Identifying Regions with High Liquefaction Potential Close to Large Populations in Europe

MSc. Earthquake Engineering with Disaster ManagementStudent: Syed Ali Hamza Naqvi (14103036)

Supervisor: Dr. Carmine GalassoUCL Department of Civil, Environmental and Geomatic Engineering, Gower St, London,WC1E 6BT

1. INTRODUCTIONSoil 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. METHODOLOGYUsing 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.

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. CONCLUSIONThe 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 PotentialExtreme High Medium Low

Turkey 3   5  Greece 1 5    

Romania 1 1 7  Russia 1 1    

Albania 1   1 4Montenegro 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       2Kosovo       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.