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This article presents an integrated approach for the probabilistic systemic risk analysis of a road network considering spatial seismic hazard with correlation of ground motion intensities, vulnerability of the network components, and the effect of interactions within the network, as well as, between roadway components and built environment to the network functionality. The system performance is evaluated at the system level through a global connectivity performance indicator, which depends on both physical damages to its components and induced functionality losses due to interactions with other systems. An object-oriented modeling paradigm is used, where the complex problem of several interacting systems is decomposed in a number of interacting objects, accounting for intra-and interdependencies between and within systems. Each system is specified with its components, solving algorithms, performance indicators and interactions with other systems. The proposed approach is implemented for the analysis of the road network in the city of Thessaloniki (Greece) to demonstrate its applicability. In particular, the risk for the road network in the area is calculated, specifically focusing on the short-term impact of seismic events (just after the earthquake). The potential of road blockages due to collapses of adjacent buildings and overpass bridges is analyzed, trying to individuate possible criticalities related to specific components/subsystems. The application can be extended based on the proposed approach, to account for other interactions such as failure of pipelines beneath the road segments, collapse of adjacent electric poles, or malfunction of lighting and signaling systems due to damage in the electric power network. C 2015 Computer-Aided Civil and Infrastructure Engineering.

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This article presents a method for the development of vector-valued fragility functions, which are a function of more than one intensity measure (IM, also known as ground-motion parameters) for use within seismic risk evaluation of buildings. As an example, a simple unreinforced masonry structure is modelled using state-of-the-art software and hundreds of nonlinear time-history analyses are conducted to compute the response of this structure to earthquake loading. Dozens of different IMs (e.g. peak ground acceleration and velocity, response spectral accelerations at various periods, Arias intensity and various duration and number of cycle measures) are considered to characterize the earthquake shaking. It is demonstrated through various statistical techniques (including Receiver Operating Characteristic analysis) that the use of more than one IM leads to a better prediction of the damage state of the building than just a single IM, which is the current practice. In addition, it is shown that the assumption of the lognormal distribution for the derivation of fragility functions leads to more robust functions than logistic, log-logistic or kernel regression. Finally, actual fragility surfaces using two pairs of IMs (one pair are uncorrelated while the other are correlated) are derived and compared to scalar-based fragility curves using only a single IM and a significant reduction in the uncertainty of the predicted damage level is observed. This type of fragility surface would be a key component of future risk evaluations that take account of recent developments in seismic hazard assessment, such as vector-valued probabilistic seismic hazard assessments.

Metrics that describe direct social losses, such as number of casualties and fatalities, or the number of displaced people, which pose a demand for emergency shelter needs, or on the health-care system and other critical facilities, are key inputs for emergency response planning and preparedness. This paper presents a model to evaluate such performance indicators. The model integrates multiple infrastructural systems within a consistent computational framework where the consequences of their physical damage and interactions are used in the quantitative assessment of social losses. In particular, the interaction between the physical damage state of buildings and the combined residual service level in the utility networks is used to assess the habitability of buildings from which the number of displaced persons can be computed. The model is integrated within a larger analysis framework for the seismic vulnerability assessment of interconnected infrastructural systems, accounting for relevant uncertainties and interdependencies. A simple application illustrates the model capabilities. Copyright (C) 2012 John Wiley & Sons, Ltd

Fragility curves are generally developed using a single parameter to relate the level of shaking to the expected structural damage. The main goal of this work is to use several parameters to characterize the earthquake ground motion. The fragility curves will, therefore, become surfaces when the ground motion is represented by two parameters. To this end, the roles of various strong‐motion parameters on the induced damage in the structure are compared through nonlinear time‐history numerical calculations. A robust structural model that can be used to perform numerous nonlinear dynamic calculations, with an acceptable cost, is adopted. The developed model is based on the use of structural elements with concentrated nonlinear damage mechanics and plasticity‐type behavior. The relations between numerous ground‐motion parameters, characterizing different aspects of the shaking, and the computed damage are analyzed and discussed. Natural and synthetic accelerograms were chosen/computed based on a consideration of the magnitude‐distance ranges of design earthquakes. A complete methodology for building fragility surfaces based on the damage calculation through nonlinear numerical analysis of multi‐degree‐of‐freedom systems is proposed. The fragility surfaces are built to represent the probability that a given damage level is reached (or exceeded) for any given level of ground motion characterized by the two chosen parameters. The results show that an increase from one to two ground‐motion parameters leads to a significant reduction in the scatter in the fragility analysis and allows the uncertainties related to the effect of the second ground‐motion parameter to be accounted for within risk assessments. Copyright © 2009 John Wiley & Sons, Ltd.

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Nonlinear dynamic analysis is often used to develop fragility curves within the framework of seismic risk assessment and performance-based earthquake engineering. In the present article, fragility curves are derived from randomly generated clouds of structural response results by using least squares and sum-of-squares regression, and maximum likelihood estimation. Different statistical measures are used to estimate the quality of fragility functions derived by considering varying numbers of ground motions. Graphs are proposed that can be used as guidance regarding the number of calculations required for these three approaches. The effectiveness of the results is demonstrated by their application to a structural model. The results show that the least-squares method for deriving fragility functions converges much faster than the maximum likelihood and sumof-squares approaches. With the least-squares approach, a few dozen records might be sufficient to obtain satisfactory estimates, whereas using the maximum likelihood approach may require several times more calculations to attain the same accuracy.