1. Identifying Regions with High Liquefaction
Potential Close to Large Populations in Europe
MSc. Earthquake Engineering with Disaster Management
Student: Syed Ali Hamza Naqvi (14103036)
Supervisor: Dr. Carmine Galasso
UCL Department of Civil, Environmental and Geomatic Engineering, Gower St, London,WC1E 6BT
1. INTRODUCTION
Soil Liquefaction is one of the secondary events triggered after an earthquake which
cause a potential risk to the elements around them i.e. population, structures and/or
infrastructures. Global seismic hazard maps have already been developed at a global
scale but liquefaction risk potential (LRP) maps have yet to be developed. Using Multi
Criteria Decision Making (MCDM) analysis, the parameters for LRP are identified and
used to identify the regions with high liquefaction susceptibility close to large
populations of Europe. Liquefaction Risk Potential is calculated and is used to rank the
regions accordingly. This kind of study can guide/prioritize more refined, site-specific,
studies which require large amount of data and computation.
2. METHODOLOGY
Using the approach of Risk Assessment, the parameters for Hazard and Exposure are
identified. For Liquefaction (Hazard), the main three parameters:
 Ground Acceleration
 Soil Type
 Hydrological Parameter of the Soil
Zhu et al., 2014 empirical equation for global model is used, which gives a liquefaction
susceptibility of an aerial region. Using the said method the Hazard parameter is
calculated.
𝑃 𝐿𝑖𝑞 =
1
1 + 𝑒−𝑋
𝑋 = 24.1 + 2.067 [ln 𝑃𝐺𝐴 ] + 0.355 × 𝐶𝑇𝐼 − 4.784 [ln 𝑉𝑠30 ]
 PGA is the peak ground acceleration (Ground Acceleration)
 Va30 is the shear wave velocity at 30 meters depth from surface (Soil Type)
 CTI is the Compound Topography Index which shows the wetness index of the soil
(Hydrological Parameter of the Soil)
 Population
 Gross domestic Product (GDP)
 Human Development Index (HDI), where
HDI is a summary measure by United
Nation Development program UNDP
that rates the countries with respect to
the level of knowledge, life expectancy
and standard of living.
• (Top) PGA data extracted from
GSHAP, 1999 Maps at a resolution of
1 km by 1 km.
3. DATA
• (Below) Vs30 data extracted from USGS
Global Vs30 Server at a resolution of
800 m by 800 m
• (Top) Population Density of
Europe from 2004 to 2013.
4. RESULTS
• Liquefaction susceptibility in the 112 assessed cities of Europe.
• Liquefaction Risk Potential Assessment done of the selected 112 cities of Europe.
5. CONCLUSION
The top 20 cities were checked for
validation out of which 9 cities had past
cases and studies showing that
liquefaction has occurred or can occur.
Turkey, Greece and Romania showed the
highest risk potential for liquefaction.
For LRP, the cities were ranked on the
basis of the weightages that were
provided to the parameters which were
50% to hazard and 50% to exposure. The
weightages provided to the parameters
can vary from organization to
organization depending on the usage
purpose of this model .
112 cities in Europe are selected on the
basis of their seismicity and population.
The seismic criteria having at least 0.8
m/s2 PGA in 10% probability of
exceedance in 50 years. The population
criteria was at 100,000 people.
With the parameters identified, LRP assessment is done using Artificial Neural Network
(Ramhormozian et al., 2013) method, which is one of the techniques of doing MCDM
analysis.
For Exposure, the three main parameters
used are:
• (Left) Gross Domestic Product (GDP)
per capita map of Europe using the
World Bank Database
• (Top Left) CTI data extracted from USGS Earth Explorer Datasets at a resolution of 1
km by 1 km.
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
References: Zhu, J., Daley, D., Baise, L., Thompson, E., Wald, D. and Knudsen, K. (2015). A Geospatial Liquefaction Model for Rapid Response and Loss Estimation. Earthquake Spectra, 31(3), pp.1813-1837; Kongar, I., &
Giovinazzi, S. (2015). Evaluating Desktop Methods for Assessing Liquefaction-Induced Damage to Infrastructure for the Insurance Sector, 1–13; Ramhormozian, S. (2013). Artificial neural networks approach to predict principal
ground motion parameters for quick post-earthquake damage assessment of bridges.