Physical vulnerability of buildings and infrastructure is a function of the intensity of the landslide event and the resistance levels of the exposed elements.8910111213,,,,, Recent seismic events show that urban areas are increasingly vulnerable to seismic damage, which leads to unprecedented levels of risk. 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). Join GEM. Different concepts for damage classification exist, which are even based on different numbers of damage severity levels. Houses built with light materials may not be a problem during an earthquake, but may be totally damaged by a super typhoon. 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. An earthquake is known to be a sudden release of energy into the earth’s crust that creates seismic waves. Fall of fairly large pieces of plaster. Empirical methods that generate physical damage-to-ground motion intensity relationships incorporate assumptions and approaches that are based on field observations and statistical analysis of building damage data (i.e., the seismic performance of the building) from past earthquake events. 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. Further guidance is provided on different options for modelling of structures: The Collapse Fragility Modeling Process consists of 3 phases: To develop the process, USGS and EERI successfully conducted a structured expert elicitation workshop in September 2012, featuring 13 leading experts from around the globe that offered judgments on collapse fragility of six masonry and six reinforced concrete building types. A number of problems can be associated with the existing empirical methods and approaches for vulnerability assessment. 2. Since the late 1990s, a blossoming of methodologies and procedures can be observed, which range from empirical to basic and more advanced analytical, implemented for modelling and measuring physical vulnerability. Moreover, the extensive seismic risk assessment and modeling studies that have been implemented in many different parts of the world, covering a wide range of building typologies and portfolios, have resulted in a wealth of seismic vulnerability models available in literature that can be used for future applications, such as fragility functions for North American building typology classes provided in HAZUS-MH (FEMA-NIBS, 2003), the European vulnerability database developed under SYNER-G (2011a, 2011b), or the worldwide vulnerability database compiled within the Global Earthquake Model (GEM) framework (Rossetto, Ioannou, Grant, & Maqsood, 2014; D’Ayala, & Meslem, 2013). Building damage distribution and the conversion in terms of economic loss for Guwahati city, for the 1897 Shillong earthquake scenario, India. Vulnerability, Exposure, and Emergency Response and Recovery, respectively. 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. Comparison between the recorded fatality rate and predicted fatality is conducted to verify the proposed model. Table 6 illustrates an example of a DPM-based physical vulnerability model. Extensive spalling in columns (possible shortening) and beams; severe joint damage; some reinforcing buckled.     At least 15,899 people died, and another 2,500 went missing. The usefulness of the EMS scale in particular relies in its feasibility to assess large building stocks in extended urban areas. 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. Negligible to slight damage (no structural damage, slight non-structural damage): Hair-line cracks in very few walls. However, reducing or better quantifying the uncertainty associated with one of the parameters when computing physical vulnerability does not necessarily mean improving the overall reliability and robustness of the results. 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. Nonlinear Dynamic Procedures (NDP) are in general limited for seismic vulnerability at an individual building level. The potential collapse mechanism and the corresponding capacity are determined by the geometry and boundary conditions, usually based on visual observations. Typically, differences in construction techniques and detailing between different countries are significant, even when buildings are nominally designed according to similar code provisions. It yields collapse multipliers which identify the occurrence of possible different mechanisms for a given masonry construction typology, given certain structural characteristics. Substantial to heavy damage (moderate structural damage, heavy non-structural damage): Large and extensive cracks in most walls. 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). To come up with appropriate predictions on expected damage and losses of an individual asset (e.g., a building) or a class of assets (e.g., a building typology class, a group of buildings), reliable physical vulnerability models have to be generated considering all these peculiarities and the associated intrinsic uncertainties at each stage of the development process. For a building experiencing different damage states dsi, the conditional function expresses the probability of a damage state dsi sustained by the building, being reached or exceeded given a certain level of ground-motion intensity measure IM. Table 3 lists the most widely-used classification schemes for either global or regional construction typologies along with the different classification criteria. With respect to building damage classifications that rely on a quantitative description of the damage effects to a building, their main difference is the parameter used to differentiate between the damage thresholds, in addition to the number of damage states that are considered. 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. These damage states were correlated with five intensities from V to IX. Some 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). The concept of vulnerability encompasses a variety of definitions. Each of these elements could be made more precise with additional efforts and resources to improve the quality and quantity of data input. Modelling of exposure and physical vulnerability in the most earthquake prone countries is the main goal in the first stage of the project. A large number of the existing empirical vulnerability models that were developed mainly use macroseismic intensities (e.g., MMI, MSK, EMS–98, PSI) for characterizing and representing the earthquake shaking. Hence, it represents the mean damage an individual building of this typology will experience. Extensive cracking and crushing; portions of face course shed. A more realistic 3D model was only used in few studies (e.g., Rossetto & Elnashai, 2003). Some people and places are more vulnerable to certain hazards than other people and places. 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. This means that (a) generally limited data is available for lower shaking intensities (i.e., intensity I < VI) where no visible damage is produced, and (b) data for a certain test bed is typically restricted to only one or two intensity grades. Extensive cracking and some crushing but wall remains in place; no falling units. The normal cumulative distribution function and the logistic distribution function have been used in cases where the intensity measure can take negative values. On March 11, 2011, a 9.1 magnitude earthquake occurred 231 miles northeast of Tokyo. This information was then used for developing mitigation plans and prevention actions (i.e., retrofitting solutions to improve the seismic response, etc.). Extensive crushing and shattering; some walls dislodge. Figure 5. 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. The vulnerability and risk assessment started by developing a customized building classification scheme for the existing building stock in the city. The lognormal cumulative distribution function is most commonly used as the regression model. (London: Routledge, 2004). With respect to generating continuous physical damage-to-ground motion intensity relationships (i.e., fragility curves) using analytical methods, it is commonly assumed that these relationships take the form of lognormal cumulative distribution functions having a median value and logarithmic standard deviation, or dispersion. Charles Perrow, The Next Catastrophe (Princeton, NJ: Princeton University Press, 2007). These activities have resulted in a wealth of seismic vulnerability models covering a wide range of building typologies and portfolios. The methods vary from simplified, non-numerically-based, to nonlinear static and dynamic numerically-based analyses of increasing complexity and accuracy. The present article provides a comprehensive summary about the term physical vulnerability and its usage within the field of earthquake engineering and risk reduction. In addition, the results obtained for the target area cannot be extended to other towns and cities. 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). The new development of high-performance computer systems and advanced programs (e.g., with nonlinear analysis capabilities), and the increasing volume of research, together with the increasing availability of earthquake damage and exposure data, is resulting in the development of more innovative procedures/approaches and important improvements in the reliability and robustness of physical vulnerability modeling. This, in turn, has pushed governments from different earthquake-prone countries to implement many research programs aimed at developing prevention and mitigation actions, or in refining code provisions and guidelines. Defining the building structural system for vulnerability measurement. They manifest themselves at the earth’s surface by shaking and sometimes displacement of the ground and can occur naturally or can be caused by humans. Chimneys fracture at the roof line; failure of individual non-structural elements (partitions, gable walls). In case of Nonlinear Static Simplified Mechanism-based Procedures, they have the advantage of analyzing a large number of buildings in a relatively short period of time. Literally, the term fragility defines the conditional probability of the seismic demand placed upon the structure exceeding its capacity for a given level of ground-motion intensity measure (IM). 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. Cities are complex. The discrete damage classes are defined separately for both structural and nonstructural components of a building. 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. In order to reduce the earthquake risk, we need to reduce vulnerability. 5000 HAZUS-based vulnerability functions for 128 building types and 33 occupancy classes. This displacement stands for the mean displacement a building typology will reach under the respective seismic demand. 3.2 Physical vulnerability in existing built whose are the responsible for the development of ef- environment fective earthquake protection measures should establish The most part of the city is characterized by a high den- a strong coordination between them at the very begin- sity of population living in a very compact land area ning. Physical vulnerability models selection framework considering size and regional factor and their uncertainties. 13 main attributes and numerous attribute values (373 in total), Brzev, Scawthorn, Charleson, Allen, Greene, Jaiswal, & Silva (2013), Lungu, Aldea, Arion, Vacareanu, Petrescu, & Cornea (2001); Milutinovic & Trendafiloski (2003), Lang, Erduran, Kumar, Yasunov, & Tailiakova (2012), Lang, Molina-Palacios, Lindholm, & Balan (2012). Accordingly, data input can be provided either qualitatively or quantitatively. Printed from Oxford Research Encyclopedias, Natural Hazard Science. Methods of this category are specifically suitable for poor-quality non-engineered construction whose resistance is difficult to calculate using analytical or numerical methods. 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. Since the late 1990s, many methods have been introduced for quantifying physical vulnerability. In this type of procedure, building vulnerability is expressed in terms of a capacity curve that represents the nonlinear behavior of the structure under lateral displacement. Vulnerability These uncertainties can derive from the definition of the structural capacity-related characteristics of the building; the uncertainty in estimating the ground-motion intensity for a given event; the uncertainty in estimating physical damage given the ground-motion intensity for a given event; and finally the uncertainty in estimating the economic loss given damage to the building. 2. For instance, HAZUS-MH (FEMA-NIBS, 2003) has provided default values for the Damage Factor, which include material and labor costs related to 33 occupancy classifications in the United States. In other cases (e.g., D’Ayala, 2005), variability in structural and geometric characteristics is accounted for by a survey of a large number of real buildings (i.e., building-to-building variability) and determining a median and standard deviation for the sample, after calculating the capacity and damage threshold for each element in the sample. of earthquake risk assessment, where vulnerability is com-monly modelled. Principal steps of the capacity spectrum-based procedures for the calculation of seismic performance (modified from Lang, 2013). 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). Different elements can be distinguished: structural components and nonstructural components (see Figure 2). Later, when the reinsurance industry also began paying greater attention to this topic in order to improve catastrophe risk models, seismic physical vulnerability became a term used to express not only the seismic performance of a structure but also to estimate the economic consequences of the physical damage in terms of monetary losses (FEMA, 2008). The term physical vulnerability, which has been used in many disciplines and different contexts, defines the probability (or the potential) of a given physical component or element to be affected or damaged under a certain external excitation, e.g., a natural hazard such as an earthquake. Partial or total failure/cracking of infill panels and other secondary elements. 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. However, selecting vulnerability models that had been originally developed for similar building typologies in other parts of the world can be a quite challenging process in order to ensure a reliable earthquake loss assessment and modeling, considering the fact that differences in both construction techniques and structural detailing between different countries are typically significant, even when buildings are nominally designed according to similar code provisions. Cooke’s method was applied to first solicit and then combine multiple judgments to produce seismic fragility models. The same may apply to the insurance and reinsurance industry in developing catastrophe (CAT) models. Partial or total failure/cracking of columns and beams. Vulnerability analysis is generally conducted in three main steps: (1) definition of the building’s structural system; (2) estimation of the physical damage given the ground-motion intensity; and (3) evaluation of the overall seismic performance, i.e., the level of vulnerability, given the ground-motion intensity. Provide feedback on the latest draft of the analytical content guidelines, other guidelines and reports, but also on other important work we share with you through our group on GEM Nexus. There is a clear understanding and agreement among the engineering and scientific communities that one should move forward using more advanced modeling strategies that are able to relax the often unrealistic assumptions and forget about the simplified assumptions used so far. In addition, there is no warn-ing for earthquake occurrence and hence people cannot be evacuated from the area at risk. In general, this approach is mostly suitable for the vulnerability analysis of single buildings, and where the majority of the economic losses are associated with nonstructural components (e.g., in case of hospitals or other highly sophisticated buildings). In addition, 30% of the world’s earthquakes occur close to Japan, so Earthquakes are common, but earthquakes of this size are rare. One of these methods was developed by D’Ayala and Speranza (2003), called Failure Mechanism Identification and Vulnerability Evaluation (FaMIVE) procedure. Structural components are the main elements that contribute to the response behavior of the building, and the consequences of the response in terms of the monetary losses are connected to the repair of structural damage or the replacement of the building. The vulnerability classes range from A to F, from the most vulnerable to the least vulnerable typologies, where the first three classes (A to C) cover adobe and stone houses, brick buildings, and reinforced-concrete constructions without any ERD, while vulnerability classes D to F address reinforced and confined masonry constructions, concrete buildings with a certain level of ERD, and steel and timber buildings. 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 … Peak ground acceleration (PGA), which is a physical parameter, was also used in empirical studies; however, this parameter in particular shows almost no correlation to structural earthquake damage (Crowley, Pinho, & Bommer, 2004). Represents a significant hazard to life safety resulting from failure of non-structural components. Although earthquakes cause death and destruction through such … 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. 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). Figure 2. Existing approaches for physical damage-to-ground motion intensity correlations. (a) Photograph showing experts responding to a target question; (b) Collapse fragility estimates obtained using expert elicitation process (adapted from Jaiswal, Wald, Perkins, Aspinall, & Kiremidjian, 2013). 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