The Middle East exposure database covers the building stock for 18 countries, divided into three occupancy classes:, residential, commercial and industrial. The database provides number of buildings, number of dwellings, human population, and economic value, each expressed in terms of urban and rural areas. The development of the exposure model relied mostly on national housing census surveys, which were collected at the lowest available administrative division. The following steps illustrate the development process:
Identification of the predominant building typologies and construction techniques. The identified building typologies are classified according to the GEM Taxonomy for each country, where each building type is defined by the lateral load resisting system material, construction technique, type of lateral load resisting system, ductility level and number of floors. This step builds on the available literature and the feedback from local experts in the region.
Mapping identified building types to census information. In this process census variables (i.e building type, wall material, construction age, etc) are related to the common building types in each country. Given, the generic nature of the census variables, building classes are assigned by giving weights to the probable types. For example, buildings with stone facade in Jordan could be stone masonry or reinforced concrete frame cladded with stone. In order to distinguish them, building age has been assigned different weights depending on the construction epochs.
Estimation of floor area per building or dwelling. Most of the statistical information from the Middle East provides number of buildings for residential occupancy. In this step, building floor area is estimated based on the average dwelling size and floors per building type.
Estimation of replacement costs. The economic values of structural, non-structural and contents components are estimated based on occupancy type and construction quality. The quality is allocated to each building type depending on the construction material and settlement type (e.g. urban, rural).
Middle East Exposure Map
Additional details about the exposure datasets for the residential building stock of Jordan, Syria, Palestine, Saudi Arabia, Lebanon, United Arab Emirates, Yemen, Oman, Kuwait, Qatar, Bahrain and Iraq can be found in Dabbeek and Silva (2019) “Modelling the residential building stock in the Middle-East for multi-hazard risk assessment”. Natural Hazards, in Review. The exposure datasets can be downloaded here.
The vulnerability component characterizes the likelihood to suffer damage or loss given a hazard intensity. The relation between probability of loss and hazard intensity is expressed by a vulnerability function, whilst the relation between probability of damage and hazard intensity is represented by fragility functions. Despite the notable advances in regional seismic vulnerability modelling in the last three decades, a uniform set of vulnerability or fragility functions covering all of the building classes in the Middle East was not available. Moreover, with a few exceptions, most of the existing vulnerability functions have not been tested against damage data from previous events and have not been applied within a probabilistic framework for earthquake loss assessment. In general, this approach relies on the following steps:
Identification of the most common building classes in the region, using peer-reviewed literature, web surveys (https://platform.openquake.org/building-class/), and World Housing Encyclopedia reports.
Development of simplified numerical models for each building class, using data from the literature and results from experimental campaigns (e.g. yield and ultimate global drift, elastic and yield period of the first mode of vibration, participation factor of the first mode of vibration, common failure mechanisms). Some of the building classes had to be explicitly modelled using complex 3D models due to the lack of information in the literature.
Selection of ground motion records using local strong motion databases, and considering the local seismicity and tectonic environment. To this end, seismic hazard disaggregation at the location of the most urbanized centers supported the identification of the combinations of magnitude and distance, which contribute the most to the seismic hazard. The use of a large set of actual time histories aims at propagating the record-to-record variability to the vulnerability assessment.
Performing nonlinear time history analysis to evaluate the structural response (i.e. engineering demand parameter (EDP) – maximum displacement and acceleration) of the simplified numerical model against the selected ground motion records. This step uses the open-source package for structural analysis OpenSees, and the Risk Modelers Toolkit supported by GEM.
Evaluation of the structural responses of the numerical models in order to evaluate the evolution of damage with increasing hazard intensities. In this process, the probability of exceeding a number of damage states for a set of intensity measure levels is defined (i.e. fragility functions).
The fragility functions can be converted into vulnerability functions (i.e. probability of loss ratio conditional on ground shaking) using a damage-to-loss model. Such functions can be used directly in the assessment of economic and human losses due to earthquakes.
This framework is supported by a set of tools that can be improved be improved upon the release of new models and datasets. As an example, fragility models for the four most common building classes in the Middle East are illustrated below.
3. Seismic Hazard
The main components concerning the probabilistic seismic hazard model for the region can be found in the associated technical documentation at:
https://hazard.openquake.org/gem/models/ARB/ and https://hazard.openquake.org/gem/models/MIE/ and the seismic hazard in terms of peak ground acceleration (PGA) for a probability of exceedance of 10% in 50 years (equivalent to approximately 475 years return period) is presented in the figure below.
Middle East Hazard Map
4. Seismic Risk Results
The results show that the risk is significant in the majority of Middle East countries, specifically Iran, Pakistan and Syria have the highest absolute economic losses. While in relative terms, Iran, Afghanistan and Armenia have the highest risk. In addition, the results show that Bahrain and Qatar have the lowest losses. Generally, the countries with larger building stock (i.e Pakistan, Syria and Afghanistan) have higher absolute losses, while countries with smaller building stock located in less populous areas have higher relative losses (i.e Lebanon, Jordan, Armenia and Georgia). Iran is the exception, in which both absolute and relative losses are high although buildings stock is large and distributed over large area.
Middle East Risk Map
5. Partners and Contributors
The Africa seismic risk model extensively relies on the enthusiasm and commitment of various organizations that openly collaborated with GEM and its partners. The creation of this model would not have been possible without the support provided by many experts. A list of the individuals that contributed to the development of the Africa seismic risk model is provided below.