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SEISMIC RISK TO ROADS AND BRIDGES IN THE KYRGYZ REPUBLIC, CENTRAL ASIA

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The Kyrgyz Republic is located in a highly seismic region subjected to devastating earthquakes that have caused loss of life, destroyed buildings and infrastructure and ruined livelihoods in historical and recent times. In order to better understand the hazard and the risk from earthquakes to critical assets, including transport infrastructure, a national level seismic hazard and risk study was undertaken. Across the Kyrgyz Republic there are around 4,300 km of four-lane primary roads, 43,000 km of two-lane secondary roads and over 1,400 road bridges with an estimated total value of USD 34 billion. The study included a probabilistic seismic hazard assessment for the country as well as twelve (12) representative scenario earthquake events hazard calculations. The mean expected direct economic losses to road transport infrastructure associated with the individual scenario earthquake events was estimated to be in the range of USD 60 million to 1 billion for roads and in the range of USD 2.4 to 22 million for bridges. These findings will allow stakeholders to make informed decisions for upgrades and new investment for transport infrastructure to reduce losses, better plan for emergency response and inform longer term recovery after earthquake disasters.
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SEISMIC RISK TO ROADS AND BRIDGES IN THE KYRGYZ
REPUBLIC, CENTRAL ASIA
Matthew FREE
1
, Katherine COATES
2
, Yannis FOURNIADIS
3
, Thomas ADER
4
, Luis SOUSA
5
,
Kevin FLEMING
6
, Massimiliano PITTORE
7
, Bolot MOLDOBEKOV
8
, Cholponbek ORMUKOV
9
ABSTRACT
The Kyrgyz Republic is located in a highly seismic region subjected to devastating earthquakes that have caused
loss of life, destroyed buildings and infrastructure and ruined livelihoods in historical and recent times. In order to
better understand the hazard and the risk from earthquakes to critical assets, including transport infrastructure, a
national level seismic hazard and risk study was undertaken. Across the Kyrgyz Republic there are around 4,300
km of four-lane primary roads, 43,000 km of two-lane secondary roads and over 1,400 road bridges with an
estimated total value of USD 34 billion. The study included a probabilistic seismic hazard assessment for the
country as well as twelve (12) representative scenario earthquake events hazard calculations. The mean expected
direct economic losses to road transport infrastructure associated with the individual scenario earthquake events
was estimated to be in the range of USD 60 million to 1 billion for roads and in the range of USD 2.4 to 22 million
for bridges. These findings will allow stakeholders to make informed decisions for upgrades and new investment
for transport infrastructure to reduce losses, better plan for emergency response and inform longer term recovery
after earthquake disasters.
Keywords: Seismic; Risk; Kyrgyz; Road; Bridge
1. INTRODUCTION
1.1 General
The Kyrgyz Republic is located in a region of high seismic hazard with earthquakes of magnitude Mw≥5
occurring about once per month, and potentially devastating earthquakes of magnitude Mw≥7 occurring
with recurrence intervals of several decades. Although earthquakes occur less frequently than other
natural hazards such as floods and landslides, they cause the largest proportion of disaster related losses
across the country (World Bank, 2008). The country has a population of approximately 6 million people
and had a GDP of 6.6 billion USD in 2015 (World Bank, 2016). Due to rapid urbanisation and the
developing nature of the economy in the Kyrgyz Republic, there is a strong incentive to invest in a
national seismic risk reduction strategy as the most effective way to mitigate the potential impact of
disaster related shocks and reduce expected losses. In order to better understand seismic hazard and risk
in the country, the World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR)
have commissioned Ove Arup & Partners International Ltd (Arup), Helmholtz Centre Potsdam German
Research Centre for Geosciences (GFZ), the Central Asian Institute of Applied Geosciences (CAIAG)
1
Director, Geohazards and Risk, Arup, London, UK, matthew.free@arup.com
2
Ms Katherine Coates, Arup, London, UK, katherine.coates@arup.com
3
Dr Yannis Fourniadis, Arup, London, UK, yannis.fourniadis@arup.com
4
Dr Thomas Ader, Arup, London, UK, thomas.ader@arup.com
5
Dr Luis Sousa, Arup, London, UK (presently with AIR), costa.sousa@fe.up.pt
6
Dr Kevin Fleming, GFZ, Potsdam, Germany, kevin@gfz-potsdam.de
7
Dr Massimiliano Pittore, GFZ, Potsdam, Germany, pittore@gfz-potsdam.de
8
Mr Bolot Moldobekov, CAIAG, Bishkek, Kyrgyz Republic, b.moldobekov@caiag.kg
9
Mr Cholponbek Ormukov, CAIAG, Bishkek, Kyrgyz Republic, ch.ormukov@caiag.kg
2
and the Global Earthquake Model Foundation (GEM) to perform the first detailed countrywide
quantitative seismic hazard and risk assessment for the Kyrgyz Republic. As part of this study, the
seismic risk to transport infrastructure (roads and bridges) from selected earthquake scenario events has
been calculated, and is presented in this paper along with related seismic risk management
recommendations. The results of the seismic risk to buildings and their occupants in the Kyrgyz
Republic is described in a separate paper (Free et al., 2018).
1.2 Earlier Seismic Risk Studies in the Kyrgyz Republic
The Kyrgyz Republic has acknowledged the importance of a comprehensive approach to disaster risk
reduction (DRR). In particular, the implementation of the Sendai Framework and the coordination of
efforts from several governmental agencies have been listed as strategic goals for the country (UN,
2015). A number of risk projects for the Central Asia region have assessed seismic risk in the Kyrgyz
Republic (Bindi et al., 2011; Pittore et al., 2014). However, these studies did not specifically assess the
expected damage and losses to transport infrastructure in the country.
2. HAZARD, EXPOSURE AND VULNERABILITY COMPONENTS OF TRANSPORT
INFRASTRUCTURE
2.1 Seismic Hazard in the Kyrgyz Republic
In the event of an earthquake, both ground shaking and related ground deformation can cause damage
and losses to linear transport infrastructure such as bridges and roads. The computation of ground
shaking intensity (in terms of peak ground acceleration (PGA), macroseismic intensity, and spectral
ordinates up to a period of 2.0sec.) has been performed as part of a comprehensive seismic hazard
assessment for the Kyrgyz Republic (Arup, 2016).
2.1.1 Scenario Earthquake Calculations
The seismic hazard assessment included scenario earthquake calculations for twelve (12) maximum
credible scenario earthquake events (selected according to the potential impact on urban centres) (Figure
1) as well as a probabilistic seismic hazard assessment for the entire country. A more detailed discussion
of the seismic hazard levels across the country is given in Arup (2016) and Free et al. (2018).
Figure 1. Geological fault locations for the scenario analyses.
3
Figure 2 gives an example that shows the distribution of ground shaking (median results) in terms of
peak horizontal ground acceleration (PGA) for the Issyk-Ata scenario event near Bishkek, the country’s
largest city (population 1 million) (Figure 2). The Issyk Ata Fault is located south of Bishkek and this
scenario is associated with a magnitude Mw 7.3 earthquake on the fault.
Figure 2. Earthquake scenario PGA values (median prediction) for the Issyk Ata Fault using a combination of
GMPEs and a USGS Vs30 site soil assumption (Arup, 2016). The red star marks the epicentre.
2.1.2 Permanent Ground Deformations (PGD) as a Result of Seismic Shaking
Damage to roads and bridges is commonly associated with permanent ground deformation (PGD),
which ismainly caused by liquefaction, and also by landslides and surface fault ruptures (Pitilakis et al.,
2014) (Figure 3). The evaluation of liquefaction-induced PGD is a complex undertaking, and a number
of methodologies are available for the assessment of expected this behaviour (Bird et al., 2006). The
majority of these methodologies require the input of detailed geotechnical data (e.g. median particle
size, fines content) (Kongar et al., 2016). However, it was not possible to retrieve such data at an
appropriate resolution for the Kyrgyz Republic, and so alternative methodologies for assessing
permanent ground deformations were investigated for this study (as presented in Bird et al, 2006).
Figure 3. Damage to Kyrgyz roads from surface seismic waves (left) and from seismically-induced rock slides
(right) as a result of the Nura earthquake (5 October 2008).
4
For the present study, the HAZUS models for lateral movement and vertical settlement (FEMA, 2003)
were chosen, as they can be applied without the need for geotechnical data. According to the HAZUS
methodology, PGD due to lateral spreading can be estimated based on:
The assignment of a liquefaction susceptibility category (SC);
The threshold ground acceleration necessary to induce liquefaction for the selected SC;
The PGA experienced at the site, as determined by probabilistic seismic hazard analysis; and
A displacement correction factor (k), as a function of the moment magnitude (Mw) of the event.
The probability of liquefaction was estimated following the procedure set out by Zhu et al. (2015), who
proposed empirical functions to predict the liquefaction probability conditional on a specific level of
PGA. The functions were developed using logistic regression on data from the earthquakes that occurred
in Kobe, Japan, on 17 January 1995 and in Christchurch, New Zealand, on 22 February 2011. The
functions were further tested on observations from the 12 January 2010 Haiti earthquake.
A number of limitations are associated with the methodology used in this study:
PGD predictions estimated using this methodology are due only to the possible effects of liquefaction
and shakedown settlement. Other causes of PGD are therefore not considered;
The selected simplified methodology (HAZUS) has been developed on the basis of empirical data
from California and Japan. Given the lack of post-earthquake damage data, it was not possible to
assess the applicability of this model to the Kyrgyz Republic;
Additional input data for the HAZUS model (e.g. groundwater depth, liquefaction susceptibility
defining parameters) were only approximately known for the territory of the Kyrgyz Republic; and
The aforementioned approximations were used under the assumption that they introduce additional
uncertainty without biasing the results towards clearly lower or higher estimates.
To account for site response effects, estimates of ground motion amplification are computed as a
function of VS30, considering the following alternatives:
A scenario in which a uniform shear-wave velocity VS30 = 250m/s was considered at all asset
locations; and
A non-uniform site amplification based on the estimation of VS30 from topographic data (Wald and
Allen, 2007).
2.2 Exposure Model for Roads and Bridges
No data on roads or bridges were received from official Kyrgyz sources as input to this study. Therefore,
open source data was used. The exposure models for roads were derived from the OpenStreetMap
database of primary and secondary roads in the Kyrgyz Republic and validated with high resolution
remote-sensing imagery provided by Bing and Google Maps. Figure 4 presents the spatial distribution
of primary and secondary roads in the Kyrgyz Republic. The replacement value of the road network was
developed on the basis of replacement costs (USD/m2) that were available from local construction cost
data sources, and assuming a total width of 7.0m for the secondary roads and 12.0m for the primary
roads. Across the Kyrgyz Republic there are around 4,300 km of four-lane primary roads and around
43,000 km of two-lane secondary roads. The total value of secondary roads is estimated to be 30 billion
USD, while the primary roads amount to a value of approximately 3.3 billion USD.
Bridge properties and locations were extracted from OpenStreetMap. In addition, a small number of
bridge visual inspections were performed, where the structural characteristics of the bridges were
recorded. Figure 5 presents examples of the inspected bridges. A total of around 1,400 bridges were
extracted from OpenStreetMap, and assigned the relevant typologies using broad categories (concrete,
steel or other). Bridges were classified according to their replacement value (in USD), which was
obtained from international construction cost data sources for bridges of this type and scale (on average,
40m span). The total value of the bridge portfolio is approximately 500 million USD.
5
Figure 4. Primary and secondary roads in the Kyrgyz Republic (the inset shows the region around Bishkek).
2.3 Fragility and Vulnerability of Roads and Bridges
Simple typology descriptions were defined specifically for each road and bridge typology. Damage
states and fragility functions for the road and bridge typologies were obtained from the SYNER-G
project (JRC, 2013) and combined into the final fragility model.
2.3.1 Fragility and Vulnerability of Roads
Fragility functions for roads are defined for minor, moderate and extensive/complete damage states, as
presented in Table 1 and Figure 6. The damage-to-loss ratios (ratio between attained loss for a specific
damage state and total value of the road segment) proposed by FEMA (2003) (Table 2) were combined
with the corresponding fragility curves (shown in Figure 6), resulting in the vulnerability model
illustrated in Figure 7.
Table 1. Lognormal parameters for fragility functions for roads in terms of PGD (JRC, 2013).
Typology
Damage state
μ (m)
β
2 traffic lanes (secondary
roads)
Minor
0.15
0.70
Moderate
0.30
0.70
Extensive/complete
0.60
0.70
≥ 4 traffic lanes (primary
roads)
Minor
0.30
0.70
Moderate
0.60
0.70
Extensive/complete
1.50
0.70
6
Figure 5. Examples of Kyrgyz bridges that were inspected in May 2015.
Figure 6. Fragility functions for primary and secondary roads for minor, moderate and complete damage.
Table 2. Damage-to-loss model proposed by FEMA (2003) for roads.
Typology
Damage state
Damage Ratio*
2 traffic lanes (secondary roads)
Minor
0.05
Moderate
0.20
Extensive/complete
0.70
≥ 4 traffic lanes (primary roads)
Minor
0.05
Moderate
0.20
Extensive/complete
0.70
* Ratio between attained loss for a specific damage state and the total value of the affected road segment.
7
Figure 7. Vulnerability functions for primary and secondary roads.
2.3.2 Fragility and Vulnerability of Bridges
Bridge vulnerability is dependent on construction material, complexity of the structure, the interaction
with the bridge abutments, and the local ground conditions. In this study, fragility functions for road
bridges were defined for three bridge types: concrete, steel and “other”. Damage thresholds were defined
by two damage states: (i) minor damage (or yielding) and (ii) extensive/complete damage. The fragility
functions developed as part of the SYNER-G project (JRC, 2013) were harmonised in terms of intensity
measure, damage state definition and bridge properties. In the case of concrete bridges, fragility curves
were computed as a weighted combination of curves defined for each of different combinations of two
sets of attributes: isolated/non-isolated and irregular/regular. Fragility functions for steel road bridges
were obtained through a similar approach, whereby curves proposed for multi-span simply-supported,
multi-span continuous, and continuous steel bridges were combined. In the case of “other” bridges,
fragility functions reflect the weighted combination of the concrete and steel fragility curves, in
accordance with the proportion of steel or concrete bridges in the exposure dataset.
Table 3 and Figure 8 present the bridge fragility model, and Figure 9 illustrates the vulnerability curves
computed for the different bridge typologies, based on the damage-to-loss model proposed by FEMA
(2003) (Table 4).
Table 3. Lognormal distribution parameters for fragility functions for road bridges in terms of PGA (units of g);
adapted from JRC (2013).
Typology
Damage State
μ (g)
β
Concrete
Minor
0.19
0.64
Complete
0.96
0.54
Steel
Minor
0.23
0.16
Complete
0.76
0.46
Other
Minor
0.21
0.52
Complete
0.88
0.52
8
Figure 8. Fragility functions for concrete, steel and “other” bridges.
Table 4. Damage-to-loss model proposed by FEMA (2003) for bridges (n is the number of spans. If n≤2, a
damage ratio of 1.00 was applied).
Bridge type
Damage state
Damage Ratio
Steel, concrete or “other”
Minor damage
0.01
Extensive/complete
2/n
Figure 9. Vulnerability functions for concrete, steel and “other” bridges.
3. SCENARIO EARTHQUAKES RISK RESULTS
This section presents a summary of the range of scenario earthquake-based risk results in terms of
economic losses estimated for transport infrastructure for all scenarios. Economic losses are presented
in absolute values and as percent of GDP. The mean results are presented for assumed site conditions of
VS30 = 250 m/s (soft soil) and for the USGS VS30 values. The losses reported correspond to the mean
plus/minus one standard deviation.
3.1 Scenario Earthquake Risk Results for Roads and Bridges
Figure 10 and Figure 11 provide a summary of the expected losses to transport infrastructure (bridges
and roads respectively) when subjected to scenario earthquake ground shaking. The mean expected
economic losses for the range of earthquake scenarios are in the order of 2.3 to 22 million USD for
bridges and 60 million to 1.0 billion for roads but could be up to three times higher than these mean
values when accounting for the associated standard deviation. These results indicate that the level of
9
damage to many roads and bridges in the event of these selected scenario earthquakes could seriously
hamper emergency response and longer term economic recovery.
Figure 10. Summary of mean economic losses for bridges across the Kyrgyz Republic. Error bars represent the
mean plus and minus one standard deviation.
Figure 11. Summary of mean economic losses for roads across the Kyrgyz Republic. Error bars represent the
mean plus and minus one standard deviation.
3.2 Example Scenario Earthquake Risk Results: Issyk Ata Fault Scenario
This section presents the risk results for the Issyk Ata Fault scenario earthquake (refer to Section 2.1 for
a description of this scenario). Figure 12 presents the spatial distribution of mean loss ratios for roads
and bridges, while Figure 13 presents the mean losses for roads and bridges aggregated by district. Note
10
the very high bridge loss ratios and road loss ratios in the area of the scenario earthquake (an area of
about 200km x 125km).
Figure 12. Issyk Ata Fault scenario. Spatial distribution of mean loss ratios (ratio between attained loss and total
value of the road or bridge segment), considering a non-uniform VS30 distribution obtained from USGS.
Figure 13. Issyk Ata Fault scenario. Spatial distribution of mean absolute losses (USD) per district, considering a
non-uniform VS30 distribution obtained from USGS.
4. RISK MANAGEMENT STRATEGY FOR ROADS AND BRIDGES
The seismic hazard and risk study for the country informed an overall seismic risk reduction strategy.
This strategy included recommendations tailored by sector, asset category and key stakeholders and was
framed according to the Sendai Framework (UN, 2015). Selected risk reduction recommendations for
transport infrastructure (roads and bridges) as presented in Table 5 include:
11
Table 5 Risk Reduction Recommendations for Roads and Bridges in the Kyrgyz Republic
Risk Reduction Recommendations
Key Stakeholders
Timeframe
Sendai
Priority
Use the results of this study to inform
emergency response planning and initial
prioritization of critical bridges for
assessment and upgrading.
Ministry of Transport (MoT),
Ministry of Emergency Services
(MoES), National Government
3 to 6
months
3, 4
Establish a countrywide database of roads
and bridges.
MoT, MoES, National
Government
6 to 12
months
1, 3
Perform selected assessments for critical
bridges.
MoT, Ministry of Construction
(GOSSTROY)
1 to 4 years
1, 3
Perform an updated seismic risk
assessment for transport infrastructure and
cost benefit analyses for bridge retrofits
and replacements.
MoT, GOSSTROY, World
Bank
1 to 4 years
1, 3
Based on the results of the risk assessment,
perform prioritized upgrades (retrofits or
replacements) for critical bridges.
MoT, GOSSTROY
1 to 4 years
3
Based on the risk results for each scenario earthquake event, potentially heavily damaged critical roads
and bridges for primary routes were identified. Recommendations were given for prioritizing upgrades
and/or replacements of critical bridges as well as highlighting damaged roads in areas that lack multiple
access routes and redundancy for emergency planning and/or supplies. For example, for the Issyk Ata
scenario, remote communities in mountainous areas south of Bishkek may be cut off if certain secondary
roads are heavily damaged.
5. CONCLUSIONS
The seismic risk assessment described is this paper includes the probabilistic assessment of the effect of
twelve (12) scenario earthquakes with magnitudes (Mw) ranging from 6.7 to 8.3 occurring on mapped
geological faults located throughout the country. This study has shown that large losses are expected for
transport infrastructure (roads and bridges) when these assets are subjected to the simulated scenario
earthquake ground shaking. The expected direct economic losses to roads from the individual scenario
earthquakes were estimated to be in the range of USD 60 million to 1 billion and damage to bridges in
the range of USD 2.4 to 22 million depending on the proximity of the scenario earthquake events to the
infrastructure assets. The risk results for transport infrastructure identified the extent and geographic
concentration of expected damage and direct economic losses near major urban centres for the selected
scenario events. The findings have been used to prepare risk management strategy options for transport
infrastructure to allow stakeholders to make informed investments and decisions to reduce losses, better
plan for emergency response and inform longer term recovery after earthquake disasters.
The significance of the seismic risk facing the Kyrgyz Republic has been communicated to a wide range
of Kyrgyz stakeholders (e.g. the Office of the Prime Minister, the Ministry of Emergency Situations, the
Ministry of Transport, etc.), and multilateral donor organisations (e.g. the World Bank) over a number
of meetings and workshops held in the Kyrgyz Republic.
5.1 Limitations of Seismic Risk Calculations
The quality of the seismic risk assessment results that are described in this paper are dependent on the
accuracy and detail of the spatial input information that was made available for this assessment. The
main sources of information for the present risk assessment came from a combination of literature
12
review, from public domain sources and from field surveys. Uncertainties still remain, however, in terms
of the location of the assets, their structural characteristics and structural condition, and their
replacement costs. Engineering judgement, statistical data treatment and the input of local experts were
used to overcome the limitations in the available information for the various infrastructure asset classes
for the current project. It should also be noted that there are inherent uncertainties in seismic risk
calculations when using estimated infrastructure characteristics and estimated ground conditions and
seismic ground motions. These uncertainties have been treated in a systematic way, and sensitivity
analyses have been undertaken. Despite these limitations, it is important to emphasize that this study
has provided a meaningful set of risk results to inform a seismic risk reduction strategy for the transport
infrastructure assets of the country.
6. ACKNOWLEDGMENTS
The authors wish to thank the Government of the Kyrgyz Republic, the World Bank and the Global
Facility for Disaster Risk Reduction (GFDRR) for their support with the undertaking of this project, and
permission to publish the results.
7. REFERENCES
Arup (2016). Measuring Seismic Risk in Kyrgyz Republic, Seismic Hazard Assessment, World Bank. Report Ref.:
240323_HAZ_HAZ002, dated 21 December 2016. (http://geonode.mes.kg/).
Arup (2017). Measuring Seismic Risk in Kyrgyz Republic, Seismic Risk Assessment, World Bank. Report Ref.:
240323_RI_RP001, dated 19 July 2017. (http://geonode.mes.kg/).
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Towards an improved seismic risk scenario for Bishkek, Kyrgyz Republic. Soil Dynamics and Earthquake
Engineering 31:521525.
Bird J-F, Bommer J, Crowley H, Pinho R (2006). Modelling liquefaction-induced building damage in earthquake
loss estimation. Soil Dynamics and Earthquake Engineering 26: 1530.
FEMA (2003). HAZUS-MH Technical Manuals. Federal Emergency Management Agency, Washington, D.C.
Free M, Coates K, Fourniadis Y, Ader T, Sousa L, Fleming K, Pittore M, Moldobekov B, Ormukov C (2018).
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Pitilakis K, Crowley H, Kaynia A (eds) (2014). SYNER-G: Typology Definition and Fragility Functions for
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Wald D-J, Allen T-I (2007). Topographic Slope as a Proxy for Seismic Site Conditions and Amplification, Bull.
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rapid response and loss estimation. Earthquake Spectra, 31(3): 1813-183.
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... In this section, a brief overview of the seismic hazard and risk assessment is presented. A more detailed description of the methodology and results can be found in Free et al. (2018a;2018b), Free et al. (2019) and the full project reports (Arup, 2016a;Arup, 2017a). ...
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Over the last decade, increasing attention has been paid by the international community to the topic of earthquake early warning (EEW) systems, as a viable solution to protect specific hazard‐prone targets (major cities or critical infrastructure) against harmful seismic events. The aim of the EEW system is to detect the occurrence of an earthquake and to determine its relevant characteristics (such as location and magnitude) early enough to predict the ground shaking at the target site before the S ‐wave arrival. Possible emergency protocols that can be activated upon event detection range from slowing down or stopping rail traffic (Nakamura, 2004; Horiuchi et al. , 2005; Espinosa‐Aranda et al. , 2011), safely shutting down or activating protective measure of critical infrastructures such as nuclear power plants (Saita et al. , 2008), to broadcasting alerts to the general public (Wenzel and Lungu, 2000; Lee and Espinosa‐Aranda, 2002; Allen and Kanamori, 2003; Horiuchi et al. , 2005; Wu et al. , 2007). Only few systems have been actually implemented and are currently operational. Examples of regional applications are the systems operating in California, Japan, and Taiwan, whereas targeted systems have been developed, for instance, in Mexico, Irpinia (Italy), and Vrancea (Romania). We refer the interested readers to the comprehensive references in Wenzel and Zschau (2014). Despite the potential benefits of EEW system, several factors so far hindered their widespread application especially in economically developing countries. When the distance between the seismic sources and the exposed target is too short for instance, or there is no technological infrastructure supporting real‐time, automatic operations, the information provided by the EEW system cannot be exploited for pre‐event actions. In these cases, which occur remarkably often in many seismic regions, the level of ground shaking predicted by the system can still be used as input …
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We describe an approach to model liquefaction extent that focuses on identifying broadly available geospatial variables (e.g., derived from digital elevation models) and earthquake-specific parameters (e.g., peak ground acceleration, PGA). A key step is database development: We focus on the 1995 Kobe and 2010-2011 Christchurch earthquakes because the presence/absence of liquefaction has been mapped so that the database is unbiased with respect to the areal extent of liquefaction. We derive two liquefaction models with explanatory variables that include PGA, shear-wave velocity, compound topographic index, and a newly defined normalized distance parameter (distance to coast divided by the sum of distance to coast and distance to the basin inland edge). To check the portability/reliability of these models, we apply them to the 2010 Haiti earthquake. We conclude that these models provide first-order approximations of the extent of liquefaction, appropriate for use in rapid response, loss estimation, and simulations.
Article
A risk scenario for Bishkek, capital of the Kyrgyz Republic, is evaluated by considering a magnitude 7.5 earthquake occurring over the Issyk-Ata fault. The intensity values predicted through the application of an attenuation relationship and a recently compiled vulnerability composition model are used as inputs for seismic risk assessment, carried out using the Cedim Risk Estimation Tool (CREST) code. Although the results of this study show a reduction by as much as a factor of two with respect to the results of earlier studies, the risk scenario evaluated in this paper confirms the large number of expected injuries and fatalities in Bishkek, as well as the severe level of building damage.Furthermore, the intensity map has also been evaluated by performing stochastic simulations. The spectral levels of the ground shaking are converted into intensity values by applying a previously derived conversion technique. The local site effects are empirically estimated considering the spectral ratios between the earthquakes recorded by a temporary network deployed in Bishkek and the recordings at two reference sites. Although the intensities computed via stochastic simulations are lower than those estimated with the attenuation relationship, the simulations showed that site effects, which can contribute to intensity increments as large as 2 units in the north part of the town, are playing an important role in altering the risk estimates for different parts of the town.
Article
We describe a technique to derive first-order site-condition maps di-rectly from topographic data. For calibration, we use global 30 arc sec topographic data and V S 30 measurements (here V S 30 refers to the average shear-velocity down to 30 m) aggregated from several studies in the United States, as well as in Taiwan, Italy, and Australia. V S 30 values are correlated against topographic slope to develop two sets of parameters for deriving V S 30 : one for active tectonic regions where to-pographic relief is high, and one for stable shields where topography is more subdued. By taking the gradient of the topography and choosing ranges of slope that maximize the correlation with shallow shear-velocity observations, we can recover, to first order, many of the spatially varying features of site-condition maps developed for California. Our site-condition map for the low-relief Mississippi Embayment also predicts the bulk of the V S 30 observations in that region despite rather low slope ranges. We find that maps derived from the slope of the topography are often well cor-related with other independently derived, regional-scale site-condition maps, but the latter maps vary in quality and continuity, and subsequently, also in their ability to match observed V S 30 measurements contained therein. Alternatively, the slope-based method provides a simple approach to uniform site-condition mapping. After validating this approach in regions with numerous V S 30 observations, we subsequently estimate and map site conditions for the entire continental United States using the respective slope correlations.
Article
The assessment of building damage caused by liquefaction-induced ground deformations requires the definition of building capacity and vulnerability as a function of the demand, as well as damage scales to describe the state of the damaged building. This paper presents a framework for resolving these issues within the context of earthquake loss estimations, where large variations in building stock and ground conditions must be considered. The principal modes of building response to both uniform and differential ground movements are discussed and the uncertainties in their evaluation are highlighted. A unified damage scale is proposed for use in both reconnaissance and assessment of all modes of building damage, including ‘rigid body’ response of structures on stiff foundations to uniform or differential ground movements. The interaction of ground shaking and liquefaction in the context of induced structural damage is also briefly considered. The paper raises important aspects of earthquake loss estimations in regions of liquefaction potential, which remain relatively poorly defined at present.
Measuring Seismic Risk in Kyrgyz Republic, Seismic Hazard Assessment, World Bank
  • Arup
Arup (2016). Measuring Seismic Risk in Kyrgyz Republic, Seismic Hazard Assessment, World Bank. Report Ref.: 240323_HAZ_HAZ002, dated 21 December 2016. (http://geonode.mes.kg/).
HAZUS-MH Technical Manuals
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FEMA (2003). HAZUS-MH Technical Manuals. Federal Emergency Management Agency, Washington, D.C.
SYNER-G: Typology Definition and Fragility Functions for
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Pitilakis K, Crowley H, Kaynia A (eds) (2014). SYNER-G: Typology Definition and Fragility Functions for Physical Elements at Seismic Risk, Vol. 27 Geotechnical, Geological and Earthquake Engineering.
D8.10 -Guidelines for deriving seismic fragility functions of elements at risk: Buildings, lifelines, transport networks and critical facilities, SYNER-G Reference Report 4 -Systemic Seismic Vulnerability and Risk Analysis for Buildings, Lifeline Networks and Infrastructures
JRC (2013). D8.10 -Guidelines for deriving seismic fragility functions of elements at risk: Buildings, lifelines, transport networks and critical facilities, SYNER-G Reference Report 4 -Systemic Seismic Vulnerability and Risk Analysis for Buildings, Lifeline Networks and Infrastructures. Joint Research Centre.