International Journal of Health System and Disaster Management

CASE REPORT
Year
: 2015  |  Volume : 3  |  Issue : 3  |  Page : 177--184

Multi hazard vulnerability analysis: The casualty model and its implementation


Leila Eshrati, Amir Mahmoudzadeh, Masoud Taghvaei 
 Department of Geography and Regional Research, Research Institute of Shakhespajouh, University of Esfahan, Esfahan, Iran

Correspondence Address:
Leila Eshrati
Department of Geography and Regional Research, Research Institute of Shakhespajouh, University of Esfahan, Esfahan
Iran

Abstract

The frequent occurrence of damaging natural and technological hazards clearly demonstrates the urgent need of study of multi-hazards vulnerability assessment methods to effectively reduce the impact of multi hazards in the built envirenment. This study presents the Multi Hazard Casualty Model (MHCM). Two types of hazards will be assessed, namely earthquake and fire following earthquake. Our objective in this paper is to present the casualty model, to analyse the applicability of casualty model for the assessment of multi-hazards vulnerability of building and human with a GIS-based Analysis. Methods used in this paper are based on theoretical approach and documentation. The approach used for casualty model is based on semi-quantitative and quantitative analysis. The analytical vulnerability model use building damage and consequential triggering hazard (e. g. fire) for the evaluation of human casualties. An attempt to develop casualty model based on interactions among hazards and/or domino effects. To this task is to correlate second hazard casualties directly with triggering hazard. The focus is on indicating hazard relations to understand how potential hazards of various degrees and magnitudes might result in MHCM. It incorporated considerations for multiple and cumulative hazards occurrences into the overall assessment framework and methodology. Triggering (domino) effects, and building vulnerability are two major factors that can lead to effected casualties in MHCM. The model is implicated with data collected in a part of Shiraz City. The casualty model shows that structural failure is the primary cause of earthquake built environment casualties and that physical damage potential should be the foundation of estimation fire followind earthquake casualty. Other factors should also be integrated. In the present study has been done to present the casualty model and to analyse the applicability of casualty model for the assessment of multi-hazards vulnerability of building and human.



How to cite this article:
Eshrati L, Mahmoudzadeh A, Taghvaei M. Multi hazard vulnerability analysis: The casualty model and its implementation.Int J Health Syst Disaster Manage 2015;3:177-184


How to cite this URL:
Eshrati L, Mahmoudzadeh A, Taghvaei M. Multi hazard vulnerability analysis: The casualty model and its implementation. Int J Health Syst Disaster Manage [serial online] 2015 [cited 2021 Apr 19 ];3:177-184
Available from: https://www.ijhsdm.org/text.asp?2015/3/3/177/157411


Full Text

 Introduction



The built environment has long suffered from natural and technological hazards that have often caused loss of life and buildings in the past. An earthquake causes large-scale damage and destruction resulting from ground shaking. Fires caused casualty after damages to oil and gas pipelines and to electrical power transmission lines damaged by an earthquake. Fires started from the effects of an earthquake are speared in time. Fire is more dangerous than the earthquake itself. Some secondary hazards have been the cause of significant casualties, for example fire following earthquakes in1990, the Oakland Hills earthquake in U.S affected perhaps 3500 people. [8] In 1994 the Northridge earthquake southern California affected perhaps 3 million people, approximately 110 ignitions and 67 people killed. [16] The 1995 Kobe earthquake in Japan affected perhaps 1.5 million people, had thousands of collapsed buildings, approximately 110 ignitions and 6,000 people killed. [15] In 1906 San Francisco perhaps 3,000 people were killed, [18] and perhaps 142,000 people were killed in 1923 Tokyo from fires following an earthquake. [19]

Our objective in this paper is to present the casualty model, to analyse the applicability of casualty model for the assessment of multi-hazards vulnerability of building and human with a GIS-based analysis.

The casualty model has the characteristics such as estimates a potential multi hazard casualities; facilitates local understanding of multi hazard, vulnerability, and risk management programmes; allows for an easily interpreted, quantitative comparison of the relative hazard; it is flexible enough to handle specific available casualty data and based on interpretation of hazards for casualty estimations; and Data formats are flexible enough to accept currently available data, and to re-evaluate previously collected data. This model provides a simple and consistent framework for multi hazard casualty estimation and formats for data collection that is involved in casualty estimation.

This study was split into three phases:

Phase1 concentrated on a review of multi hazard risk and vulnerability assessment, and loss estimation. The 2 nd phase is the data analysis stage (presented the casaulty model) where the assembled data was analysed. By hazard event and also across events to test the relationships of casualty to the main explanatory variables of building type, building damage level, earthquake intensity, etc., where hazard relationship (dominated effect) would be presented. The final phase, phase 3, concentrated on the development of the casualty estimation model to the case study Shiraz in Iran, data collection and development of multi hazard snarioes.

Review of existing models

There are several functioning models for multi hazard risk and vulnerability assessment, and loss estimation, but none of them computes the causal relationships frequently occur between hazards. However, these models very rarely account hazard relations. However, they are not in event tree format and do not account for other kinds of losses (social, economic and environmental losses and the indirect losses) related casualties, nor do they account for multi hazard dominant effects. The casualty model fills in this gap in the multi hazard vulnerability assessment modeling.

Several studies and methodologies had been developed during these years to model the number of casualtis and physical damage caused by hazards like earthquakes and fire following earthquakes. Casualty estimation methodologies are also summarized below:

Applied Technology Council (ATC) suggested casualty rates that limited post-earthquake fatality data in 1985. The HAZUS casualty rates were obtained by revising those suggested in ATC and in fact is based on the models suggested by Coburn and Spence. Coburn and Spence proposed , the fatalities estimation due to physical damage, which is the important indicator for earthquakes. [3],[4]

Earthquake Engineering Department (EED) presented The KOERI methodology for the estimation of number of casualities.JICA proposed indicators for the death and the building damage in Turkey. [7],[8]

Published data on collapse-related casualty rates from the earthquake is limited. Collapse-related casualty rates on the casualty models suggested by Noji (1990), Murakami (1992), Durkin and Murakami (1989), Shiono et al., (1991), Coburn et al., (1992). The California Highway Patrol proposed casualty rates for complete structural damage. [11]

 Case History



 Materials and Methods



This vulnerability assessment model provides a methodology for estimating casualities caused by multi hazard. The casualty model utilizes a theoretical approach. The built environment is a 'bottom-up' approach that relates casualty to the performance of specific building types. [2] The earthquake casualty estimation processes in local scale base on damage scenarios method that is entirely based on vulnerability curves for earthquakes and provides an estimate of fire following earthquake that rely on fire behaviour potential assessment with simulation model. [5],[6] In this study risk assessment shall include an overview and analysis of the vulnerability to the hazards, based on estimates provided in local risk assessments as well as multi hazard vulnerability assessment, and most vulnerable to damage and loss associated with hazard events.

The vulnerability assessment for determining potential loss estimations is based on the casualty model.

 Results



The casualty methodology

The model estimates casualties directly caused by physical damage and for following earthquake. Not only casualties are not directly derived from secondary hazard damage but also are derived from triggering hazard damage output. Secondary event such as falls, fire and other causes directly attributable to damage would increase estimates of casualties, such cases have involved the combination of a number of conditions, which are of low probability of occurrence. Aroni and Durkin studies suggest that falls, heart attacks, car accidents, fire and other causes not directly attributable to structural or nonstructural damage would increase estimates of casualities. [1] This study present fire following earthquakes as secondary hazard event.

The casualty model can establish a direct relation between physical damage grade and casualty percent.

In triggering event, physical damage will most likely control the casualty estimates. In earthquake where there will be a large number of physical damages, there will be a proportionately larger number of casualties. The secondary event generated casualties.

The casualty model is an event tree model that uses the relationship between the physical damages and the social, economic and environment losses and the indirect losses which is calculated by combining the following relationships. In this study focus exclusively on vulnerability assessment, as regard to vulnerability as part of a risk assessment and design vulnerability map showing the relationship of the hazards.

The methodology takes into account a wider range of causal relationships in the casualty modeling. At last a final vulnerability will be estimated according. The methodology is shown in flowchart [Figure 1].{Figure 1}

The MHCM is defined as the probability of harmful consequences, or expected direct loss (physical, social, economic or environment damaged) or/and indirect loss resulting.

Interpretation of hazards as a recognized relevant issue in MHCM, is included in the methodology.

In this paper, the vulnerability is investigated without taking into consideration the social, legal or cultural setting and indirect damages. The focus is on the physical damages and casualty, particularly on the impact of multi hazards on the built environment. An extension of the model proposed. Following section is discussed general procedure for MHCM.

 Discussion



Form of casualty estimate

When a hazard leads to the damages of a particular built environment, it starts a domino reaction in which damages of other dependent environment are triggered. This is also known as domino effected. For example, the Kobe earthquake of 1995 damaged the power transmission lines, that led to the disrupted of 90% of the traffic signals on the streets then cut off gas and phone connections. Finally 531 fires broke out in different parts of Tokyo City, most of them resulting from the earthquake as a triggering hazard event. [13],[14],[15],[16],[17] This study includes formulation of domino effects, comparison of vulnerabilities of multi-hazard mechanisms, and evaluation of casualty. MHCM have been made for over specific needs, usually preparedness and focused on specific scenarios.

Casualties caused by multi hazard can be modeled by developing a tree of events leading to their occurrence. As with any event tree, the casualty event tree begins with an initiating event (triggering scenario) and follows the possible course of hazards leading to loss of life or injuries. Creation a set of scenarios to correlate triggering event is one of peculiar aspect of this procedure. Triggering hazard, dominate effect and secondary hazard would be correlated in a parallel sequence series of happenings through an event tree that is caused risk scenarios. Casualty rates are inferred by available statistics data and combined with expert opinion.

The casualty methodology estimate the casualties from structural damage, the model combines a variety of inputs including the probability of being in the damage state, the probability of collapse and the relationship between the damage state with specific casualty inputs provided for each damage state (state1-slight, state 2 moderate, state 3 Extensive, state 4 complete with collapse or non-collapse structural damage) and for following earthquake casualty as secondary hazard in combination with occupancy data and time hazard events. Particular MHCM could be simulated with an event tree as shown in [Figure 2].{Figure 2}

The methodology takes into account a wider range of causal relationships in the casualty modeling. At last a final vulnerability will be estimated accordingly. Casualty rates are different that is depend on causal events proceeding, damage state, collapse as well as causal consequence for following earthquake. For the assessment of human casualties from damage data computed from intensity based vulnerabilities it can be assumed that the number of deaths will be equal to the number of buildings with damages in state1 to state 4 level.

The structure would be affected by both earthquake and fire that may be differed before the earthquake. Secondary hazard effects after and/or during the triggering hazard cause great differences on the amount of casualty. Multi-hazard casualities will be estimated based on the causal consequence buildings in various damage states and fir following earthquake.

Shiraz city case study results

In this study, we are developing a comprehensive MHCM and testing this model in local level at part of Shiraz. Analytical loss models use building damage and consequential physical damage (e.g. fire) as the assessment of casualties. This model has been applied to Shiraz City which is located in south-west of Iran [Figure 3].{Figure 3}

The seismic analysis based on the Deterministic Seismic Hazard Analysis (DSHA) method indicated that there are several active faults in the vicinity of shiraz city, such as Sabzpushan, Kazeron-karbas, Ardakan-sarvestan, Bamou and Zarghan [Figure 3]. According to studies and damping equations for Iran, their acceleration and intensity degree will be 0.2 g, [12] Therefore the Sabzepushan fault is selected for an earthquake scenario process for this research. According to the fault earthquake scenario, an earthquake of 6.3 M magnitude at 10 km in depth and at 5 km distance from the city was simulated by the model. Loss estimates for Shiraz are performed for the part of the city where approximately 31,978 building of different types and with different land uses exist. The vulnerability assessment, the estimation of direct physical damages and human casualties are performed for each of the buildings (about 397905 inhabitants).

To prepare the casualty map, four basic layers of the area; were combined such as, the seismic layer, the population layer, the building vulnerability layer and fire damages layer. The first term in equation represents the physical building damage. The second term represents the damages following fire. The final term expected number of occupants killed is a product of the number of occupants of the building at the time of earthquake and fire. This provides an indication of the percentage of affected people in the specific damage zone. The report showed that more than 9% of the population would be killed as a result of the occurrence of an fire following earthquake of 6.7 M.

Physical vulnerability

Vulnerability can have many dimensions (physical, economic, social, etc.). Physical vulnerability is perceived as the degree of loss to elements, within the area affected by hazard. Physical vulnerability assessment for multi hazard. Developed approach is based on indicators of vulnerability, as a step towards local-scale analysis where vulnerability assessments.

In this study, By analysing the vulnerability, we identify the consideration of important vulnerability indicators and the selection of all the relevant indicators [Table 1]. The structure type is a very important indicator of vulnerability because a direct relationship has observed between the damage of building and the number of casualties.{Table 1}

The earthquake casualty estimation processes rely on building damage level to express physical vulnerability.

The physical vulnerability processes for earthquake express fragility curves to building damage level.

The casualty model uses four structural damage states (slight, moderate, extensive, and complete) computed by fragility curves. The HAZUS method use for vulnerability assessment, it uses damage functions and fragility curves whose output is damage state For each census tract and each model building type. [8] In [[Table 2], the damage level based on land use of building is described in four different classes.{Table 2}

Building types based on building classes FEMA-178. [9],[10] Building height were added to show the variation of typical building periods and other design parameters with building height. Structural building types for multi hazard casualty model in Shiraz city is included steel-braced frame, concrete-moment frame, concrete-shear walls and unreinforced masonry bearing walls.

Consequently, based on the fragility curves a local scale for building vulnerability was developed that certain types of buildings will be damaged in specific ranges of earthquake intensity.

Masonry buildings (adobe, unreinforced masonry) were built in Shiraz City and more of them were destroyed as a result of the occurrence of an earthquake with 6.7 M.

Earthquake assessment

Determining seismic sources is one of the significant steps in the seismic hazard assessment. Therefore, the fault map is prepared based on geological maps. With the use of aerial photos Land Sat 7 and field studies; active faults and seismic resources are determined [Figure 3]. The earthquake's magnitude is computed for Sabze-poshan fault-line.

Fire assessment

Fires following earthquakes can cause losses. These losses can occur following physical damage caused by the earthquake, such as complete damage of buildings with or without collapse. The indicators of the fires following an earthquake, presented in [Table 2].

Losses caused by the fire, according to the presented indicators have been checked by the GIS software [Figure 4] and as 'fire damages layer' in the final estimation of casualty is presented.{Figure 4}

Population vulnerability

Population vulnerability have a dynamic change through time, because under the consideration of complex interaction between natural systems and social systems, especially as much as people are concerned.

There are indicators that play a major role in the casualty estimation [Table 1]. In relation to the population inside the building, proposed a 'building occupancy' indicator, which measures the percentage of the population at the time of the hazards.

The population dataset is usually obtained from census and casualties are calculated at the census tract level for three times of day [Figure 5]a-c.

Building collapse is the main cause of casualties, accounting [Table 3]. The expected number of a population who killed (ENoccupants killed) is an outcome of the number of occupants of the building at the time of hazard (Noccupants) and the probability of an occupant being killed (Pkilled) in each specific damaged state zone (state 1; slight, state 2; moderate, state 3; extensive, state 4; complete with collapse or non-collapse structural damage). They are estimated based on The methodology of HAZUS-MH. The casualty model has organised hazards data and assets for protection in HAZUS-MH software. Accuracy of the model for multi-hazard casualties depends clearly on the rate of occupancy and on the damage state of a building.{Figure 5}{Table 3}

Using the population dataset, the building vulnerability data and fire damages, the casualty report is prepared (Eq.(1)).

ENoccupants killed = Noccupants*Pkilled (1)

The best correlation seems to exist between the number of deaths and the states of physical damage. Relationship between the number of deaths and the states of physical damage is shown in [Table 3].

The smaller values are related with damage state1 of the buildings, the larger Casualty values are given for total collapse in damage state 4. There is a direct relationship between the damage of building and the number of casualties.

Research findings demonstrate that the output of simulation modeling depends on the quality of data input and also on the relationship established and manipulated amongst the spatial data layers.

 Conclusion



In this study focus exclusively on vulnerability assessment, as regard to vulnerability as part of a risk assessment. The casualty model is an event tree model that uses the relationship between the physical damages and the social, economic and environmental losses, and the indirect losses which is calculated by combining the hazards relationship. It is a function of the potential losses from multi-hazard.

This study proposes multi hazard scenario that improve their understanding of multi-hazards and their specific level of vulnerability. A proper understanding of vulnerability will lead to more effective risk assessment, emergency management and to the development of mitigation and preparedness activities all of which are designed to reduce the loss of life.

By analysing the vulnerability, we identify the consideration of important vulnerability indicators and the selection of all the relevant indicators. The most important vulnerability indicators for earthquake and fire following earthquake induced casualty are the state of damage, type of the structure building, the hour of the event and occupation indicator.

The model is applied with data collected in Shiraz city. The multi-hazard scenarios are presented by the casualty model with use of technology such as HAZUS. In this study only take into account two types of elements at risk: Buildings and population. Building information could be obtained from existing cadastral databases, and population data from census database. The population, the building vulnerability, fire damages and the multi hazard casualty maps were produced in this process. Design vulnerability map showing the relationship of the hazards. In triggering event, physical damage will most likely control the casualty estimates. There is a direct relationship between the damage of building and the number of casualties.

This study develops a comprehensive multi hazard casualty model that is applicable at the local level. It can be concluded that the newly developed model is applicable to other multi-hazard processes as well as to further catchments in Iran as well as in other countries with different environmental settings. Based on these observations, we identify the future needs in the field of multi-hazard vulnerability assessment that include indicator based model which uses hazards relationship for analysis.

In conclusion, this study covers the consequences of hazard domino effects on multi hazard vulnerabilities, in the framework of vulnerability analyses. However, further application and assessment of the model is required for various multi hazard events.

 Acknowledgments



The authors are grateful to the Research Institute of Shakhespajouh supporting this study as well. Many thanks also to Cees van Westen, Stefan Greiving and Melanie Simone Kappes for very helpful discussions and valuable advices. Furthermore, the authors want to express their gratitude to the municipal of shiraz city for the provision of information, support and always very constructive feedbacks.

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