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

UCL DEPARTMENT OF CIVIL, ENVIRONMENTAL & GEOMATIC ENGINEERING

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i

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.

ii

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

v

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

vii

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)𝑑𝑧20

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 + 𝑒−𝑋



Where,

𝑋 = 𝛽0 + 𝛽1𝑥1 + ⋯ + 𝛽𝑘𝑥𝑘

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)

http://www.citypopulation.de/



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 + 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), 𝑋𝑛 =(𝑋𝑖 − 𝑋𝑚𝑖𝑛)

(𝑋𝑚𝑎𝑥 − 𝑋𝑚𝑖𝑛)



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, 𝐻𝐷𝐼𝑛 = 1 −(𝐻𝐷𝐼𝑖 − 𝐻𝐷𝐼𝑚𝑖𝑛)

(𝐻𝐷𝐼𝑚𝑎𝑥 − 𝐻𝐷𝐼𝑚𝑖𝑛)

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

http://www.citypopulation.de/



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



FRA 5.369780° 43.296482° Marseille 362

FRA 5.724524° 45.188529° Grenoble 392

FRA 3.876716° 43.610769° Montpellier 281

FRA 2.894833° 42.688659° Perpignan 254

Germany DEU 9.182932° 48.775846° Stuttgart 419

DEU 7.842104° 47.999008° Freiburg 312

Greece

GRC 23.729360° 37.983917° Athínai 331

GRC 22.944419° 40.640063° Thessaloniki 334

GRC 21.734574° 38.246639° Pátrai 356

GRC 25.144213° 35.338735° Iraklion 397

GRC 22.419125° 39.639022° Larissa 251

GRC 22.942159° 39.362190° Volos 287

Hungary

HUN 19.040235° 47.497912° Budapest 332

HUN 20.141425° 46.253010° Szeged 244

HUN 20.762386° 48.096363° Miskolc 312

HUN 18.232266° 46.072734° Pécs 435

HUN 17.650397° 47.687457° Gyor 209

HUN 21.724405° 47.949532° Nyiregyhaza 199

HUN 19.689686° 46.896371° Kecskemet 228

Iceland ISL -

21.817439° 64.126521° Reykjavík 400

Italy

ITA 12.496366° 41.902783° Roma 318

ITA 14.216341° 40.857155° Napoli 539

ITA 7.686864° 45.070339° Torino 344

ITA 13.361267° 38.115688° Palermo 337

ITA 8.946256° 44.405650° Genova 446

ITA 11.342616° 44.494887° Bologna 362

ITA 11.255814° 43.769560° Firenze 250

ITA 15.083030° 37.507877° Catania 393

Kosovo KOSOVO 21.165503° 42.662914° Pristine 366

Macedonia MKD 21.427996° 41.997346° Skopje 242

Moldova MDA 28.863810° 47.010453° Chisinau 382



MDA 27.918415° 47.753995° Balti 311

MDA 29.596805° 46.848185° Tiraspol 269

Montenegro MNE 19.259364° 42.430420° Podgorica 191

Norway NOR 5.322054° 60.391263° Bergen 554

Poland

POL 16.284355° 50.784009° Walbrzych 437

POL 18.546285° 50.102174° Rybnik 303

POL 19.020002° 50.121801° Tychy 244

POL 19.058385° 49.822377° Bielsko-Biala 323

Portugal

PRT -9.139337° 38.722252° Lisbon 386

PRT -8.629105° 41.157944° Porto 379

PRT -8.611785° 41.123876° Vila Nova de

Gaia 408

PRT -9.224547° 38.757760° Amadora 420

PRT -8.426507° 41.545471° Braga 332

PRT -8.440357° 40.204829° Coimbra 318

Romania

ROU 26.102538° 44.426767° București 261

ROU 23.623635° 46.771210° Cluj-Napoca 347

ROU 21.208679° 45.748872° Timisoara 192

ROU 27.601442° 47.158455° Iasi 350

ROU 28.634814° 44.159801° Constanta 382

ROU 23.794881° 44.330178° Craiova 268

ROU 25.601198° 45.657976° Brasov 352

ROU 28.007994° 45.435321° Galati 346

ROU 26.012862° 44.936664° Ploiesti 260

Russia RUS 38.987221° 45.039267° Krasnodar 186

RUS 47.512628° 42.966631° Makhachkala 280

Serbia

SRB 20.448922° 44.786568° Beograd 409

SRB 19.833550° 45.267135° Novi Sad 207

SRB 21.895759° 43.320902° Niš 217

SRB 20.911423° 44.012793° Kragujevac 352

Slovakia SVK 17.107137° 48.145892° Bratislava 334

SVK 21.257800° 48.721005° Kosice 277



Slovenia SVN 14.505752° 46.056946° Ljubljana 291

Spain

ESP 2.173404° 41.385064° Barcelona 345

ESP -5.984459° 37.389092° Seville 219

ESP -0.889742° 41.648870° Zaragoza 318

ESP -4.421399° 36.721274° Malaga 366

ESP -1.130654° 37.992240° Murcia 265

ESP -2.934985° 43.263013° Bilbao 319

ESP -8.720727° 42.240599° Vigo 444

ESP -8.411540° 43.362344° A Coruña 407

ESP -3.598557° 37.177336° Granada 496

ESP -0.996584° 37.625683° Cartagena 313

ESP -0.490686° 38.345996° Alicante 388

Switzerland

CHE 8.541971° 47.377085° Zürich 420

CHE 6.142296° 46.198392° Genève 244

CHE 7.597551° 47.567442° Basel 253

CHE 6.633597° 46.519962° Lausanne 533

CHE 7.444608° 46.947922° Bern 326

CHE 8.737565° 47.499950° Winterthur 420

Turkey

TUR 28.978359° 41.008238° İstanbul 377

TUR 32.859742° 39.933363° Ankara 347

TUR 27.142826° 38.423734° İzmir 262

TUR 29.060964° 40.188528° Bursa 394

TUR 35.330828° 36.991419° Adana 236

TUR 37.378110° 37.065953° Gaziantep 336

TUR 32.493155° 37.874643° Konya 207

TUR 30.713323° 36.896891° Antalya 244



5.4 CTI Values extracted from USGS Earth Explorer Maps

CTI values were obtained using the help of ArcGIS. The data extracted from the USGS Earth

Explorer Maps were converted into graphical representation on the software. The CTI values

from ArcGIS were then manually extracted against the corresponding cities and tabulated. As

mentioned before, the values in ArcGIS were magnified by a multiple of 100 as the data from

USGS was in raster format. As raster format data is comprised of pixels, the pixel value cannot

be defined in decimals. Hence, when the values were extracted, they were then divided by a

multiple of 100 to bring the true CTI value.

Table 10. CTI values obtained for the cities from the USGS Earth Explorer Maps

Country Country

Abbv. Co-ordinates (Long, Lat) City CTI

Albania

ALB 19.818698° 41.327546° Tirana 6.06

ALB 19.461607° 41.332807° Durres 7.12

ALB 20.086640° 41.110236° Elbasan 11.83

ALB 19.562760° 40.727504° Fier 6.31

ALB 20.777807° 40.614079° Korçë 4.47

ALB 19.503256° 42.069299° Shkoder 6.42

Austria AUT 16.373819° 48.208174° Wien 5.08

AUT 11.404102° 47.269212° Innsbruck 5.23

Belgium BEL 4.444643° 50.410809° Charleroi 9.51

BEL 5.579666° 50.632557° Liège 3.59

Bosnia and

Herzegovina

BIH 18.413076° 43.856259° Sarajevo 7.97

BIH 17.191000° 44.772181° Banja Luka 6.76

Bulgaria

BGR 23.321868° 42.697708° Sofia 8.01

BGR 24.745290° 42.135408° Plovdiv 7.79

BGR 27.914733° 43.214050° Varna 4.85

BGR 27.462638° 42.504795° Burgas 5.18

BGR 25.965655° 43.835571° Ruse 3.95

BGR 25.634464° 42.425777° Stara Zagora 6.18

Croatia HRV 15.981919° 45.815011° Zagreb 8.6

HRV 16.440194° 43.508132° Split 5.9



HRV 14.442176° 45.327063° Rijeka 8.09

Cyprus CYP 33.022617° 34.707130° Lemesós 3.53

Czech Republic CZE 18.262524° 49.820923° Ostrava 5.42

France

FRA 1.444209° 43.604652° Toulouse 5.87

FRA 5.369780° 43.296482° Marseille 5.06

FRA 5.724524° 45.188529° Grenoble 8.99

FRA 3.876716° 43.610769° Montpellier 4.44

FRA 2.894833° 42.688659° Perpignan 5.18

Germany DEU 9.182932° 48.775846° Stuttgart 4.21

DEU 7.842104° 47.999008° Freiburg 7.77

Greece

GRC 23.729360° 37.983917° Athínai 3.22

GRC 22.944419° 40.640063° Thessaloniki 4.2

GRC 21.734574° 38.246639° Pátrai 4.7

GRC 25.144213° 35.338735° Iraklion 7.9

GRC 22.419125° 39.639022° Larissa 9.97

GRC 22.942159° 39.362190° Volos 6.53

Hungary

HUN 19.040235° 47.497912° Budapest 5.44

HUN 20.141425° 46.253010° Szeged 9.9

HUN 20.762386° 48.096363° Miskolc 4.08

HUN 18.232266° 46.072734° Pécs 4.7

HUN 17.650397° 47.687457° Gyor 5.98

HUN 21.724405° 47.949532° Nyiregyhaza 5.11

HUN 19.689686° 46.896371° Kecskemet 5.93

Iceland ISL -21.817439° 64.126521° Reykjavík 3.9

Italy

ITA 12.496366° 41.902783° Roma 7.49

ITA 14.216341° 40.857155° Napoli 3.12

ITA 7.686864° 45.070339° Torino 6.56

ITA 13.361267° 38.115688° Palermo 6.38

ITA 8.946256° 44.405650° Genova 3.22

ITA 11.342616° 44.494887° Bologna 3.88

ITA 11.255814° 43.769560° Firenze 8.99

ITA 15.083030° 37.507877° Catania 4.49



Kosovo KOSOVO 21.165503° 42.662914° Pristine 5.74

Macedonia MKD 21.427996° 41.997346° Skopje 9.47

Moldova

MDA 28.863810° 47.010453° Chisinau 6.08

MDA 27.918415° 47.753995° Balti 5.64

MDA 29.596805° 46.848185° Tiraspol 5.74

Montenegro MNE 19.259364° 42.430420° Podgorica 5.8

Norway NOR 5.322054° 60.391263° Bergen 4.46

Poland

POL 16.284355° 50.784009° Walbrzych 5.2

POL 18.546285° 50.102174° Rybnik 4.72

POL 19.020002° 50.121801° Tychy 5.53

POL 19.058385° 49.822377° Bielsko-Biala 4.25

Portugal

PRT -9.139337° 38.722252° Lisbon 5.33

PRT -8.629105° 41.157944° Porto 3.21

PRT -8.611785° 41.123876° Vila Nova de

Gaia 3.88

PRT -9.224547° 38.757760° Amadora 4.39

PRT -8.426507° 41.545471° Braga 3.69

PRT -8.440357° 40.204829° Coimbra 3.06

Romania

ROU 26.102538° 44.426767° București 6.6

ROU 23.623635° 46.771210° Cluj-Napoca 5.81

ROU 21.208679° 45.748872° Timisoara 10.78

ROU 27.601442° 47.158455° Iasi 4.52

ROU 28.634814° 44.159801° Constanta 4.02

ROU 23.794881° 44.330178° Craiova 6.44

ROU 25.601198° 45.657976° Brasov 4.2

ROU 28.007994° 45.435321° Galati 8.99

ROU 26.012862° 44.936664° Ploiesti 4.55

Russia RUS 38.987221° 45.039267° Krasnodar 7.25

RUS 47.512628° 42.966631° Makhachkala 6.86

Serbia

SRB 20.448922° 44.786568° Beograd 4.7

SRB 19.833550° 45.267135° Novi Sad 6.42

SRB 21.895759° 43.320902° Niš 12.11



SRB 20.911423° 44.012793° Kragujevac 7.41

Slovakia SVK 17.107137° 48.145892° Bratislava 5.02

SVK 21.257800° 48.721005° Kosice 8

Slovenia SVN 14.505752° 46.056946° Ljubljana 9.62

Spain

ESP 2.173404° 41.385064° Barcelona 7.1

ESP -5.984459° 37.389092° Seville 7.81

ESP -0.889742° 41.648870° Zaragoza 4.43

ESP -4.421399° 36.721274° Malaga 5.27

ESP -1.130654° 37.992240° Murcia 3.49

ESP -2.934985° 43.263013° Bilbao 2.65

ESP -8.720727° 42.240599° Vigo 4.41

ESP -8.411540° 43.362344° A Coruña 4.65

ESP -3.598557° 37.177336° Granada 5.33

ESP -0.996584° 37.625683° Cartagena 5.92

ESP -0.490686° 38.345996° Alicante 4.8

Switzerland

CHE 8.541971° 47.377085° Zürich 4.36

CHE 6.142296° 46.198392° Genève 9.68

CHE 7.597551° 47.567442° Basel 4.5

CHE 6.633597° 46.519962° Lausanne 9.74

CHE 7.444608° 46.947922° Bern 7.44

CHE 8.737565° 47.499950° Winterthur 6.5

Turkey

TUR 28.978359° 41.008238° İstanbul 5.7

TUR 32.859742° 39.933363° Ankara 3.5

TUR 27.142826° 38.423734° İzmir 7.83

TUR 29.060964° 40.188528° Bursa 5.11

TUR 35.330828° 36.991419° Adana 6.34

TUR 37.378110° 37.065953° Gaziantep 3.63

TUR 32.493155° 37.874643° Konya 5.49

TUR 30.713323° 36.896891° Antalya 11.14



5.5 City GDP calculations with the help of World Bank data

With the help of the World Bank data on GDP per capita for each country and the population

data from the previously mentioned source, the City GDP was calculated by multiplying the

GDP per capita and the population data.

Table 11. City GDP data of the assessed cities.

Country Country

Abbv. City Population

GDP per

capita (USD) City GDP

Albania

ALB Tirana 800,986

$4,659.34

$3,732,066,109.24

ALB Durres 276,191 $1,286,867,773.94

ALB Elbasan 301,397 $1,404,311,097.98

ALB Fier 315,012 $1,467,748,012.08

ALB Korçë 224,165 $1,044,460,951.10

ALB Shkoder 218,523 $1,018,172,954.82

Austria AUT Wien 1,797,337

$50,546.70 $90,849,454,137.90

AUT Innsbruck 126,965 $6,417,661,765.50

Belgium BEL Charleroi 202,480

$46,877.99 $9,491,855,415.20

BEL Liège 195,968 $9,186,585,944.32

Bosnia and

Herzegovina

BIH Sarajevo 369,534 $4,661.76

$1,722,678,819.84

BIH Banja Luka 150,997 $703,911,774.72

Bulgaria

BGR Sofia 1,228,282

$7,498.83

$9,210,677,910.06

BGR Plovdiv 341,567 $2,561,352,866.61

BGR Varna 335,949 $2,519,224,439.67

BGR Burgas 198,725 $1,490,204,991.75

BGR Ruse 147,055 $1,102,740,445.65

BGR Stara Zagora 137,729 $1,032,806,357.07

Croatia

HRV Zagreb 688,163

$13,607.51

$9,364,184,904.13

HRV Split 167,121 $2,274,100,678.71

HRV Rijeka 128,384 $1,746,986,563.84

Cyprus CYP Lemesós 101,000 $25,248.98 $2,550,146,980.00

Czech Republic CZE Ostrava 294,200 $19,844.76 $5,838,328,392.00

France FRA Toulouse 453,317 $42,503.30 $19,267,468,446.10



FRA Marseille 852,516 $36,234,743,302.80

FRA Grenoble 158,346 $6,730,227,541.80

FRA Montpellier 268,456 $11,410,265,904.80

FRA Perpignan 120,489 $5,121,180,113.70

Germany DEU Stuttgart 604,297

$46,268.64 $27,960,000,346.08

DEU Freiburg 220,286 $10,192,333,631.04

Greece

GRC Athínai 3,168,846

$21,956.41

$69,576,482,002.86

GRC Thessaloniki 806,635 $17,710,808,780.35

GRC Pátrai 195,265 $4,287,318,398.65

GRC Iraklion 157,452 $3,457,080,667.32

GRC Larissa 144,651 $3,176,016,662.91

GRC Volos 130,094 $2,856,397,202.54

Hungary

HUN Budapest 1,744,665

$13,480.91

$23,519,671,845.15

HUN Szeged 161,921 $2,182,842,428.11

HUN Miskolc 161,265 $2,173,998,951.15

HUN Pécs 146,581 $1,976,045,268.71

HUN Gyor 128,902 $1,737,716,260.82

HUN Nyiregyhaza 118,164 $1,592,958,249.24

HUN Kecskemet 112,071 $1,510,819,064.61

Iceland ISL Reykjavík 120,879 $47,461.19 $5,737,061,186.01

Italy

ITA Roma 2,872,021

$35,925.88

$103,179,881,803.48

ITA Napoli 978,399 $35,149,845,066.12

ITA Torino 896,773 $32,217,359,185.24

ITA Palermo 678,492 $24,375,422,172.96

ITA Genova 592,507 $21,286,335,381.16

ITA Bologna 386,181 $13,873,892,264.28

ITA Firenze 381,037 $13,689,089,537.56

ITA Catania 315,601 $11,338,243,653.88

Kosovo KOSOVO Pristine 145,149 $3,877.17 $562,767,348.33

Macedonia MKD Skopje 497,900 $4,838.46 $2,409,069,234.00

Moldova MDA Chisinau 674,500

$2,239.00 $1,510,205,500.00

MDA Balti 144,900 $324,431,100.00



MDA Tiraspol 133,807 $299,593,873.00

Montenegro MNE Podgorica 150,977 $7,106.86 $1,072,972,402.22

Norway NOR Bergen 275,112 $100,818.50 $27,736,379,172.00

Poland

POL Walbrzych 116,691

$13,647.96

$1,592,594,100.36

POL Rybnik 140,052 $1,911,424,093.92

POL Tychy 128,621 $1,755,414,263.16

POL Bielsko-Biala 173,013 $2,361,274,503.48

Portugal

PRT Lisbon 552,700

$21,733.07

$12,011,867,789.00

PRT Porto 237,591 $5,163,581,834.37

PRT Vila Nova de

Gaia 186,502 $4,053,261,021.14

PRT Amadora 175,136 $3,806,242,947.52

PRT Braga 136,885 $2,974,931,286.95

PRT Coimbra 105,842 $2,300,271,594.94

Romania

ROU București 1,883,425

$9,499.21

$17,891,049,594.25

ROU Cluj-Napoca 324,576 $3,083,215,584.96

ROU Timisoara 319,279 $3,032,898,269.59

ROU Iasi 290,422 $2,758,779,566.62

ROU Constanta 283,872 $2,696,559,741.12

ROU Craiova 269,506 $2,560,094,090.26

ROU Brasov 253,200 $2,405,199,972.00

ROU Galati 249,432 $2,369,406,948.72

ROU Ploiesti 209,945 $1,994,311,643.45

Russia RUS Krasnodar 805,680

$14,611.70 $11,772,354,456.00

RUS Makhachkala 578,332 $8,450,413,684.40

Serbia

SRB Beograd 1,166,763

$6,353.96

$7,413,565,431.48

SRB Novi Sad 231,798 $1,472,835,220.08

SRB Niš 183,164 $1,163,816,729.44

SRB Kragujevac 150,835 $958,399,556.60

Slovakia SVK Bratislava 419,678

$18,046.84 $7,573,861,717.52

SVK Kosice 239,464 $4,321,568,493.76

Slovenia SVN Ljubljana 278,789 $23,289.34 $6,492,811,809.26



Spain

ESP Barcelona 1,602,386

$29,863.18

$47,852,341,547.48

ESP Seville 696,676 $20,804,960,789.68

ESP Zaragoza 666,058 $19,890,609,944.44

ESP Malaga 566,913 $16,929,824,963.34

ESP Murcia 439,712 $13,131,198,604.16

ESP Bilbao 346,574 $10,349,801,745.32

ESP Vigo 294,997 $8,809,548,510.46

ESP A Coruña 244,810 $7,310,805,095.80

ESP Granada 237,540 $7,093,699,777.20

ESP Cartagena 216,451 $6,463,915,174.18

ESP Alicante 332,067 $9,916,576,593.06

Switzerland

CHE Zürich 391,317

$84,815.41

$33,189,711,794.97

CHE Genève 194,546 $16,500,498,753.86

CHE Basel 168,563 $14,296,739,955.83

CHE Lausanne 133,859 $11,353,305,967.19

CHE Bern 129,964 $11,022,949,945.24

CHE Winterthur 106,780 $9,056,589,479.80

Turkey

TUR İstanbul 14,025,646

$10,971.66

$153,884,619,192.36

TUR Ankara 4,587,558 $50,333,126,606.28

TUR İzmir 2,847,691 $31,243,897,437.06

TUR Bursa 1,800,278 $19,752,038,121.48

TUR Adana 1,663,485 $18,251,191,835.10

TUR Gaziantep 1,510,270 $16,570,168,948.20

TUR Konya 1,174,536 $12,886,609,649.76

TUR Antalya 1,068,099 $11,718,819,074.34



5.6 Calculation of Probability of Liquefaction, P[Liq]

Using the proposal given by Zhu et al, (2014), the equations were used as stated earlier to reach

to the calculation of the hazard i.e. the Probability of Liquefaction.

Table 12. Calculation of Probability of Liquefaction for the marked Cities.

Country Abbv. City CTI PGA [g] Vs30 [m/s] X P[Liq]

ALB Tirana 6.06 0.22 284 -3.887 0.0201

ALB Durres 7.12 0.26 187 -1.213 0.2293

ALB Elbasan 11.83 0.26 330 -2.205 0.0993

ALB Fier 6.31 0.28 274 -3.161 0.0406

ALB Korçë 4.47 0.21 371 -5.799 0.0030

ALB Shkoder 6.42 0.25 258 -3.064 0.0446

AUT Wien 5.08 0.10 311 -6.352 0.0017

AUT Innsbruck 5.23 0.12 279 -5.436 0.0043

BEL Charleroi 9.51 0.13 356 -4.825 0.0080

BEL Liège 3.59 0.10 245 -5.808 0.0030

BIH Sarajevo 7.97 0.19 425 -5.439 0.0043

BIH Banja Luka 6.76 0.19 311 -4.432 0.0117

BGR Sofia 8.01 0.23 288 -3.178 0.0400

BGR Plovdiv 7.79 0.25 245 -2.357 0.0865

BGR Varna 4.85 0.16 387 -6.429 0.0016

BGR Burgas 5.18 0.12 351 -6.558 0.0014

BGR Ruse 3.95 0.16 453 -7.589 0.0005

BGR Stara Zagora 6.18 0.26 374 -4.802 0.0081

HRV Zagreb 8.6 0.30 298 -2.585 0.0701

HRV Split 5.9 0.25 287 -3.763 0.0227

HRV Rijeka 8.09 0.18 507 -6.395 0.0017

CYP Lemesós 3.53 0.31 448 -6.255 0.0019

CZE Ostrava 5.42 0.10 247 -5.181 0.0056

FRA Toulouse 5.87 0.04 278 -7.516 0.0005

FRA Marseille 5.06 0.09 362 -7.308 0.0007

FRA Grenoble 8.99 0.13 392 -5.479 0.0042

FRA Montpellier 4.44 0.09 281 -6.378 0.0017

FRA Perpignan 5.18 0.14 254 -4.597 0.0100

DEU Stuttgart 4.21 0.08 419 -8.510 0.0002

DEU Freiburg 7.77 0.09 312 -5.678 0.0034

GRC Athínai 3.22 0.17 331 -6.173 0.0021

GRC Thessaloniki 4.2 0.29 334 -4.802 0.0081

GRC Pátrai 4.7 0.33 356 -4.624 0.0097

GRC Iraklion 7.9 0.23 397 -4.756 0.0085

GRC Larissa 9.97 0.26 251 -1.582 0.1706



GRC Volos 6.53 0.36 287 -2.758 0.0596

HUN Budapest 5.44 0.09 332 -6.674 0.0013

HUN Szeged 9.9 0.08 244 -3.877 0.0203

HUN Miskolc 4.08 0.10 312 -6.779 0.0011

HUN Pécs 4.7 0.08 435 -8.473 0.0002

HUN Gyor 5.98 0.09 209 -4.281 0.0136

HUN Nyiregyhaza 5.11 0.07 199 -4.801 0.0082

HUN Kecskemet 5.93 0.10 228 -4.462 0.0114

ISL Reykjavík 3.9 0.30 400 -5.689 0.0034

ITA Roma 7.49 0.18 318 -4.358 0.0126

ITA Napoli 3.12 0.15 539 -8.749 0.0002

ITA Torino 6.56 0.10 344 -6.179 0.0021

ITA Palermo 6.38 0.13 337 -5.642 0.0035

ITA Genova 3.22 0.13 446 -8.136 0.0003

ITA Bologna 3.88 0.21 362 -5.914 0.0027

ITA Firenze 8.99 0.19 250 -2.539 0.0732

ITA Catania 4.49 0.20 393 -6.188 0.0020

KOSOVO Pristine 5.74 0.14 366 -6.123 0.0022

MKD Skopje 9.47 0.15 242 -2.752 0.0599

MDA Chisinau 6.08 0.15 382 -6.088 0.0023

MDA Balti 5.64 0.14 311 -5.360 0.0047

MDA Tiraspol 5.74 0.09 269 -5.633 0.0036

MNE Podgorica 5.8 0.30 191 -1.450 0.1899

NOR Bergen 4.46 0.08 554 -9.673 0.0001

POL Walbrzych 5.2 0.08 437 -8.448 0.0002

POL Rybnik 4.72 0.08 303 -6.686 0.0012

POL Tychy 5.53 0.07 244 -5.778 0.0031

POL Bielsko-Biala 4.25 0.10 323 -6.847 0.0011

PRT Lisbon 5.33 0.14 386 -6.634 0.0013

PRT Porto 3.21 0.13 379 -7.399 0.0006

PRT Vila Nova de Gaia 3.88 0.13 408 -7.503 0.0006

PRT Amadora 4.39 0.13 420 -7.448 0.0006

PRT Braga 3.69 0.13 332 -6.624 0.0013

PRT Coimbra 3.06 0.13 318 -6.522 0.0015

ROU București 6.6 0.21 261 -3.410 0.0320

ROU Cluj-Napoca 5.81 0.10 347 -6.629 0.0013

ROU Timisoara 10.78 0.19 192 -0.708 0.3301

ROU Iasi 4.52 0.25 350 -5.174 0.0056

ROU Constanta 4.02 0.10 382 -7.611 0.0005

ROU Craiova 6.44 0.10 268 -5.056 0.0063

ROU Brasov 4.2 0.30 352 -4.976 0.0069

ROU Galati 8.99 0.26 346 -3.470 0.0302

ROU Ploiesti 4.55 0.28 260 -3.486 0.0297

RUS Krasnodar 7.25 0.23 186 -1.380 0.2010



RUS Makhachkala 6.86 0.34 280 -2.660 0.0654

SRB Beograd 4.7 0.20 409 -6.345 0.0018

SRB Novi Sad 6.42 0.14 207 -3.227 0.0382

SRB Niš 12.11 0.13 217 -1.593 0.1690

SRB Kragujevac 7.41 0.26 352 -4.084 0.0166

SVK Bratislava 5.02 0.11 334 -6.428 0.0016

SVK Kosice 8 0.10 277 -4.664 0.0093

SVN Ljubljana 9.62 0.18 291 -3.165 0.0405

ESP Barcelona 7.1 0.12 345 -5.710 0.0033

ESP Seville 7.81 0.10 219 -3.582 0.0271

ESP Zaragoza 4.43 0.03 318 -9.441 0.0001

ESP Malaga 5.27 0.14 366 -6.342 0.0018

ESP Murcia 3.49 0.18 265 -4.890 0.0075

ESP Bilbao 2.65 0.06 319 -8.200 0.0003

ESP Vigo 4.41 0.09 444 -8.374 0.0002

ESP A Coruña 4.65 0.09 407 -8.074 0.0003

ESP Granada 5.33 0.20 496 -7.050 0.0009

ESP Cartagena 5.92 0.11 313 -5.894 0.0027

ESP Alicante 4.8 0.17 388 -6.353 0.0017

CHE Zürich 4.36 0.08 420 -8.536 0.0002

CHE Genève 9.68 0.09 244 -3.685 0.0245

CHE Basel 4.5 0.15 253 -4.694 0.0091

CHE Lausanne 9.74 0.09 533 -7.396 0.0006

CHE Bern 7.44 0.09 326 -5.973 0.0025

CHE Winterthur 6.5 0.08 420 -7.651 0.0005

TUR İstanbul 5.7 0.25 377 -5.095 0.0061

TUR Ankara 3.5 0.20 347 -5.982 0.0025

TUR İzmir 7.83 0.49 262 -1.251 0.2225

TUR Bursa 5.11 0.37 394 -4.759 0.0085

TUR Adana 6.34 0.20 236 -3.077 0.0441

TUR Gaziantep 3.63 0.32 336 -4.808 0.0081

TUR Konya 5.49 0.18 207 -2.981 0.0483

TUR Antalya 11.14 0.36 244 -0.343 0.4152

In order to validate the results, liquefaction studies have been studied to see in what cities have

liquefaction actually occurred in the past or any tests done in the city to show that liquefaction

potential is high. The top 20 cities for Probability of Liquefaction are studied and cities with X

mark against them validate the results.



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.



Table 13. Top 20 Cities that resulted with high values P[Liq]

Country Name Normalized

P[Liq] Liquefaction Studies

TUR Antalya 1.0000

ROU Timisoara 0.7951 x

ALB Durres 0.5521 x

TUR İzmir 0.5359 x

RUS Krasnodar 0.4840

MNE Podgorica 0.4574

GRC Larissa 0.4107 x

SRB Niš 0.4070

ALB Elbasan 0.2391

BGR Plovdiv 0.2083

ITA Firenze 0.1762

HRV Zagreb 0.1687 x

RUS Makhachkala 0.1574

MKD Skopje 0.1443

GRC Volos 0.1435 x

TUR Konya 0.1162 x

ALB Shkoder 0.1073 x

TUR Adana 0.1060

ALB Fier 0.0978 x

SVN Ljubljana 0.0974

5.7 MCDM Analysis

Using the Artificial Neural Network model, the parameters have been normalized and weighted

accordingly as mentioned earlier.

Normalized (Inverted)

HDI

Normalized

Population

Normalized City

GDP

Normalized

P[Liq]

0.25 0.20 0.05 0.50

Thus the equation for Liquefaction Risk Potential (LRP) becomes as follows,

𝐿𝑅𝑃 = [𝐻𝑎𝑧𝑎𝑟𝑑] × [𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒]

Where,

𝐻𝑎𝑧𝑎𝑟𝑑 = [0.65 × 𝑁𝑜𝑟𝑚 𝑃[𝐿𝑖𝑞]]



𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 = [[0.15 × 𝑁𝑜𝑟𝑚 𝑃𝑜𝑝] × [0.15 × 𝑁𝑜𝑟𝑚 𝐻𝐷𝐼] × [0.05 × 𝑁𝑜𝑟𝑚 𝐶𝑖𝑡𝑦 𝐺𝐷𝑃]]

LRP results in a value range 1.00 to 0.00 where 1.00 shows the maximum risk and 0.00 shows

no risk. These results can also be used for ranking purposes.

Table 14. MCDM Analysis for Liquefaction Risk Potential

Country Name

Normalized

(Inverted)

HDI

Normalized

Population

Normalized

City GDP

Normalized

P[Liq] Ranking

TUR Antalya 0.3982 0.0695 0.0744 1.0000 0.617

ROU Timisoara 0.5310 0.0157 0.0178 0.7951 0.534

TUR İzmir 0.3982 0.1973 0.2015 0.5359 0.417

GRC Larissa 0.7522 0.0031 0.0187 0.4107 0.395

RUS Krasnodar 0.5044 0.0506 0.0747 0.4840 0.382

MNE Podgorica 0.5575 0.0036 0.0050 0.4574 0.369

TUR İstanbul 0.3982 1.0000 1.0000 0.0145 0.357

ALB Durres 0.1681 0.0126 0.0064 0.5521 0.321

ITA Firenze 0.7788 0.0201 0.0872 0.1762 0.291

SRB Niš 0.3274 0.0059 0.0056 0.4070 0.287

ITA Roma 0.7788 0.1990 0.6699 0.0303 0.283

CHE Genève 0.9823 0.0067 0.1055 0.0588 0.282

CHE Basel 0.9823 0.0049 0.0911 0.0217 0.262

AUT Wien 0.8230 0.1218 0.5896 0.0040 0.262

NOR Bergen 1.0000 0.0125 0.1786 0.0000 0.261

GRC Volos 0.7522 0.0021 0.0166 0.1435 0.261

CHE Zürich 0.9823 0.0208 0.2141 0.0003 0.261

GRC Athínai 0.7522 0.2203 0.4511 0.0049 0.257

DEU Stuttgart 0.9558 0.0361 0.1801 0.0003 0.255

CHE Bern 0.9823 0.0021 0.0698 0.0060 0.252

CHE Lausanne 0.9823 0.0024 0.0720 0.0013 0.250

SVN Ljubljana 0.7876 0.0128 0.0403 0.0974 0.250

CHE Winterthur 0.9823 0.0004 0.0570 0.0010 0.249

DEU Freiburg 0.9558 0.0086 0.0644 0.0081 0.248

HRV Zagreb 0.5926 0.0422 0.0590 0.1687 0.244

ESP Seville 0.7699 0.0428 0.1335 0.0651 0.240

ESP Barcelona 0.7699 0.1078 0.3096 0.0078 0.233

BGR Plovdiv 0.4956 0.0173 0.0147 0.2083 0.232

FRA Marseille 0.8319 0.0540 0.2340 0.0015 0.231

ISL Reykjavík 0.8938 0.0014 0.0354 0.0080 0.229

FRA Perpignan 0.8319 0.0014 0.0314 0.0239 0.222

FRA Toulouse 0.8319 0.0253 0.1235 0.0012 0.220

BEL Charleroi 0.8230 0.0073 0.0599 0.0190 0.220

ITA Torino 0.7788 0.0571 0.2078 0.0048 0.219

ITA Napoli 0.7788 0.0630 0.2269 0.0002 0.219

FRA Montpellier 0.8319 0.0120 0.0723 0.0039 0.216



FRA Grenoble 0.8319 0.0041 0.0419 0.0099 0.216

ITA Palermo 0.7788 0.0415 0.1568 0.0084 0.215

RUS Makhachkala 0.5044 0.0343 0.0531 0.1574 0.214

GRC Thessaloniki 0.7522 0.0507 0.1134 0.0195 0.214

BEL Liège 0.8230 0.0068 0.0579 0.0071 0.214

AUT Innsbruck 0.8230 0.0019 0.0398 0.0103 0.213

ESP Murcia 0.7699 0.0243 0.0835 0.0178 0.210

ITA Genova 0.7788 0.0353 0.1366 0.0006 0.209

ESP Zaragoza 0.7699 0.0406 0.1276 0.0000 0.207

ESP Malaga 0.7699 0.0335 0.1083 0.0041 0.207

ITA Bologna 0.7788 0.0205 0.0884 0.0063 0.206

ITA Catania 0.7788 0.0154 0.0719 0.0048 0.204

ROU București 0.5310 0.1280 0.1145 0.0769 0.203

GRC Pátrai 0.7522 0.0068 0.0260 0.0233 0.202

CZE Ostrava 0.7611 0.0139 0.0361 0.0133 0.202

ESP Alicante 0.7699 0.0166 0.0626 0.0040 0.201

GRC Iraklion 0.7522 0.0041 0.0206 0.0204 0.200

ESP Bilbao 0.7699 0.0176 0.0654 0.0005 0.200

ESP Cartagena 0.7699 0.0083 0.0401 0.0065 0.199

ESP Vigo 0.7699 0.0139 0.0554 0.0004 0.198

ESP Granada 0.7699 0.0098 0.0442 0.0019 0.198

ESP A Coruña 0.7699 0.0103 0.0457 0.0006 0.197

BGR Sofia 0.4956 0.0810 0.0580 0.0962 0.191

HUN Budapest 0.6283 0.1180 0.1512 0.0029 0.190

SVK Kosice 0.6814 0.0099 0.0262 0.0223 0.185

CYP Lemesós 0.7257 0.0000 0.0147 0.0045 0.184

TUR Ankara 0.3982 0.3222 0.3258 0.0059 0.183

HUN Szeged 0.6283 0.0044 0.0123 0.0488 0.183

TUR Adana 0.3982 0.1122 0.1169 0.1060 0.181

POL Tychy 0.6991 0.0020 0.0095 0.0073 0.179

SVK Bratislava 0.6814 0.0229 0.0474 0.0037 0.179

POL Bielsko-

Biala 0.6991 0.0052 0.0134 0.0024 0.178

POL Rybnik 0.6991 0.0028 0.0105 0.0029 0.177

TUR Konya 0.3982 0.0771 0.0820 0.1162 0.177

HRV Split 0.5926 0.0047 0.0129 0.0545 0.177

POL Walbrzych 0.6991 0.0011 0.0084 0.0004 0.176

HUN Gyor 0.6283 0.0020 0.0094 0.0327 0.174

PRT Lisbon 0.6460 0.0324 0.0763 0.0030 0.173

ROU Galati 0.5310 0.0107 0.0135 0.0726 0.172

HUN Kecskemet 0.6283 0.0008 0.0079 0.0273 0.171

ROU Ploiesti 0.5310 0.0078 0.0110 0.0714 0.171

HUN Nyiregyhaza 0.6283 0.0012 0.0084 0.0195 0.167

PRT Porto 0.6460 0.0098 0.0317 0.0013 0.166

ALB Elbasan 0.1681 0.0144 0.0072 0.2391 0.165



PRT Vila Nova de

Gaia 0.6460 0.0061 0.0244 0.0012 0.165

PRT Braga 0.6460 0.0026 0.0174 0.0030 0.164

PRT Amadora 0.6460 0.0053 0.0228 0.0013 0.164

PRT Coimbra 0.6460 0.0003 0.0130 0.0034 0.164

HUN Miskolc 0.6283 0.0043 0.0122 0.0026 0.160

HUN Pécs 0.6283 0.0033 0.0109 0.0004 0.158

HRV Rijeka 0.5926 0.0020 0.0094 0.0039 0.151

MKD Skopje 0.2655 0.0285 0.0137 0.1443 0.145

ROU Brasov 0.5310 0.0109 0.0137 0.0164 0.144

ROU Craiova 0.5310 0.0121 0.0147 0.0151 0.143

ROU Iasi 0.5310 0.0136 0.0160 0.0134 0.143

TUR Bursa 0.3982 0.1220 0.1267 0.0203 0.140

ROU Cluj-Napoca 0.5310 0.0161 0.0181 0.0030 0.138

ROU Constanta 0.5310 0.0131 0.0156 0.0010 0.137

TUR Gaziantep 0.3982 0.1012 0.1059 0.0193 0.135

BGR Stara Zagora 0.4956 0.0026 0.0048 0.0195 0.134

SRB Novi Sad 0.3274 0.0094 0.0076 0.0918 0.130

BGR Varna 0.4956 0.0169 0.0145 0.0037 0.130

BGR Burgas 0.4956 0.0070 0.0078 0.0033 0.127

BGR Ruse 0.4956 0.0033 0.0052 0.0011 0.125

SRB Kragujevac 0.3274 0.0036 0.0043 0.0397 0.103

SRB Beograd 0.3274 0.0765 0.0463 0.0041 0.102

ALB Shkoder 0.1681 0.0084 0.0047 0.1073 0.098

ALB Fier 0.1681 0.0154 0.0076 0.0978 0.094

KOSOVO Pristine 0.3274 0.0032 0.0017 0.0051 0.085

ALB Tirana 0.1681 0.0503 0.0223 0.0483 0.077

BIH Banja Luka 0.2478 0.0036 0.0026 0.0281 0.077

BIH Sarajevo 0.2478 0.0193 0.0093 0.0103 0.071

ALB Korçë 0.1681 0.0088 0.0048 0.0071 0.048

MDA Chisinau 0.0000 0.0412 0.0079 0.0053 0.011

MDA Balti 0.0000 0.0032 0.0002 0.0111 0.006

MDA Tiraspol 0.0000 0.0024 0.0000 0.0084 0.005

The colours in the rankings indicate the severity of the risk. Red indicates cities with Extreme

threat of liquefaction. The value range for this is from 1.00 to 0.300. Orange indicates the cities

with High Risk and range from 0.299 to 0.150. Yellow indicates the cities with Medium Risk

and range from 0.149 to 0.100. Green indicates the regions with Low Risk and range from 0.99

to 0.00. This range is made on the basis of self-judgement.



Figure 38 Map illustration of Liquefaction Risk Potential Assessment done on the 112 cities of Europe.



5.8 Discussion

The initial target of the study was to attain spatial information (continuous data) of the region

when it came to the hazard parameter as Zhu et al. 2014, method gave the probability of

liquefaction for a spatial region rather than a specific site location. As the information extracted

from the sources of the parameters was data intensive, the computers were not able to compute

them on the software. Thus this lead to point extraction of data from the cities, meaning that

the CTI value, PGA values and Vs30 values varied within a city and a continuous model could

not have been made. Due to the data being intensive, point values for each parameter was taken

with the assumption that the point value was constant throughout the city. Thus the precision

of the result wasn’t achieved as expected.

The procedure for achieving a city GDP was using GDP per capita and multiplying with the

population of the city. This is to be understood that this is a very crude procedure to assume

the GDP of a city, but since the weightage given to this parameter was very less compared to

the other parameter, this procedure was considered acceptable for MCDM analysis.

The most important aspect of the MCDM analysis was the weightage of the parameter. For this

report, the hazard and the exposure parameters have been equally divided and the exposure

parameter subdivided on the basis of limited knowledge that I have. It has to be understood

that weightage of the parameters is done on the basis for what an organization is looking for.

In the case of Insurance sector, they are looking forward to seeing how many people would be

willing to pay for a liquefaction insurance and therefore their concentration would be more in

the exposure parameter compared to the hazard. In the case of government sector, their focus

would be more on being able to foresee the possible potential liquefaction risk in their regions

and would focus the analysis with more weightage to the hazard component over the exposure.

Then there are also weightages done on the basis of expert judgements from a panel of

experienced professionals who can help in fixing the weightages of the parameters. So all in

all there is no fixed weightage values for these parameters and need to be done on the basis of

the requirement of the organization or sector who are using it.

The overall results are validated with actual cases of liquefaction in the past and also in cities

where liquefaction testing is done and show high liquefaction potential. Out of the top 20 cities,

9 of them are validated with past records and tests. It is to be understood that this does not mean

that the cities that do not have past liquefaction records and ranked among the top are wrong



results, but that there is a possibility in the future that it might happen given that the provided

parameters do lead to the result of liquefaction.



6 Conclusion

With the use of Multi Criteria Decision Making analysis, the ranking of the 112 cities was done

for Liquefaction Risk Potential. The top cities that lie in extreme liquefaction risk potential are:

Antalya, Izmir and Istanbul from Turkey

Timisoara from Romania

Larissa from Greece

Podgorica from Montenegro

Durres from Albania

Krasnodar from Russia

The cities that have high liquefaction risk potential are:

Ljubljana from Slovenia

Nis from Serbia

Makhachkala from Russia

Bucharest from Romania

Bergen from Norway

Rome, Florence, Turin, Naples, Palermo, Genova, Bologna and Catania from Italy

Reykjavik from Iceland

Zagreb from Croatia

Volos, Athens, Thessaloniki, Patras, and Heraklion from Greece

Marseille, Perpignan, Toulouse, Montpellier and Grenoble from France

Seville, Barcelona, Murica, Zaragoza, Malaga, Alicante and Bilbao from Spain

Stuttgart and Freiburg from Germany

Geneva, Basel, Zurich, Bern, Lausanne and Winterthur from Switzerland

Plovdiv from Bulgaria

Charleroi and Liege from Belgium

Vienna and Innsbruck from Austria

Ostrava from Czech Republic

The cities that have medium liquefaction risk potential are:

Ankara, Adana, Konya, Bursa and Gaziantep from Turkey



Kosice and Bratislava from Slovakia

Novi sad, Kragujevac and Belgrade from Serbia

Galati, Ploiesti, Brasov, Craiova, Iasi, Cluj-Napoca and Constanta from Romania

Lisbon, Porto, Vila Nova de Gaia, Braga, Amadora and Coimbra from Portugal

Tychy, Bielsko-Biala, Rybnik and Walbrzych from Poland

Skopje from Macedonia

Budapest, Szeged, Gyor, Kecskemet, Nyiregyhaza, Miskolc and Pecs from Hungary

Split and Rijeka from Croatia

Cartagena, Vigo, Granada and A Coruna from Spain

Lemesós from Cyprus

Sofia, Stara Zagora, Varna, Burgas and Ruse from Bulgaria

Elbasan from Albania

The cities that have low liquefaction risk potential are:

Chisinau, Balti and Tiraspol from Moldova

Pristine from Kosovo

Sarajevo and Banja Luka from Bosnia and Herzegovina

Shkoder, Fier, Tirana and Korçë from Albania

Following is a table of the number of cities of the selected country under what range of

liquefaction risk potential they lie in:

Table 15. Number cities of the selected countries with liquefaction risk potential

Country Liquefaction Risk Potential

Extreme High Medium Low

Turkey 3 5

Greece 1 5

Romania 1 1 7

Russia 1 1

Albania 1 1 4

Montenegro 1

Italy 8

Spain 7 4

Switzerland 6

France 5

Belgium 2

Austria 2

Germany 2



Bulgaria 1 5

Serbia 1 3

Croatia 1 2

Slovenia 1

Norway 1

Iceland 1

Czech Republic 1

Hungary 7

Portugal 6

Poland 4

Slovakia 2

Macedonia 1

Cyprus 1

Moldova 3

Bosnia and Herzegovina 2

Kosovo 1

For Future recommendations, Multi Criteria Decision Making analysis should be followed for

liquefaction risk potential with the use of Zhu et al. 2014 method to calculate probability of

liquefaction. In order to make the results more precise, the data should be in continuous format

in order to get better and accurate results. As stated previously in the discussion, this model is

also very flexible in terms of usage as various organizations can use this model to the need of

their requirements by changing the weightages of the parameters.



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