To identify a capacity curve, which is defined as the relationship between the base shear force and the lateral displacement of a control node of the building (Goel, 2005), a nonlinear structural analysis method such as the nonlinear quasi-static “pushover” analysis (U.S. Army, 1986; ATC, 1996; FEMA, 2000) is required. Coordinated by Keith Porter, the project features as co principal investigators Anne Kiremidjian, Emily So, Dina D’Ayala, Tiziana Rossetto and Kishor Jaiswal. The number of data points included and their level of detail required for vulnerability analysis/assessment can vary widely, mainly depending on the type of the method selected for the analysis. Vulnerability The concept of DPM, which was initially introduced and described by Whitman, Reed, and Hong (1973), as illustrated in Table 6, and which later became the basis for ATC–13 (1985), was the first of its kind and was most widely used to represent empirical-based building vulnerability in a discrete form. Methods of this category are specifically suitable for poor-quality non-engineered construction whose resistance is difficult to calculate using analytical or numerical methods. Seismic fragility functions on the other hand represent the relation between shaking intensity and the probability of reaching or exceeding a certain limit state – a condition beyond which a structure no longer fulfills the relevant design criteria – such as collapse. Recently, efforts have been carried out within the Global Earthquake Model (Jaiswal, Wald, Perkins, Aspinall, & Kiremidjian, 2013) trying to elicit expert opinion on uncertain quantities and providing clear definitions with respect to biases, assumptions, and expert opinions in order to improve the philosophy of the methods. The location where each IDA curve becomes flat identifies the IM level beyond which it is assumed that global collapse of the building will occur. Defining the characteristics and typology of a building is a major step and represents the starting point of a physical vulnerability assessment. One of the main shortcomings when using intensities to predict earthquake damage may lie in the fact that intensity does not have any connection to the frequency (spectral) content of seismic ground motion. Table 5 shows examples of building damage classifications that are based on a quantitative description and on which element deformations are related to average inter-story drift ratios of structural damage state. Uncertainties are inevitable in any practical study of vulnerability assessment and should be expressed and quantified. Other models that use different parameters can also be found in literature: relating element to yielding and ultimate rotation/displacement limits (e.g., FEMA, 1997; CEN, 2004, 2005; Dolsek & Fajfar, 2004), or relating roof displacement to yielding and ultimate limits, i.e., on a global level (e.g., FEMA, 1997; Giovinazzi, 2005; Barbat, Moya, & Canas, 1996; Kappos, Panagopoulos, Panagiotopoulos, & Penelis, 2006; Lagomarsino & Giovinazzi, 2006). In early studies, when the term physical vulnerability was introduced, the basic principle was to express the seismic performance of a physical element (i.e., an individual building or infrastructure) to a given earthquake ground-motion level. Devastating explosions and fires are possible. These DPM were developed by asking 58 experts to provide low, best, and high estimates of the damage factor (i.e., the ratio of loss to replacement cost, expressed as a percentage) for Modified Mercalli Intensities (MMI) from VI to XII for 36 different building classes. In practice, IM is measured in terms of a macro-seismic intensity (e.g., MMI, MSK, EMS-98, PSI) or in terms of a physical parameter, e.g., peak ground acceleration (PGA), spectral acceleration (Sa), and spectral displacement (Sd). They are based on approaches that correlate the loss,—i.e., the cost of the physical damage to buildings—, with a ground-motion intensity measure. Vulnerability classes are assigned primarily according to the main construction material and then refined according to structural characteristics, construction type (or in case of the EMS: level of earthquake resistant design). This worldwide classification model, which has been developed within the GEM Building Taxonomy framework, is based on data input representing a wide range of building typologies from 49 countries. For instance, the HAZUS building classification scheme (see Table 2a), which was developed by the Federal Emergency Management Agency (FEMA-NIBS, 2003), has been one of the most widely used for vulnerability assessment in the United States. Canada Earthquake Model Region specific innovations in both hazard and vulnerability Earthquake Risk in Canada All Canadian provinces have some degree of earthquake risk. When it comes to assessing the earthquake risk for an area or region with hundreds, often thousands of individual buildings, defining the structural system of each building with its individual structural and nonstructural characteristics would be costly and impractical, if not impossible to conduct. In general, the categorization of building damage can be either done in a qualitative descriptive manner by describing the damaging effects to the structure, or in a quantitative manner by assigning capacity thresholds (i.e., an empirical definition of damage state thresholds) to an individual structural element or to the entire building. Figure 3. Chimneys fracture at the roof line; failure of individual non-structural elements (partitions, gable walls). The following presents a complete overview of project collaborators: The compendium contains 23 vulnerability and 135 fragility relationships constructed mainly from sing-event databases for reinforced concrete and masonry buildings located in Japan, Southern Europe, Turkey and the United States. Lognormal cumulative distribution function, Yamaguchi & Murao (2000); Shinozuka, Feng, Lee, & Naganuma (2000); Sarabandi, Pachakis, King, & Kiremidjian (2004); Rota, Penna, & Strobbia (2008); Liel & Lynch (2012), Basöz, Kiremidjian, King, & Law (1999); O’Rourke & So (2000), Rossetto & Elnashai (2003); Amiri, Jalalian, & Amrei (2007). The same may apply to the insurance and reinsurance industry in developing catastrophe (CAT) models. Printed from Oxford Research Encyclopedias, Natural Hazard Science. A number of problems can be associated with the existing empirical methods and approaches for vulnerability assessment. Estimating physical damage, on a local and global level for a building or infrastructure, given a certain ground-motion intensity level is the next major step in vulnerability assessment. The IDA is done by subjecting a building model to nonlinear time-history analysis under a suite of ground-motion accelerograms that are scaled to increasing levels of the IM until collapse is reached (see Figure 7). Overall, the building classification should cover all types of conventional buildings that are available and that are representative for the target area. In principle, each method used to provide building vulnerability information is based on expert opinion to some extent, since the damage predictions are based on the subjective opinion of the expert when, for instance, using the terms “few”, “many,” and “most”. The methods use different procedures and assumptions that can be based on empirical approaches (Rossetto, Ioannou, Grant, & Maqsood, 2014a), analytical (D’Ayala, Meslem, Vamvatsikos, Porter, & Rossetto, 2015), and expert judgment. as accurately as possible. Accordingly, data input can be provided either qualitatively or quantitatively. These ranges exist because vulnerability also depends on factors other than those previously discussed, such as quality of workmanship, state of preservation, regularity, ductility, position, interventions for strengthening, and earthquake-resistant design level. How to assign a vulnerability class to a building? Since the late 1990s, many methods have been introduced for quantifying physical vulnerability. Recently, the different parameters influencing the development of physical vulnerability models have been investigated (D’Ayala & Meslem, 2013; Rossetto, D’Ayala, Ioannou, & Meslem, 2014) resulting in the development of a Relevance Ranking System that can assist analysts in selecting a vulnerability model appropriate for their application scope. Previous studies mostly focus on generalized vulnerability assessment from landslides or other types of slope failures, such as debris flow and rockfall, while the long-term damage induced by slow-moving landslides is usually ignored. Modified Capacity Spectrum Method (MADRS), Improved Displacement Coefficient Method (I-DCM), Fajfar (2002); Dolsek & Fajfar (2004); Eurocode 8 (CEN 2004), Nonlinear Static Simplified Mechanism-based Procedures, Failure Mechanism Identification and Vulnerability Evaluation (FaMIVE), Bernardini, Gori, & Modena (1990); Cosenza, Manfredi, Polese, & Verderame (2005), Displacement-Based Earthquake Loss Assessment (DBELA), Miranda (1999); Crowley, Pinho, & Bommer (2004), Mechanical Based Procedure for the Seismic Risk Estimation (MeBaSe), Restrepo-Vélez and Magenes (2004); Restrepo-Vélez (2005); Modena, Lourenço, & Roca (2005), Shome & Cornell (1999); Vamvatsikos & Cornell (2002). This present work is part of international collaborative research projects carried out by NORSAR in collaboration with local governmental organizations and research institutions from different earthquake-prone countries. Finally, earthquake engineering professionals from around the world supplied their expert opinion by 5000 HAZUS-based vulnerability functions for 128 building types and 33 occupancy classes. Three percent of the total area of buildings with Complete damage is expected to be collapsed, on average. In this section you can explore the different types of resources available for you to use, to share with others, or to promote GEM with. The outcome was as follows: GEM's Physical Vulnerability project is delivering a dataset of existing and newly derived sets of empirical, analytical and expert opinion fragility and vulnerability functions from around the world that have been quality rated, as well as reports that the methodology behind the dataset and guidelines for creation of new ones. 3.1 Ability to change hazard or exposure level Earthquakes cannot be prevented or predicted nor can the shaking that occurs be reduced. Extensive damage to structural and non-structural components. In fact, this technique of analysis is considered to be quite impractical for everyday use. GEM is an international forum where organisations and people come together to develop, use and share tools and resources for transparent assessment of earthquake risk. Defining the building structural system for vulnerability measurement. The basis for estimating seismic risk lies in combining what we know about seismic hazard (the probability of groundshaking) and physical risk (the exposure of people and structures to earthquakes,and their vulnerability to possible events), expressed in terms of loss or damage. This includes the purpose of the assessment (at both the individual building level and the building stock level), the size of the urban center, the prevalent building typologies within it, and the availability of the required data input (i.e., the quality and level of details) in order to accurately define the typology class and to develop a consistent model that would best represent the real behavior of the individual building or building stock selected, and thereby better quantify the uncertainty (Figure 10). Oxford Research Encyclopedia of Natural Hazard Science, NORSAR, Department of Earthquake Hazard and Risk, EERI Ad Hoc Committee on Seismic Performance, FEMA (Federal Emergency Management Agency), FEMA-NIBS (Federal Emergency Management Agency—National Institute of Building Sciences), The Society for Earthquake & Civil Engineering Dynamics, Concept of Physical Seismic Vulnerability, Methods for Physical Vulnerability Assessment, Physical Damage-to-Ground Motion Intensity Correlation, Physical Damage-to-Economic Loss Correlation, Current Challenges in Physical Vulnerability Assessment, Challenges in Choosing Between Different Methods, Challenges in Selecting Existing Vulnerability Models Database from the Literature, https://doi.org/10.1093/acrefore/9780199389407.013.71, Deterministic earthquake damage and loss assessment for the city of Bucharest, Romania. Tax code: 96059180180 | VAT number: IT02585230184 | PEC: gemfoundation@pec.it, Geoscience Australia (GA): Mark Edwards and. There are many classifications that rely on a qualitative description of the damage effects to a building, but they make use of the concept of building damage states, such as the one adopted in the earthquake loss estimation methodology developed by FEMA and NIBS, commonly known as HAZUS–MH (FEMA-NIBS, 1999, 2003). Earthquake risk maps for the city of Santiago de Cuba (Cuba), using customized and collected physical vulnerability models. It was the fifth most powerful earthquake ever recorded, and within 30 minutes, a 133-foot high tsunami pummeled Japan's northeastern shoreline. In doing so, local experts such as structural engineers or architects have to be consulted in order to identify the local construction typologies and to identify their major characteristics (Lang & Aldea, 2011). At Risk: Natural Hazards, People’s Vulnerability, and Disasters, 2d ed. These 3 approaches are explained in detail, guidance is provided on how to apply them and what factors to take into account, considering also effort and uncertainty. Some Typically, uncertainty in geometric parameters is accounted for by randomizing parameters such as buildings’ plan dimensions, height, and number of stories; uncertainty in structural parameters is accounted for by randomizing parameters such as bay length and column orientation; uncertainty in mechanical parameters of the construction materials is accounted for by randomizing parameters such as compressive strength and elasticity modulus of concrete, tensile strength, and elasticity modulus of steel reinforcement, hardening ratio of steel, and compressive strength of masonry infill; modeling uncertainty is typically introduced in some studies by randomizing the parameters of the hysteric models. In general, most of the existing empirical approaches were developed based on the use of macroseismic intensities for characterizing the earthquake shaking; examples include the Modified Mercalli Intensity (MMI) scale (Wood & Neumann, 1931), Medvedev–Sponheuer–Karnik (MSK) scale (Medvedev, Sponheuer, & Karnik, 1965), European Macroseismic Scale—EMS-98 (Grünthal, 1998), and the parameter-less scale of seismic intensity PSI (Spence, Coburn, Sakai, & Pomonis, 1991). ATC-58-2 (ATC 2003), Vision 2000 (SEAOC 1995), Minor hairline cracking (0.02″); limited yielding possible at a few locations; no crushing (strains below 0.003). Hence, it represents the mean damage an individual building of this typology will experience. Modelling of exposure and physical vulnerability in the most earthquake prone countries is the main goal in the first stage of the project. These include questions such as: How to collect or interpret damage data (which is in turn essential to determining shaking intensity)? The uncertainty in the demand is introduced by the record-to-record variability, which captures the variability in the complexity of the mechanism of the seismic source, path attenuation, and site effects of the seismic event. The final step is to identify the target displacement (or performance point) dp. Figure 1. The database comprises fragility and vulnerability curves, damage-to-loss models, and capacity curves for various types of structures. A recently conducted extensive literature review under the framework of developing the GEM Guide for Selecting of Existing Analytical Fragility Curves and Compilation of the Database (D’Ayala & Meslem, 2013) shows that in most vulnerability studies the examined building is typically simulated in terms of a 2D symmetrical model with deterministic geometrical properties, reducing the ability of the model to capture the real behavior of the building and the variability in the structural characteristics. The vulnerability and risk assessment started by developing a customized building classification scheme for the existing building stock in the city. This method uses a nonlinear pseudo-static structural analysis with a degrading pushover curve in order to estimate the performance points in a similar way to the Capacity Spectrum-based methods. Existing approaches for physical damage-to-ground motion intensity correlations. Table 4 shows examples, from literature, of building damage classifications that rely on a qualitative-based description. Structure may have large permanent lateral displacement or be in imminent danger of collapse due to cripple wall failure or failure of the lateral load-resisting system; some structures may slip and fall off the foundation; large foundation cracks. In other words, it provides a clear picture to understand which type of structure or element is more vulnerable, i.e., likely to suffer damage, and how this vulnerability is affected by the various structural and nonstructural components of a building. Minor spalling in a few places in ductile columns and beams; flexural cracking in beams and columns; shear cracking in joints < 1/16″ width. Nonstructural components can be divided into two categories: those which may contribute to the response behavior of the structure (and thereby to the monetary loss connected to the damage), and those which do not contribute to the response behavior of the structure, but which are important to consider as they contribute to the reconstruction costs. Extensive crushing and spalling of veneers at corners of openings. Vulnerability to Earthquake Hazard: Bucharest Case Study, Romania | SpringerLink. Table 7 shows an example of DPMs for EMS-98 vulnerability class A, containing a qualitative description of the proportion of buildings that belong to each damage grade for various levels of intensity. Guide for conversion from damage to loss in indirect vulnerability curve. Very heavy damage (heavy structural damage, very heavy non-structural damage): Serious failure of walls; partial structural failure of roofs and floors. From a seismic risk analysis perspective, the use of the physical vulnerability concept started with the development of the earthquake loss estimation (ELE) discipline in the early 1980s, which aimed at predicting the consequences of earthquake shaking for an individual structure or for a portfolio of buildings or infrastructure facilities (EERI, 1984). Disasters occur when potentially damaging natural processes interact with elements at risk and their associated physical, social, economic and environmental vulnerability (Birkmann, 2006).Therefore, an important aspect for disaster risk … Hence, special care should be given when selecting the existing vulnerability models that are available from literature, in order to ensure a reliable earthquake loss assessment. • Photos of building Level I + • Plan sketch • Dimensions of key building components (column size, wall layout etc.) Figure 9. (2012) studied methods from different categories which led to the development of guideline documents that would assist analysts in ensuring the consistency between the purpose of the type of analysis (approach/method), the mathematical modeling, and the type and quality of data input to be used (Rossetto, D’Ayala, Ioannou, & Meslem, 2014; D’Ayala, Meslem, Vamvatsikos, Porter, & Rossetto, 2015; D’Ayala & Meslem, 2013; Jaiswal, Aspinall, Perkins, Wald, & Porter, 2012). The EMS-98 building classification concept, understandably, represents a major simplification and comes with a number of difficulties, such as the fact that building height is not addressed (this especially applies to engineered building typologies such as RC or steel, where all height ranges are involved), the fact that the concept of vulnerability classes principally allows buildings of completely different construction typologies to be assigned the same vulnerability class, leading one to expect them to demonstrate the same damage extent. This is the approach generally used in analytical-based vulnerability assessment. However, the main challenge in using these predefined physical vulnerability models, is how to identify suitable ones in order to ensure a reliable earthquake loss assessment. Extensive damage to beams; spalling of cover and shear cracking (<1/8″ width) for ductile columns; minor spalling in non-ductile columns; joint cracks < 1/8″ wide. Figure 11. Regarding the analytical-based methods, the main challenge remains the quantification and modeling of the uncertainties (due to simplified assumptions) that would be involved at each stage of the analysis. The use of a nonprobability distribution function in order to express the fragility curves may have implications in the risk assessment which requires its coupling with a hazard curve to produce the annual probability of reaching or exceeding a certain damage state. The example shown in Figure 8 illustrates the results of a seismic vulnerability and risk assessment that was carried out for the city of Guwahati, one of the most rapidly growing cities in India. The methods vary from simplified, non-numerically-based, to nonlinear static and dynamic numerically-based analyses of increasing complexity and accuracy. The selected scenario is a M 7.5 earthquake located at 40 km distance to the city center. The ATC-13 report developed the DPM for 78 structural typologies, out of which 40 belonged to buildings. Negligible to slight damage (no structural damage, slight non-structural damage): Hair-line cracks in very few walls. Extensive damage to beams; spalling of cover and shear cracking (<1/8″) for ductile columns; minor spalling in non-ductile columns; joints cracked < 1/8″ width. An application example is presented in Figure 11, showing the strength of the physical vulnerability representativeness on the risk assessment outcomes (i.e., damage and economic loss). Different elements can be distinguished: structural components and nonstructural components (see Figure 2). The purpose of seismic vulnerability assessment is to estimate the damage level (in a deterministic or a probabilistic way) induced to a given building typology due to a given level of ground shaking. 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