A stochastic computational framework for the joint transportation network fragility analysis and traffic flow distribution under extreme events

Bocchini, Paolo; Frangopol, Dan M.

doi:10.1016/j.probengmech.2010.11.007

Cited by 98 publications

(43 citation statements)

References 28 publications

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“…To be consistent with the existing research which generally prefers an exponentially decaying function (e.g. Bocchini and Frangopol, 2011), this study elects a function in the form of Equation 4, reflecting user discretion rather than real data analysis: Figure 6 demonstrates the initial values assumed in this study and the fitted curves to evaluate the entries of R 1 and R 2 . For instance, Figure 6b associates the maximum and minimum differences in the FRC levels with correlation ratios of -0.4 and 1, respectively.…”

Section: Realization Of Correlated Bridge Failuresmentioning

confidence: 80%

Seismic Reliability Assessment of Aging Highway Bridge Networks with Field Instrumentation Data and Correlated Failures, II:Application

et al. 2014

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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.

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Section: Realization Of Correlated Bridge Failuresmentioning

confidence: 80%

Seismic Reliability Assessment of Aging Highway Bridge Networks with Field Instrumentation Data and Correlated Failures, II:Application

et al. 2014

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“…Common construction practices and code enforcement within a community also introduce positive correlation in structural response above and beyond that introduced by the hazard [45,5]. Such correlation structures depend on the stochastic variability in the hazard demand, the locations of bridges and roads, and their susceptibility to damage if the hazardous event occurs, and need to be taken into consideration in the probabilistic evaluation of the network performance [23,4].…”

Section: Reliability Of Bridges (Links) In a Community Transportationmentioning

confidence: 99%

Resilience-based risk mitigation for road networks

2016

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a b s t r a c tTransportation infrastructure has been identified by the US Department of Homeland Security as one of sixteen critical infrastructure systems essential to the well-being of modern societies. In this study, we propose a resilience-based framework for mitigating risk to surface road transportation networks. We utilize recent developments in modern network theory to introduce a novel metric based on system reliability and network connectivity to measure resilience-based performance of a road transportation network. The formulation of this resilience-based performance metric (referred in the paper as WIPW), systematically integrates the network topology, redundancy level, traffic patterns, structural reliability of network components (i.e. roads and bridge) and functionality of the network during community's post-disaster recovery, and permits risk mitigation alternatives for improving transportation network resilience to be compared on a common basis. Using the WIPW as a network performance metric, we propose a project ranking mechanism for identifying and prioritizing transportation network retrofit projects that are critical for effective pre-disaster risk mitigation and resilience planning. We further present a decision methodology to select optimal solutions among possible alternatives of new construction, which offer opportunities to improve the resilience of the network by altering its existing topology. Finally, we conclude with an illustration that uses the WIPW as the performance metric to support risk-based mitigation decisions using a hypothetical bridge network susceptible to seismic hazards.

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“…In Equation (3) and are balancing factors for the cost of time and distance covered. TTT and TTD are the total travel time and distance of all of passengers departing in 1 hour, which can be computed by solving the traffic distribution and assignment problem for transportation networks (Bocchini and Frangopol 2011b). The effect of the damaged bridges on the performance of the network is accounted for by the following traffic parameters:…”

Section: Resiliecne Of Transportation Networkmentioning

confidence: 99%

Optimal Bridge Restoration Sequence for Resilient Transportation Networks

Karamlou¹,

Bocchini²

2014

Structures Congress 2014

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Transportation networks are necessary infrastructure elements to provide aids to impacted areas after the occurrence of an extreme event. Without functional roads, recovering other damaged facilities and lifelines would be slow and difficult. Therefore, restoring the damages of transportation networks, specifically bridges as their most vulnerable elements, is among the first priorities of disaster management officials.This paper presents a new methodology for scheduling the restoration of damaged bridges. The problem is formulated as a multi-objective combinatorial optimization solved by Genetic Algorithms, which minimizes the time to connect the selected critical locations and maximizes the resilience of the transportation network. The main purpose of developing the algorithm was providing a restoration plan which is practical to be used by decision makers at the time of an event, yet based on solid computations rather than mere engineering judgment. The algorithm is examined with a numerical example. The presented algorithm can be considered as the enhancement of previous work performed at Lehigh University. The results show that the new optimization setup improved the solution quality and efficiency compared to the previous techniques.

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A stochastic computational framework for the joint transportation network fragility analysis and traffic flow distribution under extreme events

Cited by 98 publications

References 28 publications

Seismic Reliability Assessment of Aging Highway Bridge Networks with Field Instrumentation Data and Correlated Failures, II:Application

Seismic Reliability Assessment of Aging Highway Bridge Networks with Field Instrumentation Data and Correlated Failures, II:Application

Resilience-based risk mitigation for road networks

Optimal Bridge Restoration Sequence for Resilient Transportation Networks

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