A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Risk-Informed Bridge Ranking at Project and Network Levels
A novel method is proposed to rank bridges for maintenance priorities based on the risk posed by structural deterioration. The proposed method integrates structural reliability analysis, public datasets, and traffic flow theory to provide more accurate estimates of (1) probabilities of bridge failure, and (2) failure consequences. For the former, failure probabilities of deteriorating bridges are determined based on the condition ratings of bridge superstructures and substructures as well as a Markov chain deterioration model; public datasets in the national bridge inventory of the United States are used to estimate Markovian transition probabilities. For the latter, social impacts of bridge failures are considered at the transportation network level to holistically evaluate the failure consequences. By virtue of traffic flow and random field theories, bridge failures and their impacts are analyzed beyond the project level, incorporating decision changes of traffic users in route choices and spatial correlation of bridge failures. Eventually, risk of each bridge in the network is quantified as the expected consequences of an undesirable outcome (i.e., bridge failure). Based on risks at network level, bridges are ranked for maintenance priorities. The proposed method is illustrated through numerical examples and is compared with bridge ranking results based on other indicators, including sufficiency ratings and risks at project level. Compared with other indicators, the proposed method provides a more rational criterion for maintenance planning and portfolio management.
Risk-Informed Bridge Ranking at Project and Network Levels
A novel method is proposed to rank bridges for maintenance priorities based on the risk posed by structural deterioration. The proposed method integrates structural reliability analysis, public datasets, and traffic flow theory to provide more accurate estimates of (1) probabilities of bridge failure, and (2) failure consequences. For the former, failure probabilities of deteriorating bridges are determined based on the condition ratings of bridge superstructures and substructures as well as a Markov chain deterioration model; public datasets in the national bridge inventory of the United States are used to estimate Markovian transition probabilities. For the latter, social impacts of bridge failures are considered at the transportation network level to holistically evaluate the failure consequences. By virtue of traffic flow and random field theories, bridge failures and their impacts are analyzed beyond the project level, incorporating decision changes of traffic users in route choices and spatial correlation of bridge failures. Eventually, risk of each bridge in the network is quantified as the expected consequences of an undesirable outcome (i.e., bridge failure). Based on risks at network level, bridges are ranked for maintenance priorities. The proposed method is illustrated through numerical examples and is compared with bridge ranking results based on other indicators, including sufficiency ratings and risks at project level. Compared with other indicators, the proposed method provides a more rational criterion for maintenance planning and portfolio management.
Risk-Informed Bridge Ranking at Project and Network Levels
Yang, David Y. (author) / Frangopol, Dan M. (author)
2018-07-06
Article (Journal)
Electronic Resource
Unknown
| British Library Online Contents | 2018
A risk ranking strategy for network level bridge management
| Taylor & Francis Verlag | 2010
A risk ranking strategy for network level bridge management
| Online Contents | 2010