MSc Dissertation - Naqvi

University College London
Identifying Regions with High
Liquefaction Potential Close To Large
Populations in Europe
Student’s Name: Syed Ali Hamza Naqvi
Supervisors: Dr. Carmine Galasso
Alexandra Tsioulou
MSc in Earthquake Engineering with Disaster Management
Dissertation 2015
September 7, 2015
Department of Civil, Environment & Geomatic Engineering

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ENVIRONMENTAL & GEOMATIC
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ACKNOWLEDGEMENTS
I would like to start by thanking the British Government for granting me the Commonwealth
Scholarship to help me pursue my dream in being able to study in University College London,
one of the most prestigious universities in the world.
Next I would like to thank the faculty of CEGE Department of UCL with foremost being my
Program Director, Lecturer and Project Supervisor, Dr. Carmine Galasso, who has helped me
throughout my time period at UCL as an academic and also as a friend. Without his kindness,
patience and humor, this tenure at UCL could not have been amazing and fruitful. Thank you
so much Carmine.
I thank my mother and father for their unconditional love and support. Without your teachings,
I could not be where I am at this point. Thank you for that. I love you.
To my dear brothers. You both know very well that I love you both but I am never going to say
that on your faces.
My sincerest thanks and love to all my family member in London especially my aunt and uncle,
Sabiha Rizvi and Ali Rizvi for letting me stay at your place during my time in London. I’m
grateful to you both for being able to bear me for this long and loved me like your own son.
Thanks to all my new friends in UK for making my stay memorable here. I have never explored
the world so much, the way you guys have helped me to. India, Bangladesh, Sri Lanka, China,
Iran, Italy, Greece, Romania, Mexico, Ecuador. Special thanks to Danish and Kamran, without
whom my memories in London would not have been as beautiful as they have been. You are
all amazing, and I hope to stay in touch with you all. I will miss you all.
My special thanks goes to Miss Irsa Anwar. This international experience would not have been
possible without your help and support in Pakistan. I can never be thankful enough to you for
helping me get this opportunity in life and I hope you get such an experience too in life. Thank
you Irsa.

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ABSTRACT
Soil Liquefaction is one of the secondary events triggered after an earthquake and can cause a
potential risk to the elements around them. Global seismic hazard and loss have already been
developed by catastrophe modelers but liquefaction risk potential maps have yet to be
developed at a global level. This research is based upon using a Multi Criteria Decision Making
analysis to assess the liquefaction susceptibility in regions close to high population cities of
Europe. In order to calculate the liquefaction risk potential for selected cities, the liquefaction
hazard and the exposure of the region is determined. In particular the potential for liquefaction
depends upon 3 main parameters; hazard, soil conditions and hydrological parameters of the
soil. In order to detect liquefaction, in-situ site tests need to be done in order to determine if the
soil will liquefy or not, but this is not possible to implement at a global level to determine the
probability of liquefaction. Therefore using the simplified method developed by Zhu et al.
(2014), the probability of liquefaction can be computed in a spatial region based on a few
parameters. The parameters used in this approach are the Peak Ground Acceleration (PGA),
average shear wave velocity till a depth of 30 m (Vs30) and compound topography index, (CTI,
which is the hydrological parameter). Once the hazard is calculated, the exposure component
is determined based on population, gross domestic product (GDP) and the human development
index (HDI). In the final stage, the liquefaction risk potential is calculated using the method of
Artificial Neural Networks (Ramhormozian et al, 2013), which uses the weighted normalized
values of the above mentioned parameters and ranks the liquefaction risk potential of the cities.
The weightages are assumed 50% hazard and 50% exposure, where exposure is further divided
into 25% HDI, 20% population and 5% GDP. The results presented here show that Turkey,
Greece, Romania and Italy are the countries where the cities with the highest liquefaction risk
are located and where more detailed probabilistic liquefaction hazard analysis should be
focused on. In some of these regions, the liquefaction hazard component of the model
developed here is consistent with case-histories of past liquefaction events during recent
earthquakes.
It is worth noting that the final results are based upon the weightages assumption and that these
rankings could vary from organization to organization (i.e., the specific decision maker)
depending upon what area to focus on more. For example NGO’s could use the model with
more weightage to hazard than exposure, whereas insurance firm could use the model with
more weightage to exposure than the hazard for financial purposes.

iii
Contents
1 Introduction........................................................................................................................1
1.1 Introduction.................................................................................................................1
1.2 Project Overview.........................................................................................................3
2 Project Objectives ..............................................................................................................5
2.1 Aim..............................................................................................................................5
2.2 Purpose........................................................................................................................5
2.3 Objectives....................................................................................................................6
2.4 Scope...........................................................................................................................6
3 Literature Review...............................................................................................................7
3.1 Earthquakes .................................................................................................................7
3.2 Earthquake Engineering..............................................................................................9
3.3 Seismic Risk Assessment (SRA)...............................................................................10
3.3.1 Seismic Hazard Analysis ...................................................................................11
3.3.2 Seismic Vulnerability Assessment.....................................................................15
3.3.3 Exposure Assessment.........................................................................................17
3.4 Earthquakes in Europe ..............................................................................................18
3.5 Catastrophe Models...................................................................................................19
3.6 Soil Liquefaction.......................................................................................................21
3.7 Liquefaction Susceptibility .......................................................................................28
3.7.1 Liquefaction Potential Index..............................................................................30
3.7.2 Zhu et al. (2014).................................................................................................31
3.8 Cases of Liquefaction in Europe ...............................................................................33
4 Methodology....................................................................................................................35
4.1 Methodology .............................................................................................................35
4.2 Peak Ground Acceleration (PGA).............................................................................35
4.3 Population..................................................................................................................36

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4.4 Time-averaged shear-wave velocity to a depth of 30m (Vs30).................................37
4.5 Compound Topographic Index (CTI) .......................................................................38
4.6 Gross Domestic Product (GDP)................................................................................39
4.7 Human Development Index (HDI)............................................................................40
4.8 Ranking Criteria........................................................................................................41
5 Data, Results and Discussion...........................................................................................44
5.1 PGA values from GSHAP Maps...............................................................................44
5.2 City Population..........................................................................................................48
5.3 Vs30 Values from USGS Vs30 Global Server...........................................................52
5.4 CTI Values extracted from USGS Earth Explorer Maps ..........................................56
5.5 City GDP calculations with the help of World Bank data ........................................60
5.6 Calculation of Probability of Liquefaction, P[Liq] ...................................................64
5.7 MCDM Analysis .......................................................................................................68
5.8 Discussion .................................................................................................................73
6 Conclusion .......................................................................................................................75
7 References........................................................................................................................78

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List of Figures
Figure 1. Yearly Direct Economic Losses for the past 111 years (Daniell, 2012) ....................1
Figure 2. Epicentres of Earthquake induced Liquefaction (Source: AIR Worldwide)..............2
Figure 3. Liquefaction Potential Map of Salt Lake County, Utah (Geology.utah.gov, 2015)...5
Figure 4. Generation of Earthquakes .........................................................................................7
Figure 5. Elastic Rebound Theory (Comet.earth.ox.ac.uk, 2015) .............................................8
Figure 6. Equivalent Energy release by earthquakes (seismo.berkely.edu, 2015) ....................8
Figure 7. Ground motion recording on an accelerograph for the El Centro Earthquake, 1940
(Vibrationdata.com, 2015).......................................................................................................10
Figure 8. The 4 steps of Deterministic Seismic Hazard Analysis (Kramer, 1996)..................12
Figure 9. Five basic steps for probabilistic seismic hazard analysis. (a) Identification of
earthquake sources, (b) Characterization of the distribution earthquake magnitude from each
sources, (c) Characterization of the distribution of source to site distances, (d) Prediction of the
resulting distribution of ground motion intensity, (e) Combination of the above information.
(Baker, 2008) ...........................................................................................................................14
Figure 10. Seismic Hazard map of Italy (Uniurb.it, 2015) ......................................................15
Figure 11. Target building performance levels and ranges (Staaleng.com, 2015) ..................16
Figure 12. Fragility curves for wood- frame building ( Kircher and McCann,1983)..............16
Figure 13. European Seismic Hazard Map showing active faults in the Euro-Mediterranean
Region with earthquake history from 1000 to 2007 (Share-eu.org, 2015) ..............................18
Figure 14. A modular approach in Cat Modelling adapted from Dlugolecki et al. 2009 (Lloyds)
..................................................................................................................................................20
Figure 15. Phenomenon of liquefaction before and after the liquefaction event (ECP, 2015) 21
Figure 16. Comparison of soil state before and after the earthquake in liquefiable soil
(Encyclopedia Britannica, 2015) .............................................................................................22
Figure 17. Damages seen due to liquefaction caused by the Sichuan Earthquake (Chen et al.
2008) ........................................................................................................................................23
Figure 18. Tilted apartment buildings at Kawagishi cho, Niigata, Japan, due to liquefaction.
(Geomaps.wr.usgs.gov, 2015) .................................................................................................24
Figure 19. Examples of Sand Boils (Arca, 2015) ....................................................................24
Figure 20. Flow Failure at the western edge of Lake Merced in San Francisco, 1957 Daly City
Earthquake (Geomaps.wr.usgs.gov, 2015) ..............................................................................25
Figure 21. Lateral Spreading induced failures (Eeri.org, 2015) ..............................................25

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Figure 22. Ground oscillation time histories computed from surface and downhole
accelerograms and excess pore-water pressure ratio recorded during an occurrence in Wildlife
Array, California (Holzer and Youd, 2007).............................................................................26
Figure 23. Overturning of apartments due to liquefaction in Niigata, Japan after the 1964
Niigata earthquake (Nisee.berkeley.edu, 2015).......................................................................26
Figure 24. Uplift of sewer due to liquefaction, 2004 Chuetsu Earthquake..............................27
Figure 25. 0.3m ground settlement around a ferry terminal on Port Island after the 1995 Kobe
Earthquake (Geerassociation.org, 2015)..................................................................................27
Figure 26. Liquefaction susceptibility using plasticity charts (Seed et al., 2003) ...................29
Figure 27. CTI map of Switzerland taken as snapshot from ArcGIS. The darker shade colours
show low value of CTI showing Crests and Ridges while lighter coloured regions show
drainage depressions giving higher values of CTI...................................................................32
Figure 28. Occurrences of Liquefaction around the Balkans, Aegean and Mediterranean Seas
and Western Turkey (Papathanassiou et al., 2005)..................................................................33
Figure 29. Building sunk in the lake due to the settlement of the soil under liquefaction
(Nap.edu, 2015) .......................................................................................................................34
Figure 30. Building tilted in Adapazar due to differential settlement (Ideers.bris.ac.uk, 2015)
..................................................................................................................................................34
Figure 31. Seismic Hazard map of Europe extracted from Global Seismic Hazard Assessment
Program (GSHAP, 1999).........................................................................................................35
Figure 32. Average Population Density between 2005 and 2013 (Bogdan Antonescu, 2014)36
Figure 33. Above illustrated is the Vs30 Map of Southern Europe using the Global Vs30 Map
Server (Earthquake.usgs.gov, 2015) ........................................................................................37
Figure 34. Snapshot taken from the ArcGIS tool showing CTI of Europe. The black colour
represent low values of CTI and as the colour moves towards white, the CTI value increases.
..................................................................................................................................................39
Figure 35. GDP per capita in US Dollars for Europe for 2014 (Knoema, 2015) ....................40
Figure 36. Depiction of an Artificial Neuron (Ramhormozian et al., 2013) ...........................42
Figure 37. Map of Liquefaction Susceptibility of the 112 cities of Europe. The cities marked in
red circle are the cities that have gone under liquefaction in the past or have studies and tests
done showing that it is susceptible...........................................................................................67
Figure 38 Map illustration of Liquefaction Risk Potential Assessment done on the 112 cities of
Europe......................................................................................................................................72

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List of Tables
Table 1. Model Building classes given in HAZUS99 (FEMA, 1999) (Source: Rossetto T.)..17
Table 2. Classification of soil liquefaction consequences after Castro 1987 (Rauch and Martin
III, 2000) ..................................................................................................................................28
Table 3. Liquefaction Severity (Hozler et al., 2003) ...............................................................31
Table 4. Coefficient and there values defined for Global model by Zhu et al. 2014...............33
Table 5. Subsoil Classification for shear wave velocity (Vs30) (British Standards 2005)......38
Table 6. Human Development Index (HDI) ranking of countries for 2013 (Source: UNDP).41
Table 7. PGA values of the marked cities using GSHAP Maps..............................................44
Table 8. Population data of the marked Cities for assessment. ...............................................48
Table 9. Vs30 values imported from USGS Vs30 Global Server............................................52
Table 10. CTI values obtained for the cities from the USGS Earth Explorer Maps................56
Table 11. City GDP data of the assessed cities........................................................................60
Table 12. Calculation of Probability of Liquefaction for the marked Cities. ..........................64
Table 13. Top 20 Cities that resulted with high values P[Liq] ................................................68
Table 14. MCDM Analysis for Liquefaction Risk Potential...................................................69
Table 15. Number cities of the selected countries with liquefaction risk potential.................76

Identifying Regions with High Liquefaction Potential Close To Large Populations in Europe
Syed Ali Hamza Naqvi Page 1
1 Introduction
1.1 Introduction
Earthquakes have been considered as one of the most destructive forces of nature resulting in
massive observable damage. They are one of the major hazards that cause massive casualties,
damage to structures and financial losses. The amount of losses that this force of nature results
annually is tremendous. International Federation of Red Cross and Red Crescent Societies have
reported average annual death of 50,184 people over a period from 2000 to 2008 just by
earthquakes. For Nepal 25th
April 2015 Earthquake alone, the casualties were around 9000 with
17,900 injured in Nepal only (Myrepublica.com, 2015). Around 500,000 houses destroyed with
another 270,000 houses damaged, and the financial loss of assets estimated to 5 Billion Dollars.
Figure 1. Yearly Direct Economic Losses for the past 111 years (Daniell, 2012)
In order for regions to be prepared for such disasters, Catastrophe modelling is done in order
to assess what regions are susceptible to such risks in the future. Catastrophe modelling is
basically a modelling software solution that uses historic records of hazards in the region, sees
the vulnerability of the region and analyses the amount of exposure of the region to the hazard.
It has to be understood that the losses are not directly correlated to the intensity of the hazard.
The risk of losses is based upon 3 basic factors; Hazard, Vulnerability and Exposure. On the

basis of these inputs, the software simulates the level of risk of the region for a potential hazard
and determines the possible amount of losses in terms of human casualty, structural damage
and financial loss. With such simulations the authorities of the region are able to assess possible
losses in future and take preventive actions accordingly in order to minimize the level of threat
to their maximum capacity.
Models are created for various hazards; floods, hurricane/cyclones, earthquakes and even
manmade hazards such as pandemics, terrorism etc. Example of one such firm that develops
such models is AIR Worldwide, who has already generated earthquake models at a global level.
As explained before, such models require detailed historical events and their corresponding
facts and figures of the losses in terms of human, structural and financial. Along with it, details
of how developed the region was is also taken into consideration as this is one main proxy to
show vulnerability and exposure of the region. Further explanation of understanding
Catastrophe models will be given in the literature review.
The damages of earthquake can be categorized in primary and secondary. Primary being the
damage done purely by the shaking of the ground leading to damage and collapse of structures.
The secondary damages are basically the hazards that are triggered directly or indirectly by
shaking of the ground such as tsunami, fire, landslides, avalanche and soil liquefaction.
Figure 2. Epicentres of Earthquake induced Liquefaction (Source: AIR Worldwide)

As aforementioned, liquefaction is one of the main secondary effects of the earthquake and
results in major damages. The Nigata, Japan earthquake of 1964 was one such case where one-
third of the city subsided by soil liquefaction by as much as 2 meters. Another example of
liquefaction damage can be seen in the Izmit, Turkey earthquake of 1999 where a vast regions
underwent soil failure thus resulting in various differential settlements of structures, roadworks
and pipe line damages.
In soil liquefaction, the soil basically loses its strength thus resulting in reduced bearing
capacity. Due to reduction in bearing capacity, the structures on top of the soil don’t remain
stable and thus sink in thus resulting in structural damages or collapse. Liquefaction poses a
serious hazard to infrastructure and must be assessed in areas where soil deposits are prone to
liquefaction, as the bearing capacity of soil is correlated to strength which in turn withstands
foundation loads (Kumar et al., 2012). The most common types of failure associated with soil
liquefaction caused by earthquakes includes failure of retaining walls due to increased lateral
loads from liquefied soil backfill or loss of support from liquefied foundation soils, buoyant
rise of buried structures, flow failures of soil mass on steep slopes, ground oscillation where
liquefaction of a soil deposit beneath a level site leads to back and forth movements of intact
blocks of surface soil, ground settlement, often associated with some other failure mechanism,
loss of bearing capacity causing foundations failures, sand boils etc. Various semi-empirical
formulations have been derived in the last few decades, to quantify the potential hazard as a
function of strong ground motion parameters and previously collected data from historical
earthquakes for performance-based seismic design in liquefied zone for areas that are
seismically active. This report explains recent developments in this area in relation with
liquefaction potential analysis and the methods implemented to carry out the analysis.
The purpose of this report is to perform a Multi Criteria Decision Making (MCDM) analysis
in order to find regions that are at pose a potential risk from liquefaction in amongst large
populated regions of Europe.
1.2 Project Overview
For this project, all major cities of Europe were identified and were ranked to the level of risk
they were exposed to with respect to liquefaction. The parameters taken into consideration for
ranking these cities is based upon population, gross domestic product (GDP), human
development index (HDI) and probability of liquefaction in those cities. This dissertation

comprises of the literature review, methodology, results and the discussion on it, and finally
the concluding remarks for the approach used.
The literature gives a brief introduction about earthquake and earthquake engineering, which
leads to a detailed explanation of seismic risk assessment as the concept for risk assessment for
liquefaction would be done using a similar approach. Seismic risk assessment would further
explain the methodology as to how it is done focusing mainly on the seismic hazard analysis
the result from this analysis is a key parameter for calculating the probability of liquefaction.
Further a thorough understanding of catastrophe models will be given as this project is basically
upon that principle. Finally an understanding of liquefaction and liquefaction susceptibility will
be explained.
The methodology will show the approach used to create this model for Europe and then the
results will be shown and explained to give a better understanding of the liquefaction potential
in the selected regions.
As the frequency of natural disaster grows, so does the total cost of the losses. This has led to
co-operation of government, insurance, and emergency management sectors working together
to reduce the losses incurred by these catastrophes. With the help of such models, hazard
potentials can be quantified to help anticipate the probable losses and use contingency plan to
reduce these losses to their maximum potential. The information derived from such models
will help provide a better understanding of the geographical distribution, frequency, and
magnitude of potential future losses.

2 Project Objectives
2.1 Aim
To perform a Multi Criteria Decision Making (MCDM) analysis to identify regions of large
population in Europe that are susceptible to liquefaction due to earthquakes.
2.2 Purpose
Figure 3. Liquefaction Potential Map of Salt Lake County, Utah (Geology.utah.gov, 2015)
As seen in figure 3, a liquefaction potential map is shown for a county in Utah, USA. In order
to do catastrophe modelling for liquefaction for the whole of Europe, such data is required to

be generated from every part of the European region. This would involve a large scale survey
and data collection, to identify and study the soil types present in the areas of interest prone to
liquefy by doing numerous in-situ site tests. This dissertation focuses to identify regions of
high liquefaction potential close to largely populated areas in Europe by using an easier
approach without the aid of field experiments. Using empirical formulas derived by Zhu et
al.2014 in the paper ‘A geospatial liquefaction model for rapid response and loss estimation’,
where the parameter of Compound Topography Index (CTI) is used, along with the shear wave
velocity, and peak ground acceleration, the liquefaction hazard is quantified and then other
parameters like HDI, GDP and population are used to conduct a liquefaction potential
assessment in high seismically active regions of Europe.
2.3 Objectives
To achieve the project aim, the following objectives will be completed:
 To identify regions of high seismicity in Europe that are a potential hazard to
liquefaction.
 To determine major cities /high population density cities of Europe.
 To identify the soil types present in the selected cities of Europe prone to liquefy.
 To collect input data such as soil maps, strong ground motion data, hydrological
parameter (CTI) for liquefaction potential assessment.
 Using Multi Criteria Decision Making analysis to rank the level of risk in the selected
high population cities of Europe.
2.4 Scope
The scope of the project is to identify regions of high liquefaction potential close to large
populations of Europe. This involves a Multi Criteria Decision Making analysis using the
parameter of Peak Ground Acceleration (PGA), Shear Wave Velocity at a Depth of 30 meters
(Vs30), Compound Topography Index (CTI), Population of the City, Gross Domestic Product
of the city, and Human Development Index of the City. After doing the analysis, the cities will
be ranked in order showing region of Extreme, High, Medium and Low regions of Liquefaction
potential.

3 Literature Review
3.1 Earthquakes
Earthquakes are a force of nature that results in catastrophic damage. The phenomena, occurs
due to the movement of the tectonic plates when the earth’s crust slides across or upon its
components which causes snapping of the ground strata (elastic rebound theory). This results
in a massive release of energy in the form of seismic waves which propagate towards the
surface causing ground shaking. The regions where the earth’s crust is prone to such movement
is called a fault zone.
Figure 4. Generation of Earthquakes

Figure 5. Elastic Rebound Theory (Comet.earth.ox.ac.uk, 2015)
The damages, as stated earlier, can be categorized as primary damages and secondary damages.
Primary damages are defined as the damages done due to the ground shaking. This would
include the damages of buildings, bridges, roads, utility lines, coastal structures and/or
infrastructure, in terms of partially damaging them by cracking the structure or by total collapse
of it. Primary damages also include changing the topography of the region i.e. fracturing ground
surface and hills/mountains. Secondary damages are defined as damages done by events or
phenomena that are initiated by the ground shaking. Such phenomenon would be tsunamis,
fire, avalanches, rock falls, landslides and Soil Liquefaction.
The energy of the earthquake can be calculated in terms of Magnitude. Magnitudes are based
on a logarithmic scale. The energy can be compared with the energy released when blowing up
TNT. A magnitude 1 energy release equals to 6 ounces of TNT explosion, whereas a magnitude
8 energy release equals to 6 million tons of TNT explosion.
Figure 6. Equivalent Energy release by earthquakes (seismo.berkely.edu, 2015)

There are multiple types of magnitude that can be calculated; Richter Magnitude (ML), Body
wave Magnitude (Mb), Surface wave Magnitude (Ms) and Moment Magnitude (Mw). Now days
Mw is more generally used for calculations due to limitations of the initial 3 types of magnitude
in terms of saturation.
3.2 Earthquake Engineering
“Earthquake Engineering is the application of civil engineering to reduce life and economic
losses due to earthquake” (Tiziana Rossetto, UCL).
This scientific field aims to work around protecting or/and limiting the risks to the socio
economic factor of region that is prone to seismic activity. This could be either to society, man-
made environment or natural environment, or to all of them. Because of the catastrophic nature
of earthquakes, this engineering scopes to reduce losses, physically and financially by either
foreseeing such hazards to regions and by predicting the possible losses, or by designing
structures to be able to perform under seismic conditions accordingly so as to minimize the
losses.
The main aim of this field is to:
 Predict the possibility of a strong earthquake and its consequences in the predicted
region with respect to physical and financial losses (Also known as Seismic Risk
Assessment).
 Design, construct, retrofit and maintain structures so that they can perform accordingly
to the expectations of the building codes.
The foremost important parameter in earthquake engineering is Peak Ground Acceleration
(PGA). Ground Acceleration is a measure of the acceleration of the ground shaking caused by
the earthquake and PGA corresponds to the highest value of the ground acceleration generated
during the recorded motion of the seismicity. Accelerographs are generally the instruments
used to record the ground motion of the earthquake and the results from these instruments are
called accelerograms.

Figure 7. Ground motion recording on an accelerograph for the El Centro Earthquake, 1940 (Vibrationdata.com, 2015)
PGA is calculated in m/s2
, ‘g’ (acceleration due to Earth’s gravity) where 1 g = 9.81 m/s2
or
Gal, where 1 Gal = 0.01 m/s2
. Out of these three, the most commonly used is ‘g’.
3.3 Seismic Risk Assessment (SRA)
In order to understand SRA, first we need to understand what Seismic Risk is. Seismic risk is
the probability of harm to an entity, be it human, materialistic or system that may occur in a
specific period of time. It can be expressed in a qualitative expression as follows:
𝑆𝑒𝑖𝑠𝑚𝑖𝑐 𝑅𝑖𝑠𝑘 = 𝑆𝑒𝑖𝑠𝑚𝑖𝑐 𝐻𝑎𝑧𝑎𝑟𝑑 × 𝑉𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑦 × 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒
Seismic Hazard is the probability of a strong earthquake effect occurring at a site within a given
period of time. This term is expressed as relationship between the level of seismic effect and
the corresponding probability of its occurrence.
Vulnerability is defined as the possibility of damage occurrence in structures, potential human
loss and/or financial loss in the assessed area when exposed to a particular earthquake effect.
This is generally represented in the form of fragility/vulnerability curves which show the
relationship between the level of earthquake effect and the level of damage/loss of either one
of the previously mentioned entities.

Exposure is a quantification of the entities in the assessed area. This includes the people and
buildings, the number and type of important infrastructures and the amount of industrial and
commercial activities. (Tiziana Rossetto, UCL)
To understand it further, for the sake of example, if a very strong earthquake, say M = 8 is to
occur in a very remote region where there is negligible population and infrastructure, meaning
a very high seismic hazard in a region with extremely low vulnerability and exposure, this
would result in a very low Seismic risk. On the other hand if an earthquake with M = 6 is to
occur in a very populated region with heavy infrastructure and concentrated buildings and the
buildings don’t conform to the seismic code, the seismic risk in that region will be very high.
With the introduction given to what seismic risk is, seismic risk assessment is the evaluation
of the region prone to possible strong earthquakes and assess the exposure and vulnerability of
the region under a given hazard for potential losses. SRA comprises of three components:
 Seismic Hazard Analysis
 Seismic Vulnerability Assessment
 Exposure Assessment
3.3.1 Seismic Hazard Analysis
Seismic hazard is defined as any physical phenomenon, such as ground shaking or ground
failure, which is associated with an earthquake and that, may produce adverse effects on human
activities (P. Anbazhagan).
Seismic hazard analysis aims to calculate the probability of ground shaking hazard at a
particular site given one or more earthquakes. This could be analysed either deterministically
or probabilistically. The parameter on which the seismic hazard analysis is dependent at a
location are:
 Magnitude of the Earthquake
 Distance from the site to the source
 Return period of the earthquake
 Duration of the ground shaking
As mentioned before, Seismic hazard analysis can be conducted by two approaches;
Deterministic Seismic Hazard Analysis (DSHA) and Probabilistic Seismic Hazard Analysis
(PSHA).

3.3.1.1 Deterministic Seismic Hazard Analysis (DSHA)
DSHA is the simpler approach out of the previously two mentioned approaches for seismic
hazard analysis. This process is easy to use in regions where tectonic features are quite active
and defined. This approach targets on generating discrete, single –valued event or models of
motion at the site, also referred as the Maximum Credible Earthquake (MCE) motion at the
selected location.
Deterministic Seismic Hazard Analysis is done by following the four main steps:
 Identification and Characterization of all sources of earthquakes that produce
significant ground motion at site
 Selection of Source-site distance parameter for the respective zones
 Selection of Controlling Earthquake in terms of magnitude, source-site distance and
ground motion
 Definition of hazard using the controlling earthquake
Figure 8. The 4 steps of Deterministic Seismic Hazard Analysis (Kramer, 1996)

Deterministic Seismic Hazard Analysis are generally carried out when a specific earthquake
event is considered for a structure. More specifically when clients require their structure to be
able to withstand an earthquake of a specific intensity, it is then that this approach is used so
that the structure is able to resist strong ground motions. Mostly the structures are power plants,
nuclear plants, dams or any critical structure.
Since DSHA results give out the MCE, it is more of a conservative approach when considering
to do the assessment for structures in a region, since it considers the maximum earthquake that
the fault is capable of generating and is assumed to occur on the fault closest to the location
site.
3.3.1.2 Probabilistic Seismic Hazard Analysis (PSHA)
PSHA is a more advanced approach compared to its predecessor technique, DHSA. This
approach does not operate on the controlling earthquake but rather uses the probabilistic
concept where uncertainties in the size, location and rate of recurrence of the earthquake are
considered also taking into account the variations of ground motion characteristics with
earthquake size and location to be explicitly considered for the assessment of seismic hazard.
This approach provides results of the likelihood of earthquake ground shaking that the site will
experience and also the probability of its occurrence.
Probabilistic seismic hazard analysis is made of the basic 5 steps, which are:
 Identification of all earthquake sources that are capable of producing damaging ground
motions
 Characterization of the distribution of the rates at which various earthquake magnitudes
are expected to occur.
 Characterization of the distribution of source to site distances that are associated with
potential earthquakes.
 Prediction of the resulting distribution of the intensity of the ground motions as a
function of earthquake magnitude, distance, etc.
 Combination of the uncertainties in earthquake magnitude, location and ground motion
intensity by using total probability theorem.

Figure 9. Five basic steps for probabilistic seismic hazard analysis. (a) Identification of earthquake sources, (b)
Characterization of the distribution earthquake magnitude from each sources, (c) Characterization of the distribution of
source to site distances, (d) Prediction of the resulting distribution of ground motion intensity, (e) Combination of the above
information. (Baker, 2008)
The result of PSHA is given in terms of a value of probability of exceedance. Probability of
exceedance is defined as the probability that at least one event will occur that will equal or
exceed the specified threshold criteria during a considered period of time. For seismic hazard,
it would be the probability that at least one earthquake will occur whose shake intensity will
create ground motions equal or would exceed the ground motion threshold of the assessed
structure during its designated life. For example, in the seismic design code, the ground motions
for residential structures are assigned with a 10% probability of exceedance in 50 years. This
implies that the threshold ground motion value has a 10% probability to occur in 50 years in a
specific location.

Since regions have varying seismicity, therefore the threshold ground motion value with 10%
probability of exceedance in 50 years, also varies. Regions located close to very high seismic
activity have a higher value of ground motion threshold, thus showing that region is exposed
to high seismic activity. A way of representation of such values on a map can be done by
seismic hazard maps, where contours of peak ground acceleration values corresponding to the
probability of exceedance are drawn.
Figure 10. Seismic Hazard map of Italy (Uniurb.it, 2015)
Figure 10 represent the seismic hazard map of Italy with 10% probability of exceedance in 50
years. The ranging contour shows the level of PGA in the region. The higher the PGA value,
the more seismically active the location is.
3.3.2 Seismic Vulnerability Assessment
Seismic vulnerability assessment is a comprehensive engineering study to evaluate
susceptibility of structural system to potential damage from seismic shaking based on the
performance objectives established by the client, and using methods, which generally follow
guidelines presented in FEMA 356 Seismic Rehabilitation of Buildings (Source: Staaleson
Engineering).

Figure 11. Target building performance levels and ranges (Staaleng.com, 2015)
Structural vulnerability assessment is done by analysing a structure subjected to increasing
severity ground motion and observing the damage to the structure in terms of damage states.
The most common representation of damage states is done through vulnerability (fragility)
curves. These fragility curves are based on the different building classes. Building classes are
defined differently by various organizations but generally they are very similar to each other.
One example is shown in Table 1 which is given in HAZUS99 (FEMA 1999).
As aforementioned, fragility curves are based upon various building classes, which shows
damage states of various structural classes. This curve comprises of a set of relationships
between ground motion and the probability of exceedance of certain thresholds of damage.
Figure 12. Fragility curves for wood- frame building (Kircher and McCann, 1983)

Table 1. Model Building classes given in HAZUS99 (FEMA, 1999) (Source: Rossetto T.)
3.3.3 Exposure Assessment
It is defined as the process of estimating and measuring the intensity and time period of the
exposure to an element. In respect to disasters, exposure would be measuring the intensity and

frequency of the hazardous event to the elements, which would be the population, structures
and systems exposed in the path of the event. Exposure could vary on the element depending
upon where the elements lie when the event strikes. In case of a tsunami, assuming the location
is based in a hilly region, the population and structures at the base of the hill would be more
compared to the elements based on top of hill.
The assessment is done based upon the nature of the event and what elements it poses a risk to.
3.4 Earthquakes in Europe
Earthquakes in Europe have been recorded to as old as 580 B.C but more detailed records of
earthquakes started from mid-16th
century. Europe has experienced around 150 earthquakes
with magnitude greater than 6.0 since the 1900 till date (depth of epicentre to maximum 20
km). (USGS). The following figure shows the seismic hazard map of Europe based on
earthquake catalogue from 1000 to 2007.
Figure 13. European Seismic Hazard Map showing active faults in the Euro-Mediterranean Region with earthquake history
from 1000 to 2007 (Share-eu.org, 2015)

The most seismically active region in Europe are South and South Eastern region of Europe.
Italy, Greece and Turkey are famously known for their earthquake history. The Izmit, Turkey
Earthquake of 1999 was major earthquake that resulted in massive catastrophe. Other than that,
Turkey has experienced over 14 major earthquakes since 1945 till 2011, out of which 13 of
them had casualties with at least 1000 people (Reuters, 2007).
Greece is also known as one of the most earthquake-prone countries in Europe. The biggest
earthquake experienced by Greece occurred on Crete’s island in 365, which had a magnitude
of 8.3. Another major earthquake experienced by Greece was on Cephalonia Island with a
magnitudes of 7.2 in 1953 which came with major destructive secondary effects such as
liquefaction.
Italy is also known to have experienced numerous catastrophic earthquakes. It is also first in
Europe which records the highest fatalities and economic losses mainly from earthquakes.
Reggio di Calabria and Messina were affected dramatically by Messina’s earthquake of 1908
with moment magnitude of 7.1. This earthquake led to the casualty of around eighty thousand
people (Britannica, 2014). Furthermore, the Emilia-Romagna earthquake in Italy caused 15.8
billion (in USD) losses, 7 fatalities and 5,000 structural failures on 20 May 2012 (AON, 2013).
3.5 Catastrophe Models
Catastrophe Models are computer simulated calculations with the aim to help estimate the
likelihood and severity of a potential future catastrophe and to mitigate the effects of the hazard
by preparing for its financial impact. They are designed to calculate where future events can
occur, how big it will be, how frequent will it be, and the potential damages and insured losses
that will be created.
Catastrophe modelling, also referred as ‘cat modelling’ came into being in the last 27 years
when the three recognized firms, namely AIR (1989), RMS (1988) and EQECAT (1994),
understood the importance of combining the elements need to estimate catastrophe losses. An
organization’s financial viability is easily affected by natural disasters such as earthquakes,
floods, tsunami’s etc. and even man-made catastrophes for example explosion of an industrial
plant due to human error. The purpose of these models is to calculate the possible financial
damages in occurrence of such hazardous events with the help of probabilistic damage
estimation. Simultaneously, by conducting these losses, it also helps analyse the level of risk
in the insurance industry.

Due to increasing population densities and property values in hazard prone areas, Catastrophe
modelling is being extensively used. Government, insurers, reinsurers and other financial
entities are using this approach on a large scale to understand the possible threat to financial
losses in their respective sectors.
Cat Models are developed using historical data and building upon existing data of the region
in terms of hazards, vulnerability and exposure, and then these model are continuously
upgraded by incorporating the lessons learnt in the past. It is one of the many tools used to
enhance the understanding and management of risk.
Figure 14. A modular approach in Cat Modelling adapted from Dlugolecki et al. 2009 (Lloyds)
These models are comprised of hazard history and its frequency, population density and
infrastructure data in the assessing region, and financial worth of the entities in the region as
input data. Using this input, the model simulates the hazard’s frequency and intensity, which
is the Hazard Module. The engineering module of the model estimates the damages using the
exposure, vulnerability and policy information of the region. Finally the Financial module
calculates the financial damages to the entities and also the insured losses. The output results
are in the form of financial loss distribution in terms of Probability of Exceedance, Tail Value
at Risk, and/or Average Annual Loss.

Implementation of Catastrophe models are now becoming standard practice in industries for
certain perils but it needs to be reasonable and ensured that the models are used for appropriate
purposes.
3.6 Soil Liquefaction
This is a phenomena where the soil reduces its strength and stiffness due to shaking of the
ground by an earthquake. In simple words, the solid soil behaves temporarily as a viscous
liquid.
The definition of soil liquefaction is the transformation “from a solid state to a liquefied state
as a consequence of increased pore pressure and reduced effective stress” (Definition of
terms…” 1978). This is more visible in saturated cohesion less soils where the shaking of the
ground results in rearrangement of the soil grains where they become more densely packed
thus reducing the voids between the grains. With the reduction of the voids, the water in the
pores are forced out. In the presence of drainage, the excess water would drain out of the pores
thus consolidating the soil and increasing more soil strength. In reality, soils with high water
table do not have the option of drainage thus pushing the water upwards which results in
increasing the pore water pressure. With increased pore water pressure, the transfer of stress
changes from the soil skeleton to the pore water eventually leading to a decrease in the effective
stress and shear resistance of the soil. Under these circumstances, if the shear resistances
decreases below the loading stresses, the soil will show large deformation, that is, liquefy.
Figure 15. Phenomenon of liquefaction before and after the liquefaction event (ECP, 2015)

A simple example scenario would be if there is a building standing on top of cohesion less soil
with high water table. In its original state, the bearing capacity of the soil is strong enough to
resist the loading capacity of the building. In case an earthquake hits the region where the
building is located, the ground shaking will cause the soil to rearrange and try to pack more
densely. The rearrangement would result in the reduction of the voids and with a high water
table in the ground, the water in the pores would be pushed out upwards. This would result in
the increase in pore water pressure and reduction of the effective stress of the soil column,
ultimately leading to the reduction in the bearing capacity of the soil. If the bearing capacity
drops below the loading capacity of the building, the soil would undergo failure resulting in
the soil to majorly deform (liquefy) and building would either experience settlement due to
punching failure of the soil or collapse due to overturning failure.
Due to the reduction in the strength and stiffness of the soil, the loading capacity of structures
become higher than the bearing capacity of the soil thus rendering the structure to either
experience complete settlement, differential settlement or overturning of structures.
Figure 16. Comparison of soil state before and after the earthquake in liquefiable soil (Encyclopedia Britannica, 2015)

Although above is a brief explanation of a ground failure mechanism termed as liquefaction, a
better classification system to define ‘soil liquefaction’ has been suggested by Robertson et al.
(1994). It is summarized as follows:
 Flow Liquefaction: This is in the case of undrained flow of contractive, saturated soil
where the residual strength of the soil is falls below the static shear stress. Cyclic or
monotonic shear loadings can trigger such failures.
 Cyclic Softening: In this the soils experiences large deformations during cyclic shear
due to the build of pore-water pressure which results in dilation in undrained,
monotonic shear.
Further classification of cyclic softening is as;
o Cyclic Liquefaction: This occurs when cyclic shear stresses exceed initial static
shear stress which lead to stress reversal. During this, the soil experiences zero
effective stress which results in large deformation
o Cyclic mobility: In this case the cyclic shear stresses don’t exceed the initial
static shear stress thus no zero effective stress condition is produced. Although
small deformation accumulates in each cyclic loading.
The above mentioned system defines various mechanisms for ground failure yet in general
usage, the term liquefaction still describes the failure of cohesion less saturated soils during an
earthquake.
Figure 17. Damages seen due to liquefaction caused by the Sichuan Earthquake (Chen et al. 2008)

Figure 18. Tilted apartment buildings at Kawagishi cho, Niigata, Japan, due to liquefaction. (Geomaps.wr.usgs.gov, 2015)
Types of ground failure that has been identified by liquefaction has been listed by the National
Research Council (Liquefaction… 1985) in eight possible ways when associated with
earthquakes.
 Sand boils, where water content in the soil, under pressure, wells up through a bed of
sand. The damage done is relatively minor in this mechanism.
Figure 19. Examples of Sand Boils (Arca, 2015)
 Flow Failures of Slopes, in which the soil mass flows down due to steep slopes.

Figure 20. Flow Failure at the western edge of Lake Merced in San Francisco, 1957 Daly City Earthquake
(Geomaps.wr.usgs.gov, 2015)
 Lateral Spreading, is the spreading of soil on normally gentle slopes, resulting in
cracking of the soil on the surface.
Figure 21. Lateral Spreading induced failures (Eeri.org, 2015)
 Ground Oscillation, results when a soil layer underneath the surface layer bed liquefies
and leads to oscillation of intact blocks of surface soil.

Figure 22. Ground oscillation time histories computed from surface and downhole accelerograms and excess pore-water
pressure ratio recorded during an occurrence in Wildlife Array, California (Holzer and Youd, 2007)
 Reduction of Bearing Capacity, due to generation of excess pore water pressure thus
losing the shear strength of the soil, leading to foundation failures.
Figure 23. Overturning of apartments due to liquefaction in Niigata, Japan after the 1964 Niigata earthquake
(Nisee.berkeley.edu, 2015)
 Buoyant rise of buried structures such as underground tanks and pipes due to the
upward thrust of water pressure in the soil.

Figure 24. Uplift of sewer due to liquefaction, 2004 Chuetsu Earthquake
 Ground Settlement, where the soil consolidates and sinks in.
Figure 25. 0.3m ground settlement around a ferry terminal on Port Island after the 1995 Kobe Earthquake
(Geerassociation.org, 2015)
 Failure of retaining wall, due to increased lateral pressure from liquefied backfill soil
or due to the reduction of support from liquefied foundation soils.
In most cases, not much damage is observed in liquefaction cases where there is no presence
of static shear loads as they are generally the reason to result in large deformations of soil.

Table 2. Classification of soil liquefaction consequences after Castro 1987 (Rauch and Martin III, 2000)
3.7 Liquefaction Susceptibility
Not all soils are susceptible to liquefaction and therefore the first step to liquefaction hazard
assessment is to evaluate liquefaction susceptibility. In order to assess whether the region is
susceptible to liquefaction, some essential parameters are required for that. Soil conditions, soil
type, ground water level and level of ground shaking are the important parameters needed for
the determination of liquefaction susceptibility.
Since development of excess pore water pressure is required in the soil for liquefaction to occur,
liquefaction susceptibility is influenced by the compositional characteristics that influence
volume change behaviour and the presence of water voids. Therefore the soils that are above
the groundwater table at a site are not susceptible to liquefaction. For saturated soils below
groundwater table, compositional characteristics associated with high volume change potential
tend to be associated with high liquefaction susceptibility. These characteristic include particle
size, shape and gradation.
Initially it was thought that liquefaction was only limited to sands. This was based upon the
criteria that fine grained soils were incapable of generating high pore water pressure whereas
coarse grained soils were to permeable to sustain any generated pore pressure long enough for
liquefaction to occur (Kramer, 1996).
Liquefaction of non-plastic has been observed (Ishihara, 1984, 1985) in the laboratory and the
field, indicating the plasticity characteristics rather than grain size alone influence the
liquefaction susceptibility of fine grained soils. With recent studies by Bray and Sancio (2006)
and Boulanger and Idriss (2006), they managed to develop a criteria for identifying soils

susceptible to liquefaction. Based primarily on cyclic testing of undisturbed specimens of
Adapazarı silts and clays, Bray and Sancio (2006) found that soils with Plasticity Index (PI) <
12 and water content to liquid limit ratios (wc/LL) > 0.85 were susceptible to liquefaction as
evidenced by a dramatic loss of strength resulting from increased pore water pressure and
reduced effective stress. Liquefaction of fine grained soils is typically manifested as cyclic
mobility with limited flow deformation resulting from a transient loss of shear resistance due
to the development of excess pore water pressures. Boulanger and Idriss (2006) use PI < 7 to
identify soils exhibiting sand like behaviour that are susceptible to liquefaction and PI > 7 to
identify soils exhibiting clay like behaviour that are judged to not be susceptible to liquefaction.
Clay like soil may soften due to the loss of effective stress resulting from the build-up of
positive excess pore water pressures but the term liquefaction is reserved for sand like soils.
Thus different definitions of liquefaction are leading to slightly different liquefaction
susceptibility criteria. However both research groups make it clear that fine grained soils can
undergo severe strength loss due to increased pore water pressures that temporarily reduce the
effective stress in soils.
Liquefaction of gravels has been observed in the field and laboratory. Liquefaction
susceptibility is also influenced by gradation and particle shape. Well graded soils are less
susceptible to liquefaction than poorly graded soil. Also soils with rounded particles are known
to densify more easily than soils with angular grains resulting in higher excess pore water
pressure (Kramer, 1996).
Figure 26. Liquefaction susceptibility using plasticity charts (Seed et al., 2003)

The best approach is using the in-situ site tests to determine the soil type, condition, and the
water level, and using historical seismic data of the region to determine the possible PGA for
the ground shaking. Some of the well-known methods of assessing liquefaction resistance are
as follows:
 SPT based simplified procedure (Bolton, Seed et al,. 1985)
 Shear wave velocity method (Vs) (Andrus et al., 2004)
 Iwasaki’s method (Iwasaki et al,. 1982)
3.7.1 Liquefaction Potential Index
Iwasaki et al. (1978) developed a concept of liquefaction potential index (LPI) to assess the
liquefaction potential of soils.
He defined the liquefaction potential as a function of the following:
1. The thickness of the respective stratum,
2. The proximity of the respective soil layer to the surface,
3. The depths at which has the factor of safety (FOS) smaller than 1.
The factor of safety (FOS) represents the ratio of the anticipated stress induced by the seismic
activity on the soil layer to the resistance strength of the soil layer. Since the effect of
liquefaction in layers deeper than 20m is almost negligible on the structures above that soil
column, the evaluation is thus limited to the initial 20m of the stratum (Iwasaki et al., 1984).
The liquefaction potential index can be calculated using the equation given below:
𝐿𝑃𝐼 = ∫ 𝐹. (10 − 0.5𝑧)𝑑𝑧
20
0
Where;
F = 1 – FOS for FOS less than or equal to 1,
F = 0 for FOS greater than 1.
z = depth of the soil in meters.
Using this approach, Liquefaction Potential Index (LPI) is used as an indicator to determine
the percentage of susceptibility in that soil column.

Table 3. Liquefaction Severity (Hozler et al., 2003)
Liquefaction Severity Little to None Minor Moderate Major
LPI LPI=0 0<LPI<5 5<LPI<15 LPI>15
In order for probabilistic liquefaction assessment, the above mentioned procedure would give
the perfect results but for a very small spatial assessment as the procedure would require
numerous site tests in order to process the results. In order to the assessment at a larger scale,
such as for the whole of Europe, this approach would not be reasonable.
3.7.2 Zhu et al. (2014)
Since it is financially extremely expensive to perform site tests in all location of Europe for
liquefaction susceptibility, Zhu et al. (2014) have developed a simpler approach that can be
used at a global scale. They have devised an empirical function that gives the probability in a
spatial region using the logit link function as follows:
𝑃[𝐿𝑖𝑞] =
1
1 + 𝑒−𝑋
Where X is a function dependant on PGA from ShakeMap estimates from USGS, Shear wave
velocity of the soil at a depth of 30 m from the surface of the layer (Vs30) values from USGS
Global Map Server, and Compound Topographic Index (CTI) which shows the steady state
wetness index of the topography.
Zhu et al. (2014) developed this approach to be able to predict liquefaction probability for use
in rapid response and loss estimation. The focused on identifying broadly available geospatial
variables and seismic specific parameters, so that liquefaction hazard can be calculated globally
without the need of in-situ site test requirements. This model does not explain liquefaction
feature on a site by site scale but rather it works on an aerial extent and helps identify broad
zones of probability of liquefaction. This is the actual purpose of this dissertation that to able
to identify regions with high liquefaction potential, rather than precise site locations.
The parameters used by Zhu et al. 2014, are PGA, Vs30 and CTI. The PGA values used are the
probability of exceedance for a specific time period. For this research, PGA values of Europe
for 10% Probability of Exceedance in 50 years are used. This shows the probabilistic seismic
activity that is expected to occur in the given time period. These PGA values are extracted from
GSHAP Maps.

Shear wave velocity of the soil at a depth of 30 meters from the surface (Vs30), shows the type
of soil strata in that region. Shear wave velocity is a sound gauge of the dynamic properties of
the soil strata as it is directly related to the shear modulus or modulus of rigidity which forms
a good proximity of the stiffness of the soil. This approach is able to assess the stiffness
characteristics of the soil present at location.
Shear Wave Velocity is a good parameter to understand how the soil reacts to the seismic
activity. In cases of loose soils, the earthquake motion when moving from bedrock to the new
layer, causes the wave to be amplified significantly and low values of Vs show the soil being
loose. With higher Vs values show the soil becoming stiffer and harder and therefore in case of
seismic activity, the seismic wave is not amplified in most cases. Therefore Vs30 values are a
good approach to as a soil density proxy derived by Wald and Allen (2007) and these value are
used from the USGS Global Vs30 Server at 30c resolution in Zhu et al. 2014 approach.
Compound Topography Index (CTI) is a hydrological parameter that shows the steady state
wetness index of the soil. It is defined as the natural logarithm of the ratio of contributing area
of the catchment to the tangent flow of the water (Moore et al. 1991). It is used in quantifying
topographic control on soil wetness. This parameter is a function of slope and upstream
contributing per unit width orthogonal to the flow direction. Regions with ridges and crests
show low values of CTI whereas regions with drainage depressions show high CTI values.
Figure 27. CTI map of Switzerland taken as snapshot from ArcGIS. The darker shade colours show low value of CTI
showing Crests and Ridges while lighter coloured regions show drainage depressions giving higher values of CTI.
Zhu et al, 2014 produced two models using this approach; region level and global level. For
this paper, the global model will be used. As stated earlier, the probability of liquefaction will
be calculated using the following equation,
1
1 + 𝑒−𝑋

Where,
𝑋 = 𝛽0 + 𝛽1 𝑥1 + ⋯ + 𝛽 𝑘 𝑥 𝑘
And the above mentioned variables are defined by Zhu et al. 2014, in the table below.
Table 4. Coefficient and there values defined for Global model by Zhu et al. 2014
Global Model
Coefficient (xk) Estimate (𝛽)
Intercept 24.1
ln(PGA) 2.067
CTI 0.355
ln(Vs30) -4.784
Therefore the final equation for X becomes as follows,
𝑋 = 24.1 + 2.067 [ln(𝑃𝐺𝐴)] + 0.355 × 𝐶𝑇𝐼 − 4.784 [ln(𝑉𝑠30)]
The resultant probability of Liquefaction, P[Liq], is given in a manner that shows the possibility
of liquefaction in an area that is highlighted. In this case the results will show the possibility of
liquefaction in the assessed city.
3.8 Cases of Liquefaction in Europe
Figure 28. Occurrences of Liquefaction around the Balkans, Aegean and Mediterranean Seas and Western Turkey
(Papathanassiou et al., 2005)

Numerous cases of liquefaction have been observed around Europe, with the more prominent
regions being Western Turkey, Greece, Italy, Bulgaria, Albania, Montenegro, Macedonia and
Serbia. Figure 28 above shows locations of liquefaction occurrences around the Aegean and
Mediterranean seas, Balkans and Western Turkey.
The 2003 Lefkas, Greece Earthquake, resulted in several structural damages to the port
facilities due to differential settlement. Piers were damaged with one of them being overturned.
Sand boils and ground fissures were observed in the town of Lefkas. Reports of muddy water
being ejected up to a height of 50 cm was observed from the cracks in the pavements by
eyewitnesses, showing the presence of high pore water pressure during the earthquake.
Liquefaction was widely observed after the 1999 Izmit, Turkey Earthquake, along the
earthquake fault break which ran to length of 120 km. Building were shifted toward lake and
sunk as shown in figure 29.
Figure 29. Building sunk in the lake due to the settlement of the soil under liquefaction (Nap.edu, 2015)
The most severe damage was observed in Adapazarı, Turkey, where the buildings were settled,
tilted or totally collapsed. Settlement was observed up to 110 cm in the region.
Figure 30. Building tilted in Adapazarı due to differential settlement (Ideers.bris.ac.uk, 2015)

4 Methodology
4.1 Methodology
Multi-Criteria Decision Making (MCDM) Analysis will used to identify regions of high
liquefaction susceptibility in Europe. Using the simple equation of calculating Risk, i.e.
𝑅𝑖𝑠𝑘 = 𝐻𝑎𝑧𝑎𝑟𝑑 × 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒,
Major cities of Europe will be highlighted that are under severe threat of liquefaction. The
parameters used for the analysis will be Gross Domestic Product (GDP), Human Development
Index (HDI) and Population for the Exposure component. For the Hazard component, the
parameters will be Horizontal Peak Ground Acceleration (PGA), Time-averaged shear-wave
velocity to a depth of 30m (Vs30) and Compound Topographic Index (CTI).
4.2 Peak Ground Acceleration (PGA)
The first stage is to identify regions in Europe with seismicity greater than 0.8 m/s2
. Using the
Global Seismic Hazard Assessment Program (GSHAP) Maps, all regions with PGA greater
than 0.8 m/s2
with 10% probability of exceedance in 50 years, which is a return period of 475
years, are marked down. The countries or regions of the countries that fell in this parameter
were as follows as seen in Figure 31.
Figure 31. Seismic Hazard map of Europe extracted from Global Seismic Hazard Assessment Program (GSHAP, 1999)

 Albania
 Austria
 Belgium
 Bosnia and
Herzegovina
 Bulgaria
 Croatia
 Cyprus
 Czech Republic
 France
 Germany
 Greece
 Hungary
 Iceland
 Italy
 Kosovo
 Macedonia
 Moldova
 Montenegro
 Norway
 Poland
 Portugal
 Romania
 Russia (European
Region)
 Serbia
 Slovakia
 Slovenia
 Spain
 Switzerland
 Turkey
4.3 Population
With the countries highlighted that are affected with the above mentioned seismicity, the next
parameter used is the population of the cities in the highligted seismic region. This parameter
helps identifying which cities are to be taken into consideration for the MCDM analysis. The
conditions used for this parameter were cities with population with atleast 100,000
peopleand/or major cities of the country. The data was complied manually in Excel Spreadsheet
in order to fitler and rank them in the later stages of the process.
Figure 32. Average Population Density between 2005 and 2013 (Bogdan Antonescu, 2014)

For majority of the countries, all the cities that matched the above criteria were considered,
except for Italy, Romania, Spain and Turkey. For the exceptional countries, numerous major
cities were considered rather than finding cities with atleast 100,000 people as the database for
the number of cities would have incerased a lot. Therefore in total, 112 cities were marked out
from this process. The data census used is from www.citypopulation.de .
4.4 Time-averaged shear-wave velocity to a depth of 30m (Vs30)
Shear wave velocity is a sound gauge of the dynamic properties of the soil strata as it is directly
related to the shear modulus or modulus of rigidity which forms a good proximity of the
stiffness of the soil. This approach is able to assess the stiffness characteristics of the soil
present at location. The shear wave velocity is inversely proportional to the total unit weight
of the soil, which hence inverts the level of severity to liquefaction in face value. This means
that lower the value of shear wave velocity (Vs30), higher the susceptibility of the soil strata
to liquefaction. The Eurocode 8 sub-soil classification for ranges of shear wave velocities are
as shown in Table 5 Error! Reference source not found.(British Standard, 2005).
With the cities marked out, in the next stage, the Vs30 maps are used to see the shear-velocity
of the soils in the marked cities. The USGS’s Global Vs30 Map server is used to create Vs30
maps of the selected cities. Since the values of the Vs30 are varying within some of the cities,
the minimum value amongst the range of values present within the city will be used.
Figure 33. Above illustrated is the Vs30 Map of Southern Europe using the Global Vs30 Map Server (Earthquake.usgs.gov,
2015)

Table 5. Subsoil Classification for shear wave velocity (Vs30) (British Standards 2005)
4.5 Compound Topographic Index (CTI)
The Topographic Wetness Index (TWI), also called Compound Topographic Index (CTI), is a
steady-state wetness index. It involves the upslope contributing catchment area, a slope raster,
and a couple of geometric functions. The value of the contributing catchment area for each cell
in the output raster (the CTI raster) is the value in a flow accumulation raster for the
corresponding digital elevation model. Higher CTI values represent drainage depressions,
while lower values represent crests and ridges.
For Liquefaction Assessment, CTI is the hydrological parameter that will be used as per Zhu
et al, (2014). The CTI map is taken from Earth Explorer by USGS where the data is provided
from the HYDRO1K project, whose purpose was to provide users hydrologically correct
Digital Elevation Models along with other complimenting data sets at regional and global level.

With the aid of ArcGIS software, CTI map was extracted and displayed in the software as
shown below. As the cities with their co-ordinates had been located, using ArcGIS, the values
of CTI for the corresponding locations were noted down. It is to be noted that the values of CTI
on the map provided by USGS had to be scaled down by a factor of 100 since pixel values in
a raster data cannot be defined in decimal places.
Figure 34. Snapshot taken from the ArcGIS tool showing CTI of Europe. The black colour represent low values of CTI and
as the colour moves towards white, the CTI value increases.
4.6 Gross Domestic Product (GDP)
GDP is one of the parameters to show the economic stability of a country. Gross domestic
product of a city gives a fine proximity of the potential effect on the economic losses and in
turn help rank cities for importance like wise. With the help of DATABANK via The World
Bank group (World Bank, 2012; Group, 2015), these values were manually obtained. The GDP
per capita for each country was obtained. Using that value, it was multiplied with the population
of each marked city in order to give the GDP of a city.

Figure 35. GDP per capita in US Dollars for Europe for 2014 (Knoema, 2015)
4.7 Human Development Index (HDI)
Since economic growth alone cannot be the criteria to assess the development of a country, the
HDI was created to emphasize that people and their capabilities should be the key parameter
to judge a country’s development. The Human Development Index (HDI) is a summary
measure of average achievement in key dimensions of human development: a long and healthy
life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean
of normalized indices for each of the three dimensions (Human Development Reports, UNDP).
This parameter in whole is a good proxy to help understand the exposure and vulnerability of
a region and therefore is included in the equation of the Liquefaction potential risk analysis.
The three dimensions on which the Human Development Index is dependent on, as stated
before, are health of a human, education, and standard of living. HDI calculates the health
component by assessing life expectancy at birth using a minimum value of 20 years and
maximum value of 85 years. The education component of the HDI is assessed by taking the
mean of years of schooling for adults aged 25 years and expected years of schooling for
children of school entering age. United Nations Educational, Scientific and Cultural
Organization (UNESCO) Institute for Statistics estimated the mean years of on the basis of
educational attainment data from censuses and surveys available in its database. UNESCO
Institute of Statistics have produced an indicator where expected years of schooling estimates
are based on enrolment by age at all levels of education and the years of schooling is capped at

18 years of age. The indicators are normalized using a minimum value of zero and maximum
aspirational values of 15 and 18 years respectively. The two indices are combined into an
education index using arithmetic mean.
The proxy for standard of living is measured by gross national income (GNI) per capita. The
goalpost for minimum income is $100 Purchasing Power Parity (PPP) and the maximum is
$75,000 (PPP). The minimum value for GNI per capita is set at $100, which is justified by the
considerable amount of unmeasured subsistence and nonmarket production in economies close
to the minimum that is not captured in the official data. Using the logarithm of income, HDI
reflects the diminishing importance of income with increasing GNI.
Using geometric mean, the scores for the three HDI dimension indices are then aggregated into
a composite index. Following were the rankings based on the results for the year 2013.
Table 6. Human Development Index (HDI) ranking of countries for 2013 (Source: UNDP)
Country HDI Country HDI Country HDI
Albania 95 Greece 29 Portugal 41
Austria 21 Hungary 43 Romania 54
Belgium 21 Iceland 13 Russia 57
Bosnia and
Herzegovina
86 Italy 26 Serbia 77
Bulgaria 58 Kosovo 77 Slovakia 37
Croatia 47 Macedonia 84 Slovenia 25
Cyprus 32 Moldova 114 Spain 27
Czech
Republic
28 Montenegro 51 Switzerland 3
France 20 Norway 1 Turkey 69
Germany 6 Poland 35
4.8 Ranking Criteria
With the above parameters explained, they will be used in the calculation of Liquefaction Risk.
As defined before,
𝑅𝑖𝑠𝑘 = 𝐻𝑎𝑧𝑎𝑟𝑑 × 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒
The Hazard component comprises of Peak ground Acceleration (PGA), Shear Wave Velocity
till a depth of 30 m from the surface (Vs30) and Compound Topography Index (CTI).
The Exposure component comprises of Population, Gross Domestic Product (GDP) and
Human Development Index (HDI).

PGA, Vs30 and CTI will be combined together using Zhu et al. (2014) equation,
𝑋 = 24.1 + 2.067 [ln(𝑃𝐺𝐴)] + 0.355 × 𝐶𝑇𝐼 − 4.784 [ln(𝑉𝑠30)],
And,
1
1 + 𝑒−𝑋
The function 𝑃[𝐿𝑖𝑞] will show the probability of Liquefaction in that city and this function will
represent the Hazard component of the Risk equation.
Population, GDP and HDI will be multiplied together to present the Exposure component.
The basic principle of Multi Criteria Decision Method (MCDM) analysis is to rank the cities
for importance factor keeping in mind the factors of population density of the city, economic
value of the city, level of development of the city, and the probability of the area of possible
liquefaction. Since these parameters comprise of various base units, they need to be converted
into a single comparable system. For this purpose, the method of Artificial Neural Networks
(ANN) (Ramhormozian et al., 2013) was used as it is the suitable way to correlate different
parameters, where each parameter is normalized and the given different weightage with respect
to importance given to the parameter. A pictorial depiction of an artificial neuron as defined by
Ramhormozian is shown below.
Figure 36. Depiction of an Artificial Neuron (Ramhormozian et al., 2013)
The process of normalizing the parameters has been defined by Yeh (2009) in the equation
below.
𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝑃𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟 (𝑋), 𝑋 𝑛 =
(𝑋𝑖 − 𝑋 𝑚𝑖𝑛)
(𝑋 𝑚𝑎𝑥 − 𝑋 𝑚𝑖𝑛)

In the above equation, Xn denotes the normalized value of the parameter X, Xi is the ‘i’ th
value of the parameter from the list of values of the parameter X, and Xmax & Xmin represent
the minimum and maximum value in the list of values for a parameter X.
For the parameters of P[Liq], Population and GDP of cities, the above normalizing procedure
is used. These parameters are directly proportional to the value of risk. For HDI, this parameter
is inversely proportional to risk. Decrease in HDI gives an increase in risk. Therefore for HDI,
the normalizing equation,
𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝐻𝐷𝐼, 𝐻𝐷𝐼 𝑛 = 1 −
(𝐻𝐷𝐼𝑖 − 𝐻𝐷𝐼 𝑚𝑖𝑛)
(𝐻𝐷𝐼 𝑚𝑎𝑥 − 𝐻𝐷𝐼 𝑚𝑖𝑛)
This results from this equation will then correctly fit into the calculation for Liquefaction Risk.
The weightages were equally divided between the hazard parameter and the exposure
parameter. The exposure parameter was further divided on the basis of trial and error.
Normalized (Inverted) HDI Normalized Population Normalized City GDP Normalized P[Liq]
0.25 0.20 0.05 0.50
Using these weightages, the ranking of the cities for liquefaction potential is done.

5 Data, Results and Discussion
5.1 PGA values from GSHAP Maps
In order to identify the target cities for this study, the minimum seismicity requirement for the
regions and the cities in the region which qualified for the population criteria were
simultaneously considered. This preliminary selection reduced the number of selected cities
from 1056 (population larger than 100,000) to 112 (population larger than 100,000 and PGA
larger than 0.08g. This led to the finalization of the cities and the PGA values correspondingly.
With the help of MATLAB software, the GSHAP Map data was imported into the software
and with the specified coordinates input, the corresponding table with PGA values was
produced. The values of PGA are in ‘g’.
Table 7. PGA values of the marked cities using GSHAP Maps
Country
Country
Abbv.
Co-ordinates (Long,
Lat)
City
PGA
[g]
Albania
ALB 19.818698° 41.327546° Tirana 0.22
ALB 19.461607° 41.332807° Durres 0.26
ALB 20.086640° 41.110236° Elbasan 0.26
ALB 19.562760° 40.727504° Fier 0.28
ALB 20.777807° 40.614079° Korçë 0.21
ALB 19.503256° 42.069299° Shkoder 0.25
Austria
AUT 16.373819° 48.208174° Wien 0.10
AUT 11.404102° 47.269212° Innsbruck 0.12
Belgium
BEL 4.444643° 50.410809° Charleroi 0.13
BEL 5.579666° 50.632557° Liège 0.10
Bosnia and
Herzegovina
BIH 18.413076° 43.856259° Sarajevo 0.19
BIH 17.191000° 44.772181° Banja Luka 0.19
Bulgaria
BGR 23.321868° 42.697708° Sofia 0.23
BGR 24.745290° 42.135408° Plovdiv 0.25
BGR 27.914733° 43.214050° Varna 0.16
BGR 27.462638° 42.504795° Burgas 0.12
BGR 25.965655° 43.835571° Ruse 0.16

BGR 25.634464° 42.425777° Stara Zagora 0.26
Croatia
HRV 15.981919° 45.815011° Zagreb 0.30
HRV 16.440194° 43.508132° Split 0.25
HRV 14.442176° 45.327063° Rijeka 0.18
Cyprus CYP 33.022617° 34.707130° Lemesós 0.31
Czech Republic CZE 18.262524° 49.820923° Ostrava 0.10
France
FRA 1.444209° 43.604652° Toulouse 0.04
FRA 5.369780° 43.296482° Marseille 0.09
FRA 5.724524° 45.188529° Grenoble 0.13
FRA 3.876716° 43.610769° Montpellier 0.09
FRA 2.894833° 42.688659° Perpignan 0.14
Germany
DEU 9.182932° 48.775846° Stuttgart 0.08
DEU 7.842104° 47.999008° Freiburg 0.09
Greece
GRC 23.729360° 37.983917° Athínai 0.17
GRC 22.944419° 40.640063° Thessaloniki 0.29
GRC 21.734574° 38.246639° Pátrai 0.33
GRC 25.144213° 35.338735° Iraklion 0.23
GRC 22.419125° 39.639022° Larissa 0.26
GRC 22.942159° 39.362190° Volos 0.36
Hungary
HUN 19.040235° 47.497912° Budapest 0.09
HUN 20.141425° 46.253010° Szeged 0.08
HUN 20.762386° 48.096363° Miskolc 0.10
HUN 18.232266° 46.072734° Pécs 0.08
HUN 17.650397° 47.687457° Gyor 0.09
HUN 21.724405° 47.949532° Nyiregyhaza 0.07
HUN 19.689686° 46.896371° Kecskemet 0.10
Iceland ISL -21.817439° 64.126521° Reykjavík 0.30
Italy
ITA 12.496366° 41.902783° Roma 0.18
ITA 14.216341° 40.857155° Napoli 0.15
ITA 7.686864° 45.070339° Torino 0.10
ITA 13.361267° 38.115688° Palermo 0.13
ITA 8.946256° 44.405650° Genova 0.13

ITA 11.342616° 44.494887° Bologna 0.21
ITA 11.255814° 43.769560° Firenze 0.19
ITA 15.083030° 37.507877° Catania 0.20
Kosovo KOSOVO 21.165503° 42.662914° Pristine 0.14
Macedonia MKD 21.427996° 41.997346° Skopje 0.15
Moldova
MDA 28.863810° 47.010453° Chisinau 0.15
MDA 27.918415° 47.753995° Balti 0.14
MDA 29.596805° 46.848185° Tiraspol 0.09
Montenegro MNE 19.259364° 42.430420° Podgorica 0.30
Norway NOR 5.322054° 60.391263° Bergen 0.08
Poland
POL 16.284355° 50.784009° Walbrzych 0.08
POL 18.546285° 50.102174° Rybnik 0.08
POL 19.020002° 50.121801° Tychy 0.07
POL 19.058385° 49.822377° Bielsko-Biala 0.10
Portugal
PRT -9.139337° 38.722252° Lisbon 0.14
PRT -8.629105° 41.157944° Porto 0.13
PRT -8.611785° 41.123876°
Vila Nova de
Gaia
0.13
PRT -9.224547° 38.757760° Amadora 0.13
PRT -8.426507° 41.545471° Braga 0.13
PRT -8.440357° 40.204829° Coimbra 0.13
Romania
ROU 26.102538° 44.426767° București 0.21
ROU 23.623635° 46.771210° Cluj-Napoca 0.10
ROU 21.208679° 45.748872° Timisoara 0.19
ROU 27.601442° 47.158455° Iasi 0.25
ROU 28.634814° 44.159801° Constanta 0.10
ROU 23.794881° 44.330178° Craiova 0.10
ROU 25.601198° 45.657976° Brasov 0.30
ROU 28.007994° 45.435321° Galati 0.26
ROU 26.012862° 44.936664° Ploiesti 0.28
Russia
RUS 38.987221° 45.039267° Krasnodar 0.23
RUS 47.512628° 42.966631° Makhachkala 0.34

Serbia
SRB 20.448922° 44.786568° Beograd 0.20
SRB 19.833550° 45.267135° Novi Sad 0.14
SRB 21.895759° 43.320902° Niš 0.13
SRB 20.911423° 44.012793° Kragujevac 0.26
Slovakia
SVK 17.107137° 48.145892° Bratislava 0.11
SVK 21.257800° 48.721005° Kosice 0.10
Slovenia SVN 14.505752° 46.056946° Ljubljana 0.18
Spain
ESP 2.173404° 41.385064° Barcelona 0.12
ESP -5.984459° 37.389092° Seville 0.10
ESP -0.889742° 41.648870° Zaragoza 0.03
ESP -4.421399° 36.721274° Malaga 0.14
ESP -1.130654° 37.992240° Murcia 0.18
ESP -2.934985° 43.263013° Bilbao 0.06
ESP -8.720727° 42.240599° Vigo 0.09
ESP -8.411540° 43.362344° A Coruña 0.09
ESP -3.598557° 37.177336° Granada 0.20
ESP -0.996584° 37.625683° Cartagena 0.11
ESP -0.490686° 38.345996° Alicante 0.17
Switzerland
CHE 8.541971° 47.377085° Zürich 0.08
CHE 6.142296° 46.198392° Genève 0.09
CHE 7.597551° 47.567442° Basel 0.15
CHE 6.633597° 46.519962° Lausanne 0.09
CHE 7.444608° 46.947922° Bern 0.09
CHE 8.737565° 47.499950° Winterthur 0.08
Turkey
TUR 28.978359° 41.008238° İstanbul 0.25
TUR 32.859742° 39.933363° Ankara 0.20
TUR 27.142826° 38.423734° İzmir 0.49
TUR 29.060964° 40.188528° Bursa 0.37
TUR 35.330828° 36.991419° Adana 0.20
TUR 37.378110° 37.065953° Gaziantep 0.32
TUR 32.493155° 37.874643° Konya 0.18
TUR 30.713323° 36.896891° Antalya 0.36

5.2 City Population
As mentioned in the methodology, the population data was collected from
www.citypopulation.de .
Table 8. Population data of the marked Cities for assessment.
Country
Country
Abbv.
Co-ordinates (Long,
Lat)
City Population
Albania
ALB 19.818698° 41.327546° Tirana 800,986
ALB 19.461607° 41.332807° Durres 276,191
ALB 20.086640° 41.110236° Elbasan 301,397
ALB 19.562760° 40.727504° Fier 315,012
ALB 20.777807° 40.614079° Korçë 224,165
ALB 19.503256° 42.069299° Shkoder 218,523
Austria
AUT 16.373819° 48.208174° Wien 1,797,337
AUT 11.404102° 47.269212° Innsbruck 126,965
Belgium
BEL 4.444643° 50.410809° Charleroi 202,480
BEL 5.579666° 50.632557° Liège 195,968
Bosnia and
Herzegovina
BIH 18.413076° 43.856259° Sarajevo 369,534
BIH 17.191000° 44.772181° Banja Luka 150,997
Bulgaria
BGR 23.321868° 42.697708° Sofia 1,228,282
BGR 24.745290° 42.135408° Plovdiv 341,567
BGR 27.914733° 43.214050° Varna 335,949
BGR 27.462638° 42.504795° Burgas 198,725
BGR 25.965655° 43.835571° Ruse 147,055
BGR 25.634464° 42.425777° Stara Zagora 137,729
Croatia
HRV 15.981919° 45.815011° Zagreb 688,163
HRV 16.440194° 43.508132° Split 167,121
HRV 14.442176° 45.327063° Rijeka 128,384
Cyprus CYP 33.022617° 34.707130° Lemesós 101,000
Czech Republic CZE 18.262524° 49.820923° Ostrava 294,200
France
FRA 1.444209° 43.604652° Toulouse 453,317
FRA 5.369780° 43.296482° Marseille 852,516

FRA 5.724524° 45.188529° Grenoble 158,346
FRA 3.876716° 43.610769° Montpellier 268,456
FRA 2.894833° 42.688659° Perpignan 120,489
Germany
DEU 9.182932° 48.775846° Stuttgart 604,297
DEU 7.842104° 47.999008° Freiburg 220,286
Greece
GRC 23.729360° 37.983917° Athínai 3,168,846
GRC 22.944419° 40.640063° Thessaloniki 806,635
GRC 21.734574° 38.246639° Pátrai 195,265
GRC 25.144213° 35.338735° Iraklion 157,452
GRC 22.419125° 39.639022° Larissa 144,651
GRC 22.942159° 39.362190° Volos 130,094
Hungary
HUN 19.040235° 47.497912° Budapest 1,744,665
HUN 20.141425° 46.253010° Szeged 161,921
HUN 20.762386° 48.096363° Miskolc 161,265
HUN 18.232266° 46.072734° Pécs 146,581
HUN 17.650397° 47.687457° Gyor 128,902
HUN 21.724405° 47.949532° Nyiregyhaza 118,164
HUN 19.689686° 46.896371° Kecskemet 112,071
Iceland ISL
-
21.817439°
64.126521° Reykjavík 120,879
Italy
ITA 12.496366° 41.902783° Roma 2,872,021
ITA 14.216341° 40.857155° Napoli 978,399
ITA 7.686864° 45.070339° Torino 896,773
ITA 13.361267° 38.115688° Palermo 678,492
ITA 8.946256° 44.405650° Genova 592,507
ITA 11.342616° 44.494887° Bologna 386,181
ITA 11.255814° 43.769560° Firenze 381,037
ITA 15.083030° 37.507877° Catania 315,601
Kosovo KOSOVO 21.165503° 42.662914° Pristine 145,149
Macedonia MKD 21.427996° 41.997346° Skopje 497,900
Moldova
MDA 28.863810° 47.010453° Chisinau 674,500
MDA 27.918415° 47.753995° Balti 144,900

MDA 29.596805° 46.848185° Tiraspol 133,807
Montenegro MNE 19.259364° 42.430420° Podgorica 150,977
Norway NOR 5.322054° 60.391263° Bergen 275,112
Poland
POL 16.284355° 50.784009° Walbrzych 116,691
POL 18.546285° 50.102174° Rybnik 140,052
POL 19.020002° 50.121801° Tychy 128,621
POL 19.058385° 49.822377° Bielsko-Biala 173,013
Portugal
PRT -9.139337° 38.722252° Lisbon 552,700
PRT -8.629105° 41.157944° Porto 237,591
PRT -8.611785° 41.123876°
Vila Nova de
Gaia
186,502
PRT -9.224547° 38.757760° Amadora 175,136
PRT -8.426507° 41.545471° Braga 136,885
PRT -8.440357° 40.204829° Coimbra 105,842
Romania
ROU 26.102538° 44.426767° București 1,883,425
ROU 23.623635° 46.771210° Cluj-Napoca 324,576
ROU 21.208679° 45.748872° Timisoara 319,279
ROU 27.601442° 47.158455° Iasi 290,422
ROU 28.634814° 44.159801° Constanta 283,872
ROU 23.794881° 44.330178° Craiova 269,506
ROU 25.601198° 45.657976° Brasov 253,200
ROU 28.007994° 45.435321° Galati 249,432
ROU 26.012862° 44.936664° Ploiesti 209,945
Russia
RUS 38.987221° 45.039267° Krasnodar 805,680
RUS 47.512628° 42.966631° Makhachkala 578,332
Serbia
SRB 20.448922° 44.786568° Beograd 1,166,763
SRB 19.833550° 45.267135° Novi Sad 231,798
SRB 21.895759° 43.320902° Niš 183,164
SRB 20.911423° 44.012793° Kragujevac 150,835
Slovakia
SVK 17.107137° 48.145892° Bratislava 419,678
SVK 21.257800° 48.721005° Kosice 239,464
Slovenia SVN 14.505752° 46.056946° Ljubljana 278,789

Spain
ESP 2.173404° 41.385064° Barcelona 1,602,386
ESP -5.984459° 37.389092° Seville 696,676
ESP -0.889742° 41.648870° Zaragoza 666,058
ESP -4.421399° 36.721274° Malaga 566,913
ESP -1.130654° 37.992240° Murcia 439,712
ESP -2.934985° 43.263013° Bilbao 346,574
ESP -8.720727° 42.240599° Vigo 294,997
ESP -8.411540° 43.362344° A Coruña 244,810
ESP -3.598557° 37.177336° Granada 237,540
ESP -0.996584° 37.625683° Cartagena 216,451
ESP -0.490686° 38.345996° Alicante 332,067
Switzerland
CHE 8.541971° 47.377085° Zürich 391,317
CHE 6.142296° 46.198392° Genève 194,546
CHE 7.597551° 47.567442° Basel 168,563
CHE 6.633597° 46.519962° Lausanne 133,859
CHE 7.444608° 46.947922° Bern 129,964
CHE 8.737565° 47.499950° Winterthur 106,780
Turkey
TUR 28.978359° 41.008238° İstanbul 14,025,646
TUR 32.859742° 39.933363° Ankara 4,587,558
TUR 27.142826° 38.423734° İzmir 2,847,691
TUR 29.060964° 40.188528° Bursa 1,800,278
TUR 35.330828° 36.991419° Adana 1,663,485
TUR 37.378110° 37.065953° Gaziantep 1,510,270
TUR 32.493155° 37.874643° Konya 1,174,536
TUR 30.713323° 36.896891° Antalya 1,068,099

5.3 Vs30 Values from USGS Vs30 Global Server
With the help of the USGS Vs30 Global Server, the data was imported into MATLAB software
and along with the corresponding coordinates of the cities, the resultant Vs30 values were
obtained.
Table 9. Vs30 values imported from USGS Vs30 Global Server.
Country
Country
Abbv.
Co-ordinates (Long,
Lat)
City
Vs30
[m/s]
Albania
ALB 19.818698° 41.327546° Tirana 284
ALB 19.461607° 41.332807° Durres 187
ALB 20.086640° 41.110236° Elbasan 330
ALB 19.562760° 40.727504° Fier 274
ALB 20.777807° 40.614079° Korçë 371
ALB 19.503256° 42.069299° Shkoder 258
Austria
AUT 16.373819° 48.208174° Wien 311
AUT 11.404102° 47.269212° Innsbruck 279
Belgium
BEL 4.444643° 50.410809° Charleroi 356
BEL 5.579666° 50.632557° Liège 245
Bosnia and
Herzegovina
BIH 18.413076° 43.856259° Sarajevo 425
BIH 17.191000° 44.772181° Banja Luka 311
Bulgaria
BGR 23.321868° 42.697708° Sofia 288
BGR 24.745290° 42.135408° Plovdiv 245
BGR 27.914733° 43.214050° Varna 387
BGR 27.462638° 42.504795° Burgas 351
BGR 25.965655° 43.835571° Ruse 453
BGR 25.634464° 42.425777° Stara Zagora 374
Croatia
HRV 15.981919° 45.815011° Zagreb 298
HRV 16.440194° 43.508132° Split 287
HRV 14.442176° 45.327063° Rijeka 507
Cyprus CYP 33.022617° 34.707130° Lemesós 448
Czech Republic CZE 18.262524° 49.820923° Ostrava 247
France FRA 1.444209° 43.604652° Toulouse 278

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