Seismic vulnerability analysis of a complex interconnected civil infrastructure

Earthq Engng Struct Dyn

Gehl

et al. 2012

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

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative assessment of social losses based on physical damage and interaction with infrastructural systems

Earthq Engng Struct Dyn

Gehl

et al. 2012

“…As a final remark, four of these models, in particular M1 and M3 to M5, have been developed within a more comprehensive model for physical simulation of multiple interacting infrastructural systems (Cavalieri et al., ; Franchin and Cavalieri, ,), according to the object‐oriented (OO) modeling paradigm (Booch et al., ). The latter has been widely used in recent years in a number of applications, for example, traffic analysis in transportation networks (Jiang and Adeli, ) and stress analysis of composite laminates (Adeli and Yu, ).…”

Section: Models and Metrics For Power Networkmentioning

confidence: 99%

“…The next modeling step consists of complementing the network graph with system‐specific information and physical flow equations. For steady‐state, these can be solved in a simplified linearized direct current (DC) form or in their complete nonlinear AC form, as in the model employed in this article (Franchin and Cavalieri, ,; Vanzi, ). An exhaustive contingency analysis would also include transient analysis (Swarup, ).…”

Section: Models and Metrics For Power Networkmentioning

confidence: 99%

Models for Seismic Vulnerability Analysis of Power Networks: Comparative Assessment

Computer aided Civil Eng

Cortés

et al. 2014

Electric power networks are spatially distributed systems, subject to different magnitude and recurrence of earthquakes, that play a fundamental role in the well-being and safety of communities. Therefore, identification of critical components is of paramount importance in retrofit prioritization. This article presents a comparison of five seismic performance assessment models (M1 to M5) of increasing complexity. The first two models (M1 and M2) approach the problem from a connectivity perspective, whereas the last three (M3 to M5) consider also power flow analysis. To illustrate the utility of the five models, the well-known IEEE-118 test case, assumed to be located in the central United States, is considered. Performances of the five models are compared using both system-level and component-level measures. Spearman rank correlation ρ is computed between results of each model. Highest ρ values, at both system- and component-level, are obtained, as expected, between M1 and M2, and within models M3 to M5. The ρ values between component-level measures are relatively high across all models, indicating that simpler ones (M1 and M2) are appropriate for vulnerability assessment and retrofit prioritization. The complex flow-based models (M3 to M5) are suitable if actual performance of the systems is desired, as it is the case when the power network is considered within a larger set of interconnected infrastructural systems. © 2014 Computer-Aided Civil and Infrastructure Engineering

Bayesian Networks and Infrastructure Systems: Computational and Methodological Challenges

Springer Series in Reliability Engineering

Gehl

et al. 2017

This chapter investigates the applicability of Bayesian Network methods to the seismic assessment of large and complex infrastructure systems. While very prom-ising in theory, Bayesian Networks tend to quickly show limitations as soon as the studied systems exceed several dozens of components. Therefore a benchmark study is conducted on small-size virtual systems in order to compare the computa-tional performance of the exact inference of various Bayesian Network formula-tions, such as the ones based on Minimum Link Sets. It appears that all formula-tions present some computational bottlenecks, which are either due to the size of Conditional Probability Tables, to the size of clique potentials in the junction-tree algorithm or to the recursive algorithm for the identification of Minimum Link Sets. Moreover, these formulations are limited to connectivity problems, whereas the accurate assessment of infrastructure systems usually requires the use of flow-based performance indicators. To this end, the second part of the chapter introduc-es a hybrid approach that presents the merit of accessing any type of system per-formance indicator: it uses simulation-based results and generates the correspond-ing Bayesian Network by counting the outcomes given the various combinations of events that have been sampled in the simulation. The issue of the system size is also addressed by a thrifty-naïve formulation, which limits the number of the components that are involved in the system performance prediction, by applying a cut-off threshold to the correlation coefficients between the components and sys-tem states. A higher resolution of this thrifty-naïve formulation is also obtained by considering local performance indicators, such as the flow at each sink. This ap-proach is successfully applied to a realistic water-supply network of 49 nodes and 71 pipes. Finally the potential of this coupled simulation-Bayesian approach as a decision support system is demonstrated, through probability updating given the observation of local evidences after an event has occurred