of ground shaking at the location of each
bridge, a weighted average of different attenuation functions is used [MAEC, 2006]. This
is to acknowledge the findings of past work which has indicated the importance of
considering the epistemic uncertainty in ground motion models, particularly attenuation
of ground motion for spatially distributed systems. Thus, the ground motions models
themselves are not a focus of this study and the epistemic uncertainty associated with
them is captured and treated explicitly in each scenario, rather than evaluating the
sensitivity of the results to different models.
The two input models that are considered in this study are change in fragility model
and in repair cost model (specifically due to change in damage ratio). The fragility
models considered in the sensitivity study for bridge classes common to the Charleston
928 J. E. Padgett, R. DesRoches, and E. Nilsson
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
region include the Nielson and DesRoches [2007] fragility curves developed for the
CSUS region as previously discussed in the case study, as well as those adopted in
HAZUS-MH [FEMA, 2005]. The bridge fragility curves currently used in HAZUS-MH
were developed using a nonlinear static approach in past work by Basoz and Mander
[1999] and Dutta [1999]. These sets of fragility models are subsequently termed CSUS
and HAZUS fragilities, respectively. A detailed discussion of the difference in the two
models is presented in Nielson and DesRoches [2007], which illustrated that for some
bridge types (i.e., multi-span simply supported steel or concrete girder bridges) the CSUS
fragility curves exhibit lower vulnerability than originally anticipated in the HAZUS
curves, while for other bridge types (i.e., multi-span continuous steel and concrete girder
bridges) the CSUS fragility curves indicate a much higher vulnerability than depicted in
the HAZUS curves.
The two damage ratios considered in the sensitivity study are those formerly pre-
sented in the case study [Basoz and Mander, 1999] termed Basoz, as well as the damage
ratios presented in REDARS [Werner et al., 2006] as shown in Table 6. Figure 7 shows a
comparison of the damage ratios for an example bridge with three spans, noting that the
Basoz damage ratios are a function of the number of spans, while the REDARS damage
ratios do not change depending upon number of spans. As illustrated in the plot, the
REDARS damage ratios imply a larger anticipated repair cost for the moderate, exten-
sive, and complete damage states in particular. Moreover, they indicate a more linearly
increasing damage ratio than exhibited in the Basoz damage ratios.
TABLE 6 REDARS repair cost ratios [Werner et al., 2006]
Damage state Best mean damage ratio (D) Range of damage ratio
None 0.00 0–0.01
Slight 0.03 0.01–0.05
Moderate 0.25 0.05–0.5
Extensive 0.75 0.5–0.8
Complete 1.00 0.8–1.0
None0.0
0.2
0.4
0.6
0.8
1.0
Slight
BasozREDARS(for n = 3)
ModerateDamage state
Dam
age
Rat
io
Extensive Complete
FIGURE 7 Comparison of Basoz and REDARS damage ratios for a three span bridge.
Risk to Charleston Bridges 929
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
8. Results
The sensitivity study is performed by conducting the regional risk assessment for
Charleston with different input models. The experiment conducted is a full factorial
design with each factor (fragility curves and damage ratios) having two categorical levels
(22), and a replication to consider two different levels of earthquake (2 x 22), for a total of
8 runs. Table 7 lists the risk assessment runs (scenarios) for the magnitude 5.3 and 7.3
events. The total estimated direct losses and standard deviation of the losses are compared
in the Table, indicating a potential range in estimated direct losses between $71,400,000
and $267,000,000 for the upper level event, and between $27,900,000 and $125,000,000
for the lower level event for different input model combinations. Similarly, the standard
deviation about those loss estimates varies for each scenario.
Figure 8 shows the percent difference in the mean value and standard deviation of the
losses relative to the base case (CSUS fragility curves and Basoz damage ratios). It is
noted that the base case uses the same input models considered in the Charleston case
study previously presented. This figure reveals that regardless of event magnitude, the use
of the REDARS damage ratios results in larger economic losses, as anticipated, due to the
increase in damage ratio and repair cost estimate for each damage state. The expected
value of total losses increases by nearly 150% for each earthquake level when the same
fragility curves are used as the base case (CSUS). In fact, the change in damage ratios
results in the largest impact on the loss estimate and standard deviation about that
estimate.
The use of HAZUS fragility curves results in a decrease in expected direct economic
losses for a given damage ratio. This finding is potentially counter-intuitive given the
total number of bridges expected in each damage state shown for each run in Fig. 9 for
Mw 5.3 and 7.3. As these figures reveal, the use of the HAZUS fragility curves as
opposed to the CSUS fragilities for the same damage ratio (Basoz) result in a larger
TABLE 7 SRA runs for sensitivity study and results
Run number Scenario
Estimated total
direct losses
Standard
deviation
7.3 Base Case 7.3, CSUS Fragilities,
Basoz Damage Ratios
$105,000,000 $19,900,000
7.3.A 7.3, HAZUS Fragilities,
Basoz Damage Ratios
$71,400,000 $17,700,000
7.3.B 7.3, HAZUS Fragilities,
REDARS Damage Ratios
$197,000,000 $27,500,000
7.3.C 7.3, CSUS Fragilities,
REDARS Damage Ratios
$267,000,000 $41,200,000
5.3 Base Case 5.3, CSUS Fragilities,
Basoz Damage Ratios
$50,900,000 $14,700,000
5.3.A 5.3, HAZUS Fragilities,
Basoz Damage Ratios
$27,900,000 $11,500,000
5.3.B 5.3, HAZUS Fragilities,
REDARS Damage Ratios
$74,200,000 $10,700,000
5.3.C 5.3, CSUS Fragilities,
REDARS Damage Ratios
$125,000,000 $24,900,000
930 J. E. Padgett, R. DesRoches, and E. Nilsson
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
number of bridges in the extensive and complete damage states; however, the expected
value of the losses is lower for the HAZUS fragility curves. This can be attributed to the
fact that: (1) The HAZUS fragility curves have been shown to underestimate the damage
of MSC bridges [Nielson and DesRoches, 2007], which are among the costliest bridges to
repair and replace and the bridges contributing the most to the economic losses (i.e.,
Tables 2 and 4); and (2) Damage to other bridge types, such as the MSSS concrete girder,
slab, and steel girder bridges, may be overestimated by using the HAZUS fragilities,
yielding more total bridges in the upper damage states, yet with insignificant net effect on
the direct losses relative to the contribution of other bridges.
Figure 8 also indicates the interaction effects of changing both the fragility curves
and damage ratios for a given earthquake scenario. The reduction in expected value of
losses due to using HAZUS fragility curves is countered and dominated by the increase in
% Variation from Base Model−100 −50 0 50 100 150 200
E[Loss] stdev[Loss]
CSUS Fragilities, REDARS Damage Ratios
HAZUS Fragilities, REDARS Damage Ratios
CSUS Fragilities, REDARS Damage Ratios
HAZUS Fragilities, Basoz Damage Ratios
HAZUS Fragilities, REDARS Damage Ratios
HAZUS Fragilities, Basoz Damage Ratios
Hig
h L
evel
Eve
nt(M
w 7
.3)
Low
Lev
el E
vent
(Mw
5.3
)
FIGURE 8 Comparison of the change in expected value of losses and standard deviation
of losses relative to the base case (CSUS fragilities, Basoz damage ratios).
(a)
None0
50
100
150
200
250
Slight Moderate
Damage State
Num
ber
of B
ridg
es
Extensive Complete
Mw = 5.3
CSUS/BasozCSUS/REDARSHAZUS/BasozHAZUS/REDARS
Mw = 7.3
CSUS/BasozCSUS/REDARSHAZUS/BasozHAZUS/REDARS
(b)
None0
50
100
200
150
250
300
Slight Moderate
Damage State
Num
ber
of B
ridg
es
Extensive Complete
FIGURE 9 Number of bridges by expected damage state for each sensitivity study
simulation at the (a) Mw 5.3 event and the (b) Mw 7.3 event.
Risk to Charleston Bridges 931
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
losses due to using REDARS damage ratios, yielding a net increase in economic losses of
46% and 88% for the Mw 5.3 and 7.3 events, respectively. The findings reveal that while
the risk assessment is very sensitive to both the assumed fragility curves and damage
ratios, the estimate of total expected economic losses is more sensitive to the vast
differences in damage ratio models.
9. Conclusions
In this article, the risk assessment framework for evaluating bridge damage and economic
losses due to earthquake events is presented for application to a case study in Charleston,
South Carolina. The bridge network for the case study consists of 375 bridges of varying
types, and the risk assessment conducted for three different scenario events utilizes region
specific bridge fragility curves and construction cost data for damage and loss estimation.
The case study reveals expected damage states of moderate, extensive, or complete
damage for over 85% of the Charleston bridges due to a Mw 7.0 event located approxi-
mately 14.5 km outside of the city center, near Summerville, South Carolina.
Additionally, while noting the potential for achieving each damage state is assessed
using the fragility curves and propagated through the loss estimation, the mean damage
state alone indicates that nearly 20% of the bridges may suffer some level of damage for a
Mw 4.0 event. Hence, this low level event may still warrant immediate deployment of
inspection teams. The expected value of direct economic losses due to bridge repair alone
are on the order of $40 million for the Mw 5.5 event, with both the loss estimate and
standard deviation about the estimate increasing exponentially with increasing event
magnitude. For the regional inventory in Charleston, the more vulnerable bridge types,
such as the multi-span continuous steel girder bridges, are expected to contribute dis-
proportionately to the economic losses, despite their relatively small percentage of the
overall bridge inventory. These results indicate that such bridge types may be critical
priorities for retrofit.
A sensitivity study is conducted to evaluate the impact of assumed SRA input models
on the resulting loss estimates, assessing the effect of fragility models and damage ratios
for upper and lower level events. In a full factorial design, both the CSUS specific bridge
fragility curves relative to current HAZUS fragilities, as well as REDARS versus Basoz
(currently implemented in HAZUS) damage ratios are considered. The findings reveal a
strong sensitivity of the resulting loss estimates, and variability about the estimate, to
assumed fragility models and damage ratios. The expected value of losses differ on the
order of 150% for both the upper and lower level events considered in the sensitivity
study (Mw 5.3 and 7.3). The roughly linearly increasing damage ratio and repair cost
estimate for the REDARS model, as opposed to roughly exponential increase with the
Basoz ratios, yields the greatest impact on increasing the loss estimate. For the case study
inventory and cost figures considered, the use of the HAZUS fragility curves resulted in
lower loss estimates. However, this was found to be a function of the type of bridges
found in the region and relative contribution of different bridge types to total losses, since
for some bridges HAZUS fragilities indicate an increase in vulnerability relative to the
CSUS specific models, while for other bridge types they depict a lower fragility.
Acknowledgments
This study has been supported by the Earthquake Engineering Research Centers program
of the National Science Foundation under Award Number EEC-9701785 (Mid-America
932 J. E. Padgett, R. DesRoches, and E. Nilsson
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
Earthquake Center). The South Carolina Department of Transportation (SCDOT) is
gratefully acknowledged for their input and data sharing throughout the research project.
References
ATC (Applied Technology Council) [1991] ‘‘Seismic vulnerability and impact of disruption of
lifelines in the conterminous United States,’’ Report No. ATC-25. Redwood City, California.
Basoz, N. and Mander, J. B. [1999] ‘‘Enhancement of the lifeline transportation module in
HAZUS,’’ National Institute of Building Sciences Report, Washington, D.C.
Bollinger, G. A. [1977] ‘‘Reinterpretation of the intensity data for the 1886 Charleston, South
Carolina, earthquake,’’ in Studies Related to the Charleston, South Carolina, Earthquake of
1886: A Preliminary Report, ed. D. W. Rankin, U.S. Geological Survey Professional Paper, pp.
17–32.
Crowley, H., Bommer, J. J., Pinho, R., and Bird, J. [2005] ‘‘The impact of epistemic uncertainty on
an earthquake loss model,’’ Earthquake Engineering and Structural Dynamics 34, 1653–1685.
Dutta, A. [1999] ‘‘On energy based seismic analysis and design of highway bridges,’’ PhD
Dissertation, State University of New York at Buffalo, Buffalo, New York.
FEMA [2005] HAZUS-MH (Hazards US Multi-Hazard) Software, Federal Emergency Management
Agency, Washington D.C.
FHWA [2005] National Bridge Inventory Data, Federal Highway Administration, Washington,
D.C.
Grossi, P. A. [2000] ‘‘Quantifying the uncertainty in seismic risk and loss estimation,’’ PhD
Dissertation, University of Pennsylvania, Philadelphia, Pennsylvania.
Kircher, C., Whitman, R., and Holmes, W. [2006] ‘‘HAZUS earthquake loss methodologies,’’
Natural Hazards Review 7(2), 45–60.
Kiremidjian, A., Moore, J., Fan, Y. Y., Yazlali, O., Basoz, N., and Williams, M. [2007] ‘‘Seismic
risk assessment of transportation networks,’’ Journal of Earthquake Engineering 11, 371–382.
Luna, R., Hoffman, D., and Lawrence, W. T. [2008] ‘‘Estimation of earthquake loss due to bridge
damage in the St. Louis metropolitan area. I: Direct losses,’’ Natural Hazards Review 9(1), 1–11.
MAEC (Mid-America Earthquake Center) [2006] MAEViz Software: Mid-America Earthquake
Center Seismic Loss Assessment System, http://mae.ce.uiuc.edu/software_and_tools/maeviz.html,
Urbana, Illinois
Nielson, B.G. [2005] ‘‘Analytical fragility curves for highway bridges in moderate seismic zones,’’
PhD dissertation, Georgia Institute of Technology, Atlanta, Georgia.
Nielson, B. G. and DesRoches, R. [2007] ‘‘Seismic fragility curves for typical highway bridge
classes in the Central and Southeastern United States,’’ Earthquake Spectra 23(3), 615–633.
Padgett, J. E. and DesRoches, R. [2007] ‘‘Bridge functionality relationships for improved seismic
risk assessment of transportation networks,’’ Earthquake Spectra 23(1), 115–130.
Porter, K. A., Beck, J. L., and Shaikhutdinov, R. V. [2002] ‘‘Sensitivity of building loss estimates to
major uncertain variables,’’ Earthquake Spectra 18(4), 719–743.
SCDOT [2007] ‘‘Personal communication: South Carolina bridge construction cost data,’’ with J.
Padgett and R. DesRoches.
Shinozuka, M., Chang, S., Eguchi, R., Abrams, D., Hwang, H., and Rose, A. [1997] ‘‘Advanced in
earthquake loss estimation and application to Memphis, TN,’’ Earthquake Spectra 13(4), 739–758.
Werner, S. D., Taylor, C. E., Moore, III, J. E., Walton, J. S., and Cho, S. [2000] ‘‘A risk-based
methodology for assessing the seismic performanceof highway systems,’’ Technical Report No.
MCEER-00-0014, Multidisciplinary Center for Earthquake Engineering Research, State
University at Buffalo, Buffalo, New York.
Werner, S. D., Taylor, C. E., and Moore, J. E. [1997] ‘‘Loss estimation due to seismic risks to
highway systems,’’ Earthquake Spectra 13(4), 585–604.
Werner, S. D., Taylor, C. E. Cho, S., Lavoie, J. P., Huyck, C. K., Eitzel, C., Chung, H., and Eguchi,
R. T. [2006] ‘‘REDARS 2 methodology and software for seismic risk analysis of highway
systems,’’ Report No. MCEER-06-SP08, Buffalo, New York.
Risk to Charleston Bridges 933
Downloaded By: [Georgia Technology Library] At: 19:07 9 September 2010
Top Related