Rapid seismic risk assessment

Author’s Accepted Manuscript RELATIVE RAPID ASSESSMENT

SEISMIC

RISK

Tanja Kalman Šipoš, Marijana Hadzima-Nyarko

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PII: DOI: Reference:

S2212-4209(17)30212-1 http://dx.doi.org/10.1016/j.ijdrr.2017.06.025 IJDRR600

To appear in: International Journal of Disaster Risk Reduction Received date: 29 March 2017 Revised date: 26 June 2017 Accepted date: 26 June 2017 Cite this article as: Tanja Kalman Šipoš and Marijana Hadzima-Nyarko, RELATIVE RAPID SEISMIC RISK ASSESSMENT, International Journal of Disaster Risk Reduction, http://dx.doi.org/10.1016/j.ijdrr.2017.06.025 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

RELATIVE RAPID SEISMIC RISK ASSESSMENT Tanja Kalman Šipoš1, Marijana Hadzima-Nyarko2 1

Assistant professor, Faculty of Civil Engineering, J. J. Strossmayer University of Osijek, Croatia, Crkvena 21, Osijek, [email protected], corresponding author 2 Associate professor, Faculty of Civil Engineering, J. J. Strossmayer University of Osijek, Croatia, Crkvena 21, Osijek, [email protected] Abstract: This paper deals with seismic risk on the Croatian territory, especially since population growth in riskprone areas increases the potential loss due to an earthquake. The net effects of such urbanization factors are examined through the use of simulation models that estimate building inventory under possible seismic hazard expressed with peak ground acceleration. A case study of seismic risk assessments is illustrated using Croatian cities to give an overview of the overall relative risk in Croatia as developing country using general parameters from Census data. Results of a prospective analysis indicate that, for the same seismic event, the overall risk is expected to increase due to growth of population in pre-code building inventory and populated areas and cities. This relative rapid assessment presented in this paper points out which cities need detailed analysis and enables city planners to incorporate seismic risk analysis into pre-disaster emergency and land-use planning to encourage risk-reduction strategies. The validation of proposed assessment was done on L’Aquila Province based on data after L’Aquila earthquake 2009. Results indicate prediction of realistic risk for the study area based on vulnerabilities of buildings and exposure of population with relative errors of 12% and 8% respectively.

Keywords: seismic risk, Croatia, rapid methodology, hazard, vulnerability, buildings, population density

1. Introduction An increasing number of rapidly growing urban areas are becoming more vulnerable to seismic risk in their development process (Hung et al. 2013). The concept of risk has been introduced in disaster management and suggests that elements at risk and vulnerability should be taken into consideration in the framework of hazard and disaster management in order to reduce losses (Aubrecht et al. 2013). During the 20th century, more than 1,100 strong earthquakes have occurred, causing more than 1,500,000 casualties- most of them due to buildings collapsing, which is some 90% of direct deaths (Lantada et al. 2009). For urban zones, exposure to possible large earthquakes, certain preparedness and emergency procedures have to be organized in the event of and prior to an earthquake. Incorporation of seismic risk of facilities into a decision making framework needs procedures to quantify such risk for stakeholders. Since the quantification of the earthquake effects on the physical and social environment is required, the main element of such quantification is the building losses, which is directly related to casualties, planning of emergency response, first aid and emergency shelter needs. According to Ramirez and Miranda (2009), there is one aim of current building codes: to protect lifesafety and do not contain provisions that aim to mitigate the amount of damage and economic loss suffered during an earthquake. Performance-based earthquake engineering (PBEE) seeks to improve seismic risk decision-making through assessment and design methods (Moehle and Dairlein, 2004). In the case of new buildings, the basic configuration and design criteria needed to prevent catastrophic failure are well known, but the majority of the existing buildings in seismic environments do not satisfy modern code requirements. One of the possible ways for seismic risk reduction in urban areas is known building inventory and population data for earthquake-prone areas. 1

The evaluation of seismic risk generally includes data collection, seismic hazard assessment, vulnerability assessment as well as discipline of social and economic sciences. Seismic risk is described as the probability of loss at a given site and obtained through the convolution of exposure, vulnerability and seismic hazard (Crowley et al. 2009). Exposure is defined as the amount of human activity located in the zones of seismic hazard as defined by the stock of infrastructure in that location; hazard is defined as the probability of a certain ground motion occurring at a location; vulnerability is defined as the susceptibility of the infrastructure stock (Daniell, 2011). According to Villacis et al (2000) general characteristics and uses of earthquake scenarios for earthquake risk evaluation should vary depending on whether they are prepared for cities in developed or developing countries. Developed countries have resources for accurate quantification of the expected damage with detailed risk analysis with precise description of the actions that are implemented in organization of risk management of threatened cities. However, developing countries, like Croatia, have very limited resources and available data with no earthquake disaster preparedness. Accordingly, for a developing country that does not have the resources to implement all the measures in all the cities, reduction of earthquake risk at local (municipality) level is unnecessary. Therefore, at first stage, identification of problem and raise of awareness toward the social and political context for cities that are possibly threatened can be implemented by rapid relative earthquake evaluation of seismic risk, as it is proposed in this paper. The main idea of this assessment was based on previous research done by Carreño et al (2007) and Salgado-Gálvez et al. (2016) where urban seismic risk with holistic approach was developed. Foremost Cardona (2001) developed a conceptual framework from a holistic or multidisciplinary approach for the seismic risk analysis of urban centers. In this method, where the risk by means of indices is achieved affecting the physical risk with an impact factor, available data about socioeconomic fragility and the lack of resilience at urban level are necessary (Carreno et al., 2007). The concept of evaluation of urban seismic risk by means of indices and based on set of factors that aggravate the physical risk, the calculation of aggravating factors using transformation functions and the calculation of weights which represent the relative importance of each factor by means of the Analytic Hierarchy Process (AHP) is the basis on which we based our method, e.g. the holistic approach developed by Cardona (2001) and then continued in works of Carreno (2006) and Carreno et al. (2007). However, application of this concept on new urban area requires a large number of data that are not available in all countries. Therefore, a Rapid relative seismic risk assessment is an adaptation on previous methodology that can be obtained for every country, especially developing, based only on statistical Census data for buildings and population. The major objective of the study can be stated as:  To develop an assessment for rapid prediction of seismic risk for an observed region based on general risk inputs: hazard data, building age data, population data.  To explore and validate the possibility of using the developed methodology for a different region. 2. Seismic risk assessments: rapid vs detailed There are several state-of-the-art approaches for seismic risk assessment (Radius 2012, Hazus) and several assessments for large cities and countries in last few years [cities: Athens, Greece (Eleftheriadou el al. 2014); Almeria-Spain (Rivas-Medina et al. 2013); Barcelona-Spain (AguilarMeléndez et al. 2012); Cologne-Germany (Tyagunov et al, 2013), Lisbon-Portugal (Mota de Sa et al, 2012), Medellin-Colombia (Salgado-Gálvez et al, 2014), Hsinchu City-Taiwan (Hung et al, 2013)], Nepal (Chaulagain, 2015). All of them are detailed analysis that required a large amount of various input data, which is impossible to collect in many developing countries. Accordingly, development of rapid seismic assessment is preferable for efficient pre-disaster emergency. Villacis et al. (2000) implemented fast earthquake scenarios for risk management in developing countries, where very limited resources, data, and, in most cases, very short histories of earthquake disaster preparedness are available. They provided fast earthquake scenarios to identify the main factors contributing to the earthquake risk of cities in developing countries. Currently, the methodology for fast earthquake scenarios is being utilized by the Risk Assessment Tools for the 2

Diagnosis of Urban Seismic Risk (RADIUS) Project, with the main goal to determine the main factors that contribute to the earthquake risk of cities in developing countries. Another fast-track earthquake risk assessment for twenty-three cities in Turkey was developed by Kepecki and Ozcep (2011). The aim of their methodology, similarly as ours, is to determine earthquake risk levels. In their study, the risk was in function of ground motion level, number of houses, population and per capita income of city residents and values for risk factors such as number of houses, population, and per capita incomes of city residents were obtained from the Turkish Statistical Institute. The selected cities were classified according to their relative risks with the five cities with highest risk. On the other hand detailed analysis for vulnerability assessment methods can be categorize as follows (Preciado et al, 2015): empirical or qualitative; analytical or quantitative; hybrid methods, depending on whether the sources of damage information are derived from post-earthquake surveys; analytical simulations, or a combination of these, respectively. Empirical assessment methods are based on the observation of damage suffered during past seismic events. A complete observed damage database would be necessary in applying such approaches; however, this is only possible in high seismicity areas where properly performed post-earthquake surveys are available (Barbat et al., 2010). Analytical methods are used when a single building is evaluated in a detailed way and in numerical terms (displacement capacity, ultimate force etc.). Accordingly, probabilistic analyses of computer generated structural responses; obtained by applying nonlinear analysis procedures to representative buildings, provide fragility curves, damage probability matrices and vulnerability functions (Barbat et al., 2010). It is important to highlight that main difference between the detailed and rapid methods (Fig. 1) is the number of inputs for deriving risk. It also must be noted, that results in detailed analysis are mostly expressed in absolute manner, for example number of mortalities (Alexander and Magni, 2013), however in contrary rapid methods represent risk as ranked list of examined cities or areas with an emphasis on the most vulnerable areas or the cities. SEISMIC RISK ASSESSMENT DETAILED METHOD – absolute risk

RAPID METHOD –relative risk Seismic hazard

RISK

Seismic hazard

Peak ground acceleration (PGA)

Low: 0.00-0.49

Probabilistic or Deterministic Seismic Hazard Assessment

Vulnerability of buildings (Census data)

Moderate: 0.5-0.74

Exposure

High: 0.75-1.00

 Building inventory (typology, structural types, number, usage, age, material, occupancy)

Exposure of population

 People: number per building

(Census data)

Vulnerability assessment method:

 Empirical (Damage Probability Matrices, Rapid Screening Method, Vulnerability Index Method, Macroseismic Method)  Analytical (Analytically-Derived Vulnerability Curves and DPM, Capacity Spectrum based Method, Collapse Mechanism-Based Methods)

Economic and social losses

 cost of repair and replacement of building damages,  percentage of injured people (HAZUS model, Cburn and Spence 2002)

Fig. 1 Overview of seismic risk assessments: rapid vs. detailed

Accordingly, development of Rapid assessment includes the seismicity, possible vulnerability of housing stock and population density. Therefore the definition of relation between these inputs must be obtained with regard to risk for threatened buildings and threatened population. Cardona and Barbat (2000) defined the term risk (Rie|T) as the probability of loss or as the loss average in an exposed element e as a consequence of the occurrence of an event with intensity larger than or equal to i during an exposition period T. Hazard (Hi|T) is defined as the probability or as the average expected rate of occurrence of an event with an intensity greater than or equal to i during an exposition 3

period T. Vulnerability (Ve) is defined as the intrinsic predisposition of the exposed element e to be affected or of being susceptible to suffer a loss as a result of the occurrence of an event with intensity i. Using these definitions, risk is a function f of the convolution between hazard Hi and vulnerability Ve during an exposition period T Rie|T = f(Hi ⊗ Ve)|T (1) where the symbol ⊗ stands for convolution (Cardona and Barbat, 2000). Correspondingly, for this study seismic risk was presented as interaction between seismic hazard and vulnerability (humans or their built environment) and therefore can be expressed qualitatively as:

R  H V

(2)

As shown in Equations 1 and 2, high seismic hazard does not necessarily mean high seismic risk and vice versa. There is no risk if there are no buildings with inhabitants, even though there is a high seismic hazard. Therefore, an evaluation of seismic risk under seismic excitation that have direct influence on vulnerability is defined: Rbuild  FH  Vbuild

(3) (4)

Rpop = FH · Epop

where Rbuild and Rpop represent the risk regarding the building potential damage and the threatened population respectively, Vbuild and Epop are building and population vulnerabilities, FH is a factor that presents seismic hazard for the observed region or city.

3. Case study The territory of Croatia is located in a highly prone earthquake area with the threat from earthquakes producing earthquake ground accelerations up to 0.36g (Table 1, Figure 2). Possibility for earthquakes with peak ground acceleration of 0.3g and higher is on an area of Croatian territory that occupying 5.53% of the territory where about 21.02% residents live. The threat of earthquakes with peak ground acceleration (PGA) of 0.2g to 0.3g covers 30.89 % of the territory, where 41.66 % residents live. On more than half of Croatian territory (56.22%) there is the possibility for an earthquake with peak ground acceleration of 0.1g to 0,2g, on which more than one-third (1.633.529) of the total Croatian population live. Therefore, the main aim of this study is to determine the seismic risk for Croatia that will be presented as relative rapid methodology thru convolution of hazard and exposure. Investigation must be made for every city because of the different range of hazard and population exposure (there will be no risk with high hazard and no exposure, and there can be high risk with low hazard and high exposure). 3.1 Seismic hazard assessment for Croatia Fortunately, Croatia has not experienced strong ground shaking although is a part of The Western Balkan seismically active region characterized by relatively higher earthquake hazard and risk when compared to the rest of Europe (except for Greece, Italy and Turkey with highest hazard levels) according to Markušić et al (2016). Figure 2 represents the hazard map, expressed in terms of the peak horizontal ground acceleration during an earthquake, which is exceeded on average once in 475 years (Herak, 2012). The map is accepted as a part of the National Annex to EN 1998-1 for Croatia.

4

Imotski

Vrgorac Ploče

Metković

Dubrovnik

Fig. 2 Seismic hazard map for Croatia (Herak, 2012), http://seizkarta.gfz.hr/karta.php

This map is used for determination of the seismic hazard impact factor FH for Croatia. The peak ground acceleration value was obtained from 429 municipalities and 127 cities in Croatia. These data were summarized (empirical) and presented by a cumulative log-normal distribution (logncdf) shown in Fig. 3. An influence of possible seismic activity value can be easily determined by known PGA value for every city. Table 1 Cities with PGA0.3g and FH0.98

1.0 0.8

FH

0.6 0.4 0.2 0.0 0

0.1

0.2

0.3

0.4

City Imotski Vrgorac Dubrovnik Metković Opuzen Ploče

PGA (g) 0.33 0.33 0.3 0.36 0.36 0.34

County Split-Dalmatia Split-Dalmatia Dubrovnik-Neretva Dubrovnik-Neretva Dubrovnik-Neretva Dubrovnik-Neretva

Peak ground acceleration (g) logncdf (mu=-1.788; sigma=0.333)

Empirical

Fig. 3 Function of seismic hazard factor FH for Croatia (mu, sigma – mean and standard deviation of lognormal function)

According to results presented on Fig 3, seismic hazard impact factor FH ranges up to 0.08 for PGA lower than 0.1g, from 0.08-0.72 for PGA between 0.1-0.2g and 0.72-0.98 for PGA between 0.2-0.3g.

3.2 Vulnerability of buildings in Croatia In the field of seismic vulnerability one of the main causes of death during an earthquake is building collapse and therefore buildings must be safe in order to reduce the loss of human lives. The element of risk can be a single dwelling in a building, a collection of the same or similar buildings (apartment 5

block, part of a village), or public services in a particular area. In a large number of elements, like building stocks, vulnerability may be defined in terms of the number or percentage of threatened buildings. The construction age parameter for buildings allows capturing the conditions of design standards as well as the requirements of the building codes, thus present factor that is directly related to the vulnerability (Salgado-Gálvez et al, 2015). In order to assess the vulnerability of buildings throughout Croatia, statistical methods were used with capturing standard data regarding to dwellings age. Census data regarding the age of dwellings are available for every municipality and city in Croatia and it was used in the application of the relative rapid assessment method. Based on the housing and population census (Croatian bureau of statistics, 2011) the age distribution of the dwellings for Croatia was determined and shown in Table 2. Table 2 Construction age distribution for dwellings in Croatia Fbuild,11 Fbuild,2 Fbuild,3 Fbuild,4 Country  before 1919 1919 – 1945 1946 – 1960 1961 – 1970 Croatia 1496558 112217 84963 138858 288563 % 100 7.63 5.78 9.45 19.63 1

Fbuild,5 1971-1980 325203 22.12

Fbuild,6 1981-1990 247084 16.81

Fbuild,7 1991-2000 129687 8.82

Fbuild,8 after 2001 143535 9.76

– impact factors for dwellings vulnerability by their construction age

The vulnerability of buildings Vbuild (Equation 5) in this paper depends on the weighted sum of a set of eight impact factors related to the construction age, Fbuild,i multiplied with weights, wi which represent the relative importance of each factor. The maximum value of Vbuild is taken as 1. 8

Vbuild   wi  Fbuild, i

(5)

i 1

Impact factors for dwellings vulnerability Fbuild,i are defined according to building construction age data (empirical) from Census data using functions shown in the Fig. 4. These functions standardise the gross values of the dwellings in municipalities and cities in Croatia for 8 time ranges (Table 2) transforming them into commensurable factors by cumulative lognormal functions. 1.0

b)

1.0

0.8

0.8

0.6

0.6

Fbuild,2

Fbuild,1

a)

0.4 0.2

0.4 0.2

0.0 0

500

1000

1500

0.0

2000

0

Number of dwelings build before 1919 logncdf (mu=4.213; sigma=1.306)

empirical

500 1000 1500 2000 Number of dwelings build between 1920-1945 logncdf(mu=3.865; sigma=1.0867)

6

empirical

1.0

c)

1.0

d)

0.8

Fbuild,4

Fbuild,3

0.8 0.6 0.4 0.2

0.6 0.4 0.2

0.0

0.0 0

500

1000

1500

2000

0

Number of dwelings build between 1946-1960 logncdf(mu=4.523; sigma=1.122)

e)

f)

2000

empirical

1.0 0.8

Fbuild,6

Fbuild,5

1500

logncdf(mu=4.965; sigma=1.189)

0.8 0.6 0.4 0.2

0.6 0.4 0.2

0.0

0.0 0

500

1000

1500

2000

0

Number of dwelings build between 1971-1980 logncdf(mu=5.315; sigma=1.118)

500

1000

1500

2000

Number of dwelings build between 1981-1990

empirical

logncdf(mu=5.105; sigma=1.047)

1.0

empirical

1.0

h)

0.8

Fbuild,8

0.8

Fbuild,7

1000

Number of dwelings build between 1961-1970

empirical

1.0

g)

500

0.6 0.4 0.2

0.6 0.4 0.2

0.0

0.0 0

500

1000

1500

2000

0

Number of dwelings build between 1991-2000 logncdf(mu=4.352; sigma=1.258)

500

1000

1500

2000

Number of dwelings build after 2001

empirical

logncdf(mu=4.265; sigma=1.238)

empirical

Fig. 4 Functions of impact factors for dwellings vulnerability Fbuild,i for Croatia (mu, sigma – mean and standard deviation of lognormal function)

If the construction period for the building group is known, it is evident that a rough conclusion on their seismic resistance and the effect of earthquake action could be made. To identify weight of impact factors for vulnerability of buildings (dwellings) and most influenced building classes, previous studies were reviewed (Benito el al., 2005). According to the construction age distribution it is possible to assign the distribution of vulnerability classes using the EMS-98 scale (Grunthal, 1998). Table 3 shows the distribution of vulnerability classes and building classes based on construction date. It can be concluded that as the structures are newer, their physical vulnerability is lower due to better construction practice and design standards (Salgado-Gálvez et al, 2015). This table has slightly modified data with respect to the original data provided. Since no such data currently exists for Croatia, and assuming similar construction methods in time ranges in Croatia and Spain, this data were used for Croatia. The method proposed in this paper, does not directly depend on this data, but uses it as an aid in the Analytic Hierarchy Process in generating weight ratio scales.

7

Table 3 Modified vulnerability and building class distribution for the buildings in Lorca by construction age (Salgado-Gálvez et al, 2015) EMS-98 vulnerability class Building class

Age

Before 1919 1920-1945 1946-1960 1961-1970 1971-1980 1981-1990 1991-2000 After 2001

A Stone masonry, earthen buildings 76% 62% 46% 18% 5% -

B

C

Brick masonry 24% 38% 49% 38% 40% 38% 28% 18%

Masonry with RC slabs, pre-code RC frames 5% 44% 55% 57% 62% 69%

D

Post-code RC frames 5% 10% 13%

Subindicators for AHP method w’1=1.0 w’2=1.1 w’3=2.0 w’4=3.0 w’5=3.4 w’6=3.6 w’7=3.8 w’8=4.0

The weights, wi, from Equation 4, that indicate importance of every Fbuild,i, are subdivided from w1 to w8 (by eight construction age time ranges) and calculated by means of the Analytic Hierarchy Process (AHP) which is used to derive ratio scales from paired comparisons (Saaty, 2001). The Analytic Hierarchy Process (AHP) is a widely used technique for multi-attribute decision making. It enables the decomposition of a problem into hierarchy and assures that both qualitative and quantitative aspects of a problem are incorporated in the evaluation process, during which opinion is systematically extracted by means of pair-wise comparisons (Parachini, 2008.) AHP is a compensatory decision methodology because alternatives that are efficient with respect to one or more objectives can be compensated by their performance with respect to other objectives. AHP transforms the application of data, experience, insights, and intuition in a logical and thorough way within a hierarchy as a whole. The core of AHP is an ordinal pair-wise comparison of attributes, sub-indicators in this context, in which preference statements are addressed. For a given objective, the comparisons are made per pairs of sub-indicators by firstly posing the question “Which of the two is the more important?” and secondly “By how much?”. The strength of preference is expressed on a semantic scale of 1-4, which keeps measurement within the same order of magnitude. A preference of 1 indicates equality between two sub-indicators while a preference of 4 indicates that one sub-indicator is 4 times larger or more important than the one to which it is being compared. The comparisons are being made between pairs of sub-indicators where perception is sensitive enough to make a distinction. The most important aids were prevailing percent of buildings for certain vulnerability class from Table 3. These comparisons resulted in proposed values of sub-indicators for AHP method: for scale from 1 to 4 it means that buildings (dwellings) with factor 1 (older buildings from stone masonry – w’1) will have 4 times higher possibility for vulnerability in regard to buildings build after 2001 (w’8). Respectively, buildings which were built between 1946 and 1960 (w’3) will have 2 times higher possibility, and etc. Accordingly, the same vulnerability class indicated similarity in possible behaviour which resulted in small change in sub-indicators preferences (w’1 to w’2; w’5 to w’8). This defined values resulted in comparison matrix W’ (Table 4) which has 8 rows and 8 columns. The first row presents sub-indicators derived from Table 3. In columns values are calculated according to ratio wi/wi=1,..,8 which derives ratios with value 1 on matrix diagonal. Therefore, every value represents the relative contribution (weights) of the two sub-indicators. Table 4 Comparison matrix W’ of eight sub-indicators W’ w’1 w’2 w’3 w’4 w’5 w’6 w’7 w’8

w’1 1.0 0.9 0.5 0.3 0.3 0.3 0.3 0.3

w’2 1.1 1.0 0.6 0.4 0.3 0.3 0.3 0.3

w’3 2.0 1.8 1.0 0.7 0.6 0.6 0.5 0.5

w’4 3.0 2.7 1.5 1.0 0.9 0.8 0.8 0.8

8

w’5 3.4 3.1 1.7 1.1 1.0 0.9 0.9 0.9

w’6 3.6 3.3 1.8 1.2 1.1 1.0 0.9 0.9

w’7 3.8 3.5 1.9 1.3 1.1 1.1 1.0 1.0

w’8 4.0 3.6 2.0 1.3 1.2 1.1 1.1 1.0

The relative weight values wi (Table 5) from Comparison matrix are calculated using an Eigen vector technique: each element of the matrix is divided with sum of its column, so we have normalized relative weight of each element. Results of averaging values accros the rows are relative weights wi for impact factors of dwellings vulnerability. One of the advantages of this method is that it is able to check the consistency of the comparison matrix through the calculation of the eigenvalues. AHP tolerates inconsistency through the amount of redundancy. For a matrix of size n×n, only n-1 comparisons are required to establish weights for n indicators. According to Saaty (2001) small inconsistency ratios (less than 0.1 is the suggested rule-ofthumb, although even 0.2 is often cited) do no drastically affect the weights. According to Table 4, the Consistency Index (CI) is obtained by first computing the Eigen value max derived from the summation of products between each element of Eigen vector ant the sum of columns of the Comparison matrix. Finally, a CI can be calculated from (λmax‐n)/(n‐1), where in our case λmax and n are equal to 8. The True Consistency Ratio (CR) is calculated by dividing the CI by the Random Index (RI=1.41, for n=8, by Saaty, 1980) for the corresponding matrix W’. In this paper, CR was equal to zero which means that the judgments are perfectly consistent. Table 5 Relative weights wi for impact factors of dwellings vulnerability Factor Fbuild,1 Fbuild,2 Fbuild,3 Fbuild,4 Fbuild,5 Fbuild,6 Fbuild,7 Fbuild,8

Description impact factor for dwellings built before 1919 (Fig. 4. a)) impact factor for dwellings built between 1920-1945 (Fig. 4. b)) impact factor for dwellings built between 1946-1960 (Fig. 4. c)) impact factor for dwellings built between 1961-1970 (Fig. 4. d)) impact factor for dwellings built between 1971-1980 (Fig. 4. e)) impact factor for dwellings built between 1981-1990 (Fig. 4. f)) impact factor for dwellings built between 1991-2000 (Fig. 4. g)) impact factor for dwellings built after 2001 (Fig. 4. h))

Weight w1 w2 w3 w4 w5 w6 w7 w8

Weight value 0.27 0.24 0.14 0.09 0.08 0.07 0.06 0.05

An example calculation of dwellings vulnerability is made for city Imotski (Table 6). The number of dwellings according to construction age is obtained from Census data (Croatian bureau of statistics, 2011). Impact factors Fbuild,i for every construction age is derived from cumulative lognormal functions presented in Figure 4 and then multiplied with relative weights for each factor according to Equation 5. The Vbuild of 0.763 indicate possible vulnerability according to buildings construction age. Table 6 Dwellings vulnerability Vbuild for Imotski (PGA=0.33g) Construction age

Imotski

Before 1919 1920-1945 1946-1960 1961-1970 1971-1980 1981-1990 1991-2000 After 2001

Number of dwelings 183 92 239 549 593 425 295 198

Fbuild,i (Figure 4.) 0.772 0.689 0.775 0.827 0.797 0.778 0.841 0.771

wi (Table 5) 0.27 0.24 0.14 0.09 0.08 0.07 0.06 0.05

Vbuild (Equation 5) 0.208 0.165 0.109 0.074 0.064 0.054 0.050 0.039

0.763

3.3 An exposure of population in Croatia The impact on human beings is the most important component for any kind of disaster. Since the risk assessment depends on the number of exposed people and their vulnerability, knowledge of the updated and detailed population exposure data of the observed area is required. It is highly important to know the spatial information of people distribution in order to understand the impact and potential consequences of any natural disaster. The main source of local population data is national census with data for all cities or municipalities in Croatia. Usually the census data is available at the administrative level and gives the population data in residence or dwelling basis. The residential population databases are useful for the purposes of 9

exposure assessment if it is assumed that the majority of the people are in their homes. Since the census data represents the night-time population as residential people, therefore, the use of these population data in population casualty estimation will produce considerable error for day-time scenarios (Bhaduri, 2010; McPherson and Brown, 2004), but will result in worst possible risk scenario. Population distribution for this study was captured in form of population density (people/km2) and taken from census data (Croatian bureau of statistics, 2011), which is presented in Table 7 for most populated cities in Croatian counties. Table 7 Cities with highest population density in Croatia per Counties City

County

Zaprešić Krapina Sisak Duga Resa Varaždin Koprivnica Bjelovar Rijeka Novalja Virovitica Požega Slavonski Brod Zadar Osijek Šibenik Vinkovci Split Pula Dubrovnik Čakovec Zagreb

Number of residents

Zagreb County Krapina-Zagorje Sisak-Moslavina Karlovac Varaždin Koprivnica-Križevci Bjelovar-Bilogora Primorje-Gorski Kotar Lika-Senj Virovitica-Podravina Požega-Slavonia Brod-Posavina Zadarska Osijek-Baranja Šibenik-Knin Vukovar-Srijem Split-Dalmatia Istria Dubrovnik-Neretva Međimurje City of Zagreb

25223.00 12480.00 47768.00 11180.00 46946.00 30854.00 40276.00 128624.00 3663.00 21291.00 26248.00 59141.00 75062.00 108048.00 46332.00 35312.00 178102.00 57460.00 42615.00 27104.00 790017.00

Population density (people/km2) 475.66 262.71 113.21 185.13 789.59 338.86 214.42 2956.22 39.43 125.29 196.29 1087.68 391.94 617.56 106.86 375.42 2235.04 1072.14 296.17 321.90 1232.48

Such a distribution of population density vulnerability is presented with lognormal distribution as is shown in Fig. 5. 1.0

Epop

0.8 0.6 0.4 0.2 0.0 0

200

400

600

800

1000

1200

Population density (people/km2) logncdf(mu=3.923; sigma=1.047)

empirical

Fig. 5. Cumulative distribution for population density in Croatia (mu, sigma – mean and standard deviation of lognormal function)

4 Results of RAPID seismic risk assessment for Croatia After creating the indicators for vulnerability of buildings and population, the final task is to derive the results and highlight areas of potential risk from the aggregation of risk indices. An example calculation of risk is made for three cities (Table 8). The number of dwellings according to construction age is obtained from Census data (Croatian bureau of statistics, 2011). Impact factors Fbuild,i for every construction age is derived from cumulative lognormal functions presented in Figure 4 and then multiplied with relative weights (Table 5) for each factor according to Equation 5. Data of an exposure of population were derived from Census data (Croatian bureau of statistics, 2011), and then 10

transformed into Epop according to cumulative lognormal distribution presented on Figure 5. An expected earthquake excitation for every city was defined according to Seismic hazard map for Croatia (Figure 2). The hazard factor was obtained from Figure 3, and then after multiplication with Vbuild and Epop, relative potential risks were calculated in terms of risks for buildings according to their construction age Rbuild (Equation (3)), and population Rpop (Equation (4)), related to population density. Average relative risk R is the mean value of individual risks Rbuild and Rpop per city in Croatia. Table 8 Risk calculation for several Croatian cities according to Relative Rapid assessment Population

Hazard

Risk

61 45 65 205 191 159 86 88 1657 858 1945 6354 6203 4737 1924 3058 183 92 239 549 593 425 295 198

0.462 0.435 0.342 0.556 0.429 0.432 0.469 0.535 0.992 0.995 0.996 0.998 0.998 0.998 0.996 0.998 0.772 0.689 0.775 0.827 0.797 0.778 0.841 0.771

0.27 0.24 0.14 0.09 0.08 0.07 0.06 0.05 0.27 0.24 0.14 0.09 0.08 0.07 0.06 0.05 0.27 0.24 0.14 0.09 0.08 0.07 0.06 0.05

0.45

30.16

0.311

0.18

0.583

0.261

0.181 0.22

Low risk

Before 1919 1920-1945 1946-1960 1961-1970 1971-1980 1981-1990 1991-2000 After 2001 Before 1919 1920-1945 1946-1960 1961-1970 1971-1980 1981-1990 1991-2000 After 2001 Before 1919 1920-1945 1946-1960 1961-1970 1971-1980 1981-1990 1991-2000 After 2001

R

1.00

391.94

0.975

0.18

0.583

0.581

0.569 0.58

Moderate risk

Number of Pop. PGA Construction dwelings Fbuild,i wi Vbuild Density Epop FH Rbuild Rpop (g) age (Census (Fig. 4.) (Table 5) (Eq. 5) (Census (Fig. 5) (Fig. 3) (Eq. 3) (Eq. 4) (Fig 2.) data) data)

0.76

185.39

0.893

0.33

0.97

0.747

0.872 0.81

High risk

Imotski

Zadar

Nin

City

Buildings

Tables 9 and Figure 6 show results of the presented evaluation of seismic risk of 127 cities in Croatia using the proposed methodology for Counties with low risks (0.5) and cities with moderate (0.50.75) to high (0.75) individual risk. Figure 6 shows that the eastern and middle localities in Croatia have the least critical situation, from the point of view of physical and social seismic risk, because the risk indicator is negligible. The localities with a greater risk factor are the north and west of Croatia. The highest values of the greater risk index, in addition to capital city of Zagreb, are the localities of south Croatia, on the Adriatic coast, where during the summer holidays the number of tourists can double the population density (larger potential population risk). All risks considered may have a very strong direct or indirect impact on pre-disaster emergency within the affected area, accordingly for regions with moderate to high seismic risk, a detailed approach is necessary with compulsory early warning or post-disaster emergency and relief actions.

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Table 9 Low and moderate seismic risk of Croatia

Low risk

County Sisak-Moslavina Karlovac Bjelovar-Bilogora Virovitica-Podravina Osijek-Baranja Vukovar-Srijem Istria

Zagreb County

Krapina-Zagorje

Varaždin

Moderate risk

Primorje-Gorski Kotar Lika-Senj Požega-Slavonia Brod-Posavina Zadar Šibenik-Knin Split-Dalmatia Dubrovnik-Neretva Međimurje Zagreb County Krapina-Zagorje Koprivnica-Križevci Primorje-Gorski Kotar Šibenik-Knin

High risk Split-Dalmatia

Dubrovnik-Neretva

City Dugo Selo Jastrebarsko Sveta Nedjelja Sveti Ivan Zelina Velika Gorica Donja Stubica Klanjec Krapina Zabok Zlatar Ivanec Lepoglava Ludbreg Novi Marof Varaždinske Toplice Bakar Kastav Novi Vinodolski Opatija Otočac Senj Pleternica Požega Nova Gradiška Slavonski Brod Benkovac Biograd na Moru Zadar Drniš Šibenik Supetar Korčula Prelog Samobor Zaprešić Oroslavje Koprivnica Crikvenica Kraljevica Rijeka Knin Imotski Kaštela Makarska Omiš Sinj Solin Split Trilj Trogir Vrgorac Dubrovnik Metković Opuzen

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Rbuild 0.470 0.716 0.526 0.618 0.662 0.496 0.354 0.518 0.468 0.503 0.472 0.486 0.516 0.498 0.510 0.557 0.444 0.632 0.637 0.549 0.696 0.558 0.555 0.516 0.569 0.549 0.477 0.581 0.703 0.639 0.427 0.644 0.435 0.856 0.706 0.520 0.733 0.764 0.556 0.75 0.75 0.747 0.773 0.751 0.756 0.863 0.738 0.789 0.719 0.678 0.731 0.946 0.849 0.486

Rpop 0.621 0.491 0.687 0.485 0.629 0.722 0.614 0.607 0.656 0.468 0.541 0.522 0.601 0.510 0.467 0.474 0.643 0.164 0.659 0.099 0.064 0.342 0.527 0.556 0.582 0.144 0.636 0.569 0.174 0.491 0.658 0.462 0.516 0.75 0.888 0.768 0.762 0.857 0.743 0.76 0.377 0.872 0.784 0.907 0.445 0.746 0.784 0.789 0.331 0.761 0.226 0.913 0.952 0.837

R 0.20 0.27 0.10 0.26 0.17 0.06 0.06 0.546 0.604 0.606 0.552 0.646 0.609 0.484 0.562 0.526 0.486 0.507 0.504 0.559 0.504 0.489 0.515 0.543 0.398 0.648 0.324 0.380 0.450 0.541 0.536 0.576 0.347 0.557 0.575 0.439 0.565 0.542 0.553 0.475 0.803 0.797 0.644 0.747 0.810 0.650 0.76 0.558 0.810 0.778 0.829 0.601 0.804 0.761 0.789 0.525 0.720 0.478 0.930 0.901 0.662

Ploče Zagreb

City of Zagreb

Sismic risk for buildings - Rbuild

≤0.2

0.21-0.35

Seismic risk for population - Rpop

0.36-0.5

0.782 0.902

0.645 0.901

0.714 0.902

Average relative seismic risk - R

0.51-0.74

≥0.75

Fig. 6 Seismic risk for Croatia according to Relative RAPID seismic risk assessment

5 Validation of relative RAPID seismic risk assessment for L’aquila province The study area used to validate the proposed methodology is L'aquila province, in Italy, a dynamic and data-rich region that has witnessed disastrous earthquake events. The most recent one was an Mw 6.3 earthquake with shallow focal depth (10 km) which originated in central Italy in the vicinity of L’Aquila, a city of about 73,000 people that is the capital of the Abruzzo region on Monday April 6, 2009 at 3:32 a.m. The earthquake killed 305 people and injured 1500. The population affected by the earthquake crisis and assisted by the Italian Civil Protection reached 67500 in April 2009 (JRC report, 2011). There are several different detailed analysis about vulnerability of buildings under this earthquake, but the most completed was JRC report (Bossi et al, 2011), which results will be used for validation of assessment presented in this study. This event was the strongest of a sequence that started a few months earlier and numbered 23 earthquakes of Mw>4 between 03/30/09 and 04/23/09, including an Mw 5.6 on 04/07 and an Mw 5.4 on 04/09 (EERI report, 2009). The earthquake struck when most people were sleeping (in relation to national census population data). Many of the region’s cultural sites were badly damaged or destroyed (in relation to large impact factor Fbuild,1=0.941 for old buildings), including Romanesque churches, palazzi, and other monuments dating from the Middle Ages and Renaissance. The historic centers of villages in the Aterno River valley southwest of L’Aquila — Onna, Paganica, and Castelnuovo — were essentially obliterated, with shaking intensities of up to X on the MCS Scale. According to this methodology and its indirect applicability, all factors must be made for the observed country. All indicators were therefore created for the area of Italy (105 cities). According to the national census data by ISTAT 2011, lognormal functions for determining impact factors for construction age of buildings were obtained for entire Italy and presented in Figure 7. The weight factors wi remained unchanged (Table 5) with respect to similar construction methods in time ranges in Croatia and Italy.

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1.0

0.8

0.8

Fbuild,2

Fbuild,1

1.0

0.6 0.4 0.2

0.6 0.4 0.2

0.0

0.0 0

20000

40000

60000

80000

100000

0

Number of buildings build before 1919 empirical

1.0

1.0

0.8

0.8

0.6 0.4 0.2

80000

100000

empirical

0.6 0.4

0.0 0

20000

40000

60000

80000

100000

0

Number of buildings build between 1946-1961 logncdf(mu=10.124; sigma=0.579)

20000

40000

60000

80000

100000

Number of buildings build between 1962-1971

empirical

logncdf(mu=10.380; sigma=0.589)

1.0

1.0

0.8

0.8

Fbuild,6

Fbuild,5

60000

0.2

0.0

0.6 0.4 0.2

empirical

0.6 0.4 0.2

0.0

0.0 0

20000

40000

60000

80000

100000

0

Number of buildings build between 1972-1981 logncdf(mu=10.281; sigma=0.701)

20000

40000

60000

80000

100000

Number of buildings build between 1982-1991

empirical

logncdf(mu=9.861; sigma=0.801)

1.0

1.0

0.8

0.8

Fbuild,8

Fbuild,7

40000

logncdf(mu=9.696; sigma=0.668)

Fbuild,4

Fbuild,3

logncdf (mu=9.937; sigma=0.783)

20000

Number of buildings build between 1919-1945

0.6 0.4 0.2

empirical

0.6 0.4 0.2

0.0

0.0 0 20000 40000 60000 80000 100000 Number of buildings build between 1992-2000

logncdf(mu=9.337; sigma=0.709)

0

20000

40000

60000

80000

100000

Number of buildings build after 2001 logncdf(mu=9.345; sigma=0.788)

empirical

empirical

Fig. 7 Functions of impact factors for buildings vulnerability Fbuild,i for Italy (mu, sigma – mean and standard deviation of lognormal function)

According to the seismic hazard map for Italy (INGV, 2004), peak ground accelerations for all cities was extracted and the hazard factor FH lognormal distribution was determined (Fig. 6.)

14

1.0

0.8

FH

0.6

0.4

0.2

0.0 0.0

0.1

0.2 PGA (g)

0.3

logncdf(mu=-1.951; sigma=0.421)

0.4 empirical

b)

a)

Fig. 8 a) Seismic hazard map; b) seismic hazard factor FH for Italy (mu, sigma – mean and standard deviation of lognormal function)

Data for population density are obtained on the basis of the ISTAT 2001 data for 8105 municipalities in Italy which resulted in the lognormal cumulative exposure function of population Epop. 1.0 0.8

Epop

0.6 0.4 0.2 0.0 0

200

400 Population density

600

800

1000

(people/km2)

logncdf(mu=4.91; sigma=1.22)

empirical

Fig. 9 Exposure of population Epop for Italy (mu, sigma – mean and standard deviation of lognormal function)

According to JRC report (2011) it is common to classify buildings by the period on construction, which relates to the codes applicable at that time, thus performance of buildings (reinforced concrete and masonry) was evaluated by year of construction and state of conservation (excellent, good, poor, bad). Reinforced concrete buildings constitute 22% and masonry building 71% of the residential building stock of the L’Aquila Province (ISTAT, 2004). Table 10. Number of RC and masonry residential buildings in L’Aquila Province (JRC report, 2011) State of conservation for RC buildings Year of construction before 1919 1919-1945 1946-1961 1962-1971 1972-1981 1982-1991 after 1991

excellent

good

poor

bad

0 157 309 934 2443 3506 3077

0 546 1128 2365 3776 2925 859

0 152 242 261 367 159 40

0 7 17 9 9 18 5

State of conservation for masonry buildings total

excellent

good

poor

bad

total

0 862 1696 3569 6595 6608 3981

7034 3624 4302 5569 6742 4179 2452

36529 22252 23670 23783 17373 6318 1620

23778 14716 11999 6430 2686 696 211

3361 1681 1011 264 74 30 16

70702 42273 40982 36046 26875 11223 4299

15

Total number of buildings (255711) for validation was extracted from Table 10 according to total number of masonry buildings+total number of RC buildings and fragmented by construction age. This number was not used from Census data because of the difference between total number of buildings according to ISTAT 2001 (200064) and JRC report data based on the field mission in May 2009 (updated number of buildings). The number of damaged buildings (211383) was obtained according to the state of conservation (good, poor, bad), whereby level of damage or damage state was neglected in relation to proposed assessment. Table 11 Calculation of vulnerability for buildings in L’Aquila Province Buildings

Hazard

L’Aquila Province

70702 43135 42678 39615 33470 17831 8280

Total

255711

0.941 0.928 0.820 0.614 0.553 0.459 0.328 0.341

0.27 0.24 0.14 0.09 0.08 0.07 0.06 0.05

Validation Number of damaged buildings (Table 10)

Number of predicted damaged buildings (Vbuild,i × FH×255711)

63668 39354 38067 33112 24285 10146

64495 56624 29200 13965 11172 8125

0.018

2751

4570

0.734

211383

186377

Number of FH Construction Fbuild,i wi Vbuild buildings (Fig. 8.b) age (Fig. 7.) (Table 5) (Eq. 5) (Table 10) for 0.35g Before 1919 1919-1945 1946-1961 1962-1971 1972-1981 1982-1991 After 1991

Risk

0.254 0.223 0.115 0.055 0.044 0.032

0.993

Rbuild (Eq. 3)

0.729

Table 11 and Figure 9 show results of the presented validation of vulnerability for buildings using the proposed methodology for L’Aquila Province. Impact factors Fbuild,i for every construction age is derived from cumulative lognormal functions presented in Figure 7 and then multiplied with relative weights (Table 5) for each factor according to Equation 4. It must be noted that according to JRC report there was 7 partition of construction age, however lognormal distributions were defined in relation to 8 divisions. Applicability of proposed methodology was obtained using mean value of products for Fbuild,7 and Fbuild,8 with number of buildings build after 1991 (8280). In regard to relative risk for buildings, validation was tested for product of Rbuild with total number of exposed buildings. The result present direct accuracy of proposed assessment: relative error of total number of predicted buildings is only 12%. Individual differences for divisions based on construction age is larger, but it must be noted that proposed method is relative risk assessment and that this validation confirms its applicability for calculation of number of predicted damaged building or threatened building under the expected earthquake excitation. 211383 186377

3,00,000

No of buildings

2,50,000 2,00,000 1,50,000 1,00,000 50,000 0 before 1919 1919–1945 1946– 1960

1961-1970

1971– 1980

1981-1990

after 1991

all

Construction age all damaged predicted

Fig. 10 Validation of Vbuild for L’Aquila Province

Another part of relative risk assessment which must be validated for L’Aquila Province is exposure of population (Table 12). Data of an exposure of population (number of inhabitants and population density) were derived from Census data (ISTAT, 2001), and then transformed into Epop according to cumulative lognormal distribution presented on Figure 8. The hazard factor was obtained from Figure 16

7.b), and then after multiplication with Epop, relative potential risks were calculated in terms of risks for population Rpop (equation (3), related to the population density. Validation was tested in regard to number of threatened inhabitants (67500) according to JRC report (2011) with comparison of product of Rbuild with total number of inhabitants for L’Aquila Province. The results again present great accuracy of proposed assessment: relative error of prediction is only 8%. This validation confirms its applicability for calculation of number of predicted threatened inhabitants under the expected earthquake excitation. Table 12 Calculation of exposure for population in L’Aquila Province Population

Hazard

Number of FH Pop. Density Epop inhabitants (Fig. 8.b) (ISTAT 2001) (Fig. 9) (ISTAT, 2001) for 0.35g L’Aquila Province

297424

59.1

0.247

0.993

Risk

Validation Number of Number of predicted Rpop threatened threatened inhabitants (Eq. 4) inhabitants (Rbuild×297424) (JRC report, 2011) 0.245

67500

72868

It can be concluded that the proposed relative RAPID assessment, although modeled for the relative relations of risks in one country, with application of this validation showed expected real risk due to the earthquake in L’Aquila 2009. 6 Conclusion The concept of seismic risk is complex and in order to account for all important parameters, it is useful to allocate corresponding vulnerability indicators as well to indicate whether they have a general importance on global risk. In order to compose useful and applicable indicators for risk assessment, building vulnerability and population exposure were examined and evaluated for territory of Croatia as developing country, and afterwards validated for A’quila Province for territory of Italy. The primary objective of this study was to develop a relative risk assessment in terms of deriving measures of risks that can be used in subsequent research to examine the relative importance of physical (building) and social (population) variables that influence vulnerability. It is, however, worthwhile to highlight indications about the potential vulnerability scores of social and physical variability in the study area. It is worth nothing that this research was based on the general global distribution of risk values for Croatia with emphasizing which cities and areas must be taken seriously in terms of risk and where detailed risk analysis is indispensable. An additional contribution of this study is that the proposed methodology can be applicable to other countries. Results for L’aquila Province indicated prediction of realistic risk for the study area based on vulnerabilities of buildings and exposure of population according to comparison within detailed vulnerability analysis. 7 References Aguilar-Meléndez A, García-Elías A, Pujades LG, Barbat AH, Lantada N, Ordaz MG (2012) Probabilistic assessment of the seismic risk of Barcelona, 15th World Conference on Earthquake Engineering (15WCEE), Lisbon, Portugal. Alexander D, Magni M (2013) Mortality in the L'Aquila (Central Italy) Earthquake of 6 April 2009, PLoS Curr. 2013 January 7; 5: e50585b8e6efd1. Aubrecht C, Fuchs S, Neuhold C (2013) Spatio-temporal aspects and dimensions in integrated disaster risk management, Natural Hazards, Volume 68; pp. 1205–1216.

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