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Abdelghani Meslem and Dominik H. Lang

In the fields of earthquake engineering and seismic risk reduction the term “physical vulnerability” defines the component that translates the relationship between seismic shaking intensity, dynamic structural uake damage and loss assessment discipline in the early 1980s, which aimed at predicting the consequences of earthquake shaking for an individual building or a portfolio of buildings. In general, physical vulnerability has become one of the main key components used as model input data by agencies when developinresponse (physical damage), and cost of repair for a particular class of buildings or infrastructure facilities. The concept of physical vulnerability started with the development of the earthqg prevention and mitigation actions, code provisions, and guidelines. The same may apply to insurance and reinsurance industry in developing catastrophe models (also known as CAT models). 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. These methods use approaches that differ in terms of level of complexity, calculation efforts (in evaluating the seismic demand-to-structural response and damage analysis) and modelling assumptions adopted in the development process. 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). 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. Hence, these physical vulnerability models will carry different types of intrinsic uncertainties of both aleatory and epistemic character. 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.