They can generally be categorized into three groups (see Figure 3): empirical methods, expert judgment-based methods, and analytical methods, although in practice many efforts combine two or more of these approaches hence leading to some sort of hybrid method (Calvi, Pinho, Magenes, Bommer, Restrepo-Vélez, & Crowley, 2006; Lang, 2013; Porter, Farokhnia, Cho, Rossetto, Ioannou, Grant, et al., 2012). Sharing as much possible as early as possible is key to that. Guide for conversion from damage to loss in indirect vulnerability curve. Calculation efforts and uncertainties associated to various methods of evaluating physical vulnerability. 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 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. Table 6 illustrates an example of a DPM-based physical vulnerability model. Such knowledge is needed to predict ground motions in future earthquakes so that earthquake-resistant structures can be designed. In order to reduce the earthquake risk, we need to reduce vulnerability. 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. Join GEM. Figure 10. Very heavy damage (heavy structural damage, very heavy non-structural damage): Serious failure of walls; partial structural failure of roofs and floors. In fact, this technique of analysis is considered to be quite impractical for everyday use. Vulnerability curves in terms of economic loss (i.e., cost of physical damage) are then obtained by converting the fragility curves through an appropriate damage-to-loss function. In addition, there is no warn-ing for earthquake occurrence and hence people cannot be evacuated from the area at risk. Vulnerability, Exposure, and Emergency Response and Recovery, respectively. On the other hand, the challenge with the implementation of nonlinear dynamic-based methods is that they involve intense calculations and require detailed mathematical models of multi degree of freedom (MDoF) systems. Bringing all functions together in a single dataset, with guidelines for applications, as well as present methods for creation of new functions where they are currently lacking, has not yet been done before on a global scale. This is the approach generally used in analytical-based vulnerability assessment. In order to be able to correlate the response spectrum with building capacity, it needs to be converted from the (conventional) Sa–T domain into the domain of the capacity curve, i.e., spectral acceleration–spectral displacement domain (Sa–Sd). Each expert was asked to fill in a comprehensive questionnaire by utilizing his/her best knowledge. Partial or total failure/cracking of infill panels and other secondary elements. 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). An example of this method is the Incremental Dynamic Analysis (IDA), which was initially developed by Shome and Cornell (1999) and then later improved by Vamvatsikos and Cornell (2002, 2005). This model, which was developed in the United States based on an empirical vulnerability analysis of damage data from the 1971 San Fernando earthquake, has nine damage states ranging from 0 for No damage to 8 for Collapse. The availability of these vulnerability models has encouraged risk analysts (e.g., academics, engineers, insurers) to use them for future applications, rather than to develop customized models (i.e., to conduct vulnerability analyses and measure physical vulnerability) that address the peculiar structural and nonstructural characteristics of the respective building stock. In addition to the aforementioned parameters, i.e., overall building height, level of code design, or period of construction (the age of a building is sometimes used as an indirect indicator of the seismic design level, especially in areas where seismic codes have been adopted; it can also indicate typical construction practices in a given region) can also have a strong impact on building vulnerability (FEMA-177, 1989). Since its introduction, this component has received much attention by research engineers and insurance analysts and has resulted in the development of a large number of methods, which can be divided into three main categories: empirical, analytical, and expert judgment/opinion. Within the RISK-UE framework, a research project consortium financed by the European Commission, a procedure was introduced which allowed the generation of DPMs considering the EMS-98 building vulnerability classes (Milutinovic & Trendafiloski, 2003). At this stage, one of the challenges that is often encountered is that some of these assumptions may highly affect the reliability and accuracy of the resulted physical vulnerability models in a negative way, hence introducing important uncertainties in estimating and predicting the inherent risk (i.e., estimated damage and losses). The analytical methods for measuring seismic physical vulnerability may also be called purely theoretical approaches, since, in contrast to the empirical or expert judgment-based methods, they are not based on observation, but rather on the theoretical simulation of physical damage under earthquake loading. The implementation of such detailed analysis requires specific knowledge (e.g., the definition of the performance criteria in terms of plastic rotation values for each structural and nonstructural component), time, and monetary resources. 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. The next step was generating physical vulnerability curves, that is, fragility curves for each building typology class, as shown in Figure 8. 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). One of the main challenges that are often faced in measuring seismic vulnerability is the quantification and modeling of the uncertainties (both aleatory and epistemic) that would be involved at each stage of the vulnerability model’s construction process. Discover GEM, the people and organisations driving the GEM Foundation, and learn how you can use the data, resources and tools as input to improved understanding, assessment and management of risk. The human vulnerability to earthquake-induced landslides is quantitatively estimated through constructing a systematic structure considering the uncertainties of the control factors and their inter-relationships. In order to analytically predict the structural damage that a building of a given capacity will produce under a given seismic impact, different methods are available in the literature covering different building typologies and locations worldwide (Meslem, D’Ayala, Ioannou, Rossetto, & Lang, 2014; D’Ayala, Meslem, Vamvatsikos, Porter, & Rossetto, 2015). The building stock in Guwahati is mainly dominated by buildings of nonductile (non-engineered and low-code engineered buildings) low-rise reinforced concrete frames and confined masonry, followed by buildings of ductile (modern engineered buildings) low-rise reinforced concrete frames with unreinforced masonry infill walls. In earthquake risk assessment, the term “physical vulnerability” is basically defined as a component that describes the susceptibility of a given building type or infrastructure to experience damage (and its associated economic loss) caused by a given level of ground shaking. Partial collapse of chimneys. There are many ways you or your organisation can get involved, from becoming a participant in the effort to sharing your expertise and data. 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. Building is considered as significantly damaged. 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. GEM Foundation | Via Ferrata 1, 27100 Pavia, Italy | +39 0382 5169865 | email@example.com A number of problems can be associated with the existing empirical methods and approaches for vulnerability assessment. 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. Other challenges that are commonly encountered when developing physical vulnerability models are the paucity of exposure information and the lack of knowledge due to either technical or nontechnical problems, such as inventory data that would allow for accurate building stock modeling, or economic data that would allow for a better conversion from damage to monetary losses. 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. It yields collapse multipliers which identify the occurrence of possible different mechanisms for a given masonry construction typology, given certain structural characteristics. The guide includes a full review of damage scales, a damage scale rating system, review and summary of existing damage factors. Figure 9. One approach is proposed as the default. This agrees with the range of almost all ground-motion intensity measures. Basically, the choice between the different existing methods for physical vulnerability quantification must consider a number of aspects. Within this perspective, and for a given building typology or portfolio, physical vulnerability defines the probability of suffering a certain level of physical damage. Figure 7. The uncertainty in the structural characteristics-related parameters is introduced by geometrical, mechanical, structural, and modeling parameters. Extensive spalling in columns (limited shortening) and beams; severe joint damage; some reinforcing buckled. Extensive crushing and spalling of veneers at corners of openings. 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”. In addition, 30% of the world’s earthquakes occur close to Japan, so Earthquakes are common, but earthquakes of this size are rare. Hence, it represents the mean damage an individual building of this typology will experience. Figure 2. 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. Social vulnerability - Example: Educated and well-informed are more likely to survive when disaster strikes. Building is considered as heavily damaged. 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 1. National Technical University of Athens (NTUA): Systematic method for determining mean empirical curves; quantifying uncertainty, Incorporating uncertainty in intensity measure(s) into vulnerability, Treat brittle & ductile systems; flexible structural analysis procedures, PEER/ATC 58 “light” method with 3 levels of uncertainty propagation, Rates for 30 building types from 25 earthquakes since 1972, Kernel smoothing for uncertain intensity measure and loss, 2 levels of detail in data, 3 levels of complexity for the curve derivation, Incorporate uncertainty in the shaking intensity, the spatial correlation of data, Include procedures for the combination of different databases, Propose a range of parametric and non-parametric regression techniques, which can be selected according to the nature of the damage or loss data, Include diagnostic procedures that can identify the optimum regression model for these data, Example applications include Irpinia 1980 (by UCLa), two Australian Earthquakes (by Geoscience Australia) and the 2011 Christchurch Earthquake NZ (nonstructural vulnerability by University of Adelaide), 8 categories, all acceleration-sensitive (analyst can add more), Define asset class with 1 or 3 index buildings, Quantities of 8 categories (analyst can add categories), Lognormal fragility functions, default params provided, Integrate damage-state pmf and unit costs for story-level vulnerability, Structural analysis per D’Ayala or defaults a la FEMA 695, ASCE 7, Mean vulnerability function and uncertainty, Hundreds of categories, drift or acceleration-sensitive (analyst can add more), Define asset class with 1, 3, or 7 index buildings, Vulnerability function factored up later to account for partial inventory, Lognormal fragility functions, default parameters provided, Fragility functions and repair costs per ATC-58 with local adjustment, Integrate damage-state PMF and unit costs for story-level vulnerability, Combine story functions using structural response per defaults using FEMA 695, ASCE 7 and generalized mode shape; or by 2D or 3D, 3 options for uncertainty propagation up to moment matching and MCS, Empirical fatality data related to predominant structural class, population, occurrence time, and well-documented building collapses from 25* significant earthquakes since 1972 (*account for > 70% of earthquake deaths during this period), Detailed investigations relating collapse and volume reduction yielded important clues related to the lethality of different failure mechanisms in modern and older construction types, Presents a set of judgment-based rates for 30 global building types; which represents a significant advancement, Updated empirical-national earthquake fatality models for the globe, based on USGS/PAGER work, First, globally applicable, direct shaking-induced economic loss estimation model, with the help of Munich Re’s NatCAT economic loss database, Improvement of existing models in terms of incorporating recent well-studied earthquakes, modelling hazard uncertainties through calibration, and improving ad-hoc regionalization schemes. 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