Browsing by Author "Rokneddin, Keivan"

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    RELIABILITY AND RISK ASSESSMENT OF NETWORKED URBAN INFRASTRUCTURE SYSTEMS UNDER NATURAL HAZARDS
    (2013-09-16) Rokneddin, Keivan; Dueñas-Osorio, Leonardo; Padgett, Jamie E.; Rojo, Javier; Wickham, Hadley
    Modern societies increasingly depend on the reliable functioning of urban infrastructure systems in the aftermath of natural disasters such as hurricane and earthquake events. Apart from a sizable capital for maintenance and expansion, the reliable performance of infrastructure systems under extreme hazards also requires strategic planning and effective resource assignment. Hence, efficient system reliability and risk assessment methods are needed to provide insights to system stakeholders to understand infrastructure performance under different hazard scenarios and accordingly make informed decisions in response to them. Moreover, efficient assignment of limited financial and human resources for maintenance and retrofit actions requires new methods to identify critical system components under extreme events. Infrastructure systems such as highway bridge networks are spatially distributed systems with many linked components. Therefore, network models describing them as mathematical graphs with nodes and links naturally apply to study their performance. Owing to their complex topology, general system reliability methods are ineffective to evaluate the reliability of large infrastructure systems. This research develops computationally efficient methods such as a modified Markov Chain Monte Carlo simulations algorithm for network reliability, and proposes a network reliability framework (BRAN: Bridge Reliability Assessment in Networks) that is applicable to large and complex highway bridge systems. Since the response of system components to hazard scenario events are often correlated, the BRAN framework enables accounting for correlated component failure probabilities stemming from different correlation sources. Failure correlations from non-hazard sources are particularly emphasized, as they potentially have a significant impact on network reliability estimates, and yet they have often been ignored or only partially considered in the literature of infrastructure system reliability. The developed network reliability framework is also used for probabilistic risk assessment, where network reliability is assigned as the network performance metric. Risk analysis studies may require prohibitively large number of simulations for large and complex infrastructure systems, as they involve evaluating the network reliability for multiple hazard scenarios. This thesis addresses this challenge by developing network surrogate models by statistical learning tools such as random forests. The surrogate models can replace network reliability simulations in a risk analysis framework, and significantly reduce computation times. Therefore, the proposed approach provides an alternative to the established methods to enhance the computational efficiency of risk assessments, by developing a surrogate model of the complex system at hand rather than reducing the number of analyzed hazard scenarios by either hazard consistent scenario generation or importance sampling. Nevertheless, the application of surrogate models can be combined with scenario reduction methods to improve even further the analysis efficiency. To address the problem of prioritizing system components for maintenance and retrofit actions, two advanced metrics are developed in this research to rank the criticality of system components. Both developed metrics combine system component fragilities with the topological characteristics of the network, and provide rankings which are either conditioned on specific hazard scenarios or probabilistic, based on the preference of infrastructure system stakeholders. Nevertheless, they both offer enhanced efficiency and practical applicability compared to the existing methods. The developed frameworks for network reliability evaluation, risk assessment, and component prioritization are intended to address important gaps in the state-of-the-art management and planning for infrastructure systems under natural hazards. Their application can enhance public safety by informing the decision making process for expansion, maintenance, and retrofit actions for infrastructure systems.
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    Seismic Reliability Assessment of Aging Highway Bridge Networks with Field Instrumentation Data and Correlated Failures. I: Methodology
    (Earthquake Engineering Research Institute, 2013) Ghosh, Jayadipta; Rokneddin, Keivan; Padgett, Jamie E.; Dueñas-Osorio, Leonardo
    The state-of-the-practice in seismic network reliability assessment of highway bridges often ignores bridge failure correlations imposed by factors such as the network topology, construction methods, and present-day condition of bridges, amongst others. Additionally, aging bridge seismic fragilities are typically determined using historical estimates of deterioration parameters. This research presents a methodology to estimate bridge fragilities using spatially interpolated and updated deterioration parameters from limited instrumented bridges in the network, while incorporating the impacts of overlooked correlation factors in bridge fragility estimates. Simulated samples of correlated bridge failures are used in an enhanced Monte Carlo method to assess bridge network reliability, and the impact of different correlation structures on the network reliability is discussed. The presented methodology aims to provide more realistic estimates of seismic reliability of aging transportation networks and potentially helps network stakeholders to more accurately identify critical bridges for maintenance and retrofit prioritization.
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    Seismic Reliability Assessment of Aging Highway Bridge Networks with Field Instrumentation Data and Correlated Failures. II: Application
    (Earthquake Engineering Research Institute, 2013) Rokneddin, Keivan; Ghosh, Jayadipta; Dueñas-Osorio, Leonardo; Padgett, Jamie E.
    The Bridge Reliability in Networks (BRAN) methodology introduced in the companion paper is applied to evaluate the reliability of part of the highway bridge network in South Carolina, USA, under a selected seismic scenario. The case study demonstrates Bayesian updating of deterioration parameters across bridges after spatial interpolation of data acquired from limited instrumented bridges. The updated deterioration parameters inform aging bridge seismic fragility curves through multidimensional integration of parameterized fragility models, which are utilized to derive bridge failure probabilities. The paper establishes the correlation structure among bridge failures from three information sources to generate realizations of bridge failures for network level reliability assessment by Monte Carlo analysis. Positive correlations improve the reliability of the case study network, also predicted from the network topology. The benefits of the BRAN methodology are highlighted in its applicability to large networks while addressing some of the existing gaps in bridge network reliability studies.