Bayesian Network Methods for Modeling and Reliability Assessment of Infrastructure Systems

A probabilistic framework for Bayesian inference combined with extreme values of Gaussian Processes is proposed to assess the maximum of the response of an uncertain structure instrumented with sensors and subject to a stochastic load. The framework is applied to the analysis of the inter-story drift of a multi-story shear-type building under seismic hazard using measurements collected by accelerometers. A cascade of two dynamic systems is proposed to model the stochastic ground motion and the response of the structure. We present an approximate analytical solution to estimate the distribution of the maximum response, and verify the accuracy and limitations of this solution against simulation results. Finally, robustness of the proposed framework to system uncertainties, including uncertainties in the structural characteristics, ground characteristics, and input motion parameters, is investigated.

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“…We consider variability in all three parameters. More detailed results considering variability in subsets of the parameters are reported in [33].…”

Section: Robustness To Uncertainty In Structural Parametersmentioning

confidence: 99%

Probabilistic framework for assessing maximum structural response based on sensor measurements

2016

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“…Bayesian models have previously been used in various civil and structural engineering applications. This includes work in structural health monitoring to probabilistically measure damage such as Vanik and Beck (1997) and Vanik et al (2000); work in structural model updating such as Katafygiotis and Yuen (2001), Yuen and Katafygiotis (2002), and Au et al (2013); and work in system-level reliability assessment such as Tien (2014) and Zhang and Au (2015). In contrast to these previous studies, the goal of this study is to estimate the nonlinear structural response, including displacement-based interstory drifts, based solely on sensor measurements without knowledge of the excitation time history.…”

Section: Background and Related Workmentioning

confidence: 99%

Framework for Probabilistic Assessment of Maximum Nonlinear Structural Response Based on Sensor Measurements: Discretization and Estimation

Saini

Tien

2017

J. Eng. Mech.

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A probabilistic framework to draw real-time inferences on the maximum response of an uncertain nonlinear structural system under stochastic excitation based on sensor measurements is proposed. The primary contributions are twofold: first, an exact discretization solution is derived for the system evolution equation for the nonlinear case. This is validated against a Taylor expansion discretization solution. Second, a methodology for robust Bayesian estimation of the time-evolving system state is proposed. The system is instrumented by sensors placed on the structure with inferences drawn using Kalman-based approaches. The sensor observations are used in real time to estimate system state without any knowledge of the time history of the input motion. The distribution of the maximum response is assessed. The proposed methodology is applied to a 10-story shear-type sample structure under earthquake loading. The interstory drift is analyzed with measured data collected through accelerometers placed on the building. A simulation approach is used to demonstrate the ability of the proposed methodology to accurately estimate nonlinear structural response based on sensor measurements. In addition, the method is shown to be robust to varying system characteristics. This includes uncertainties in the structural, ground, and input motion parameters, as well as varying measurement characteristics.

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“…Previously, BNs have been used to model smaller systems of five to ten components (e.g., Bobbio et al 2001;Kim 2011). Algorithms have been developed to use BNs to model and assess the reliability of much larger systems (Tien andDer Kiureghian 2015, 2016) and increase computational efficiency in conducting inferences for critical infrastructure systems (Tien and Der Kiureghian 2017). We used both a minimum link set (MLS) formulation and supercomponent identification to decrease the dimensionality of the network and make it computationally tractable for modeling systems of hundreds of nodes.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Framework for Probabilistic Vulnerability Analysis of Interdependent Infrastructure Systems

Applegate

Tien

2019

J. Comput. Civ. Eng.

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Critical infrastructure systems are deteriorating and experiencing increased cascading failures. In this paper, we propose a new probabilistic framework for modeling interdependent infrastructure networks. As part of this framework, we present our modeling approach and the accompanying sets of algorithms that enable the computationally efficient probabilistic modeling of large infrastructure systems considering interdependencies between networks. The proposed method creates a representative Bayesian network (BN) of the system, which provides exact inferences over the network compared to simulation-based approximations. With the traditional computational limitations of BNs to tens of parent component nodes per child system node, our generalized framework enables computationally efficient BN modeling of systems of, for example, hundreds of component nodes, including component-level performance and interdependent connections across networks. Our approach requires only simple inputs based on the basic component characteristics of location, type, connectivity, and initial failure probabilities. We modeled three types of interdependencies across the system-service provision, geographic, and access for repair. We combined a minimum link set (MLS) formulation with the idea of supercomponents in order to reduce network complexity without any approximating assumptions. We present algorithms to efficiently identify MLSs and supercomponents, as well as to identify and remove any cyclic dependencies that arise across the network. Once the BN was constructed, we were able to perform exact inference analyses over a range of component state and hazard event scenarios in order to identify vulnerabilities across the network. The main novelty of the paper is to enable the probabilistic assessment of large, complex, interdependent infrastructure systems. We were able to consider performance from the component level and model hundreds of component nodes in a computationally efficient manner without approximating assumptions. We accounted for interdependencies between systems with exact inference results. The model can be used to investigate the potential for cascading failures and to prioritize critical components for repair, replacement, or reinforcement. We applied the proposed methodology and algorithms to the water distribution network in Atlanta, Georgia, and its dependencies with the power system. We validated the model using the results from a recent interdependent outage event.

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Bayesian Network Methods for Modeling and Reliability Assessment of Infrastructure Systems

Cited by 13 publications

References 39 publications

Probabilistic framework for assessing maximum structural response based on sensor measurements

Probabilistic framework for assessing maximum structural response based on sensor measurements

Framework for Probabilistic Assessment of Maximum Nonlinear Structural Response Based on Sensor Measurements: Discretization and Estimation

Framework for Probabilistic Vulnerability Analysis of Interdependent Infrastructure Systems

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