Quantifying and Reducing Uncertainty in Transportation System Resilience Assessment: A Dynamic Bayesian Network Approach
Transportation systems are complex, and due to their interdependence with other essential facilities, any damage to them would pose a significant threat to the well-being of communities. Given the frequent occurrences and grave consequences of natural disasters observed in recent years, research on...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/36/1/29 |
| Summary: | Transportation systems are complex, and due to their interdependence with other essential facilities, any damage to them would pose a significant threat to the well-being of communities. Given the frequent occurrences and grave consequences of natural disasters observed in recent years, research on the resilience assessment of transportation systems has received a great deal of attention. This paper develops a dynamic Bayesian network (BN)-based resilience assessment model for a highway network subject to seismic events that can explicitly quantify uncertainties in all phases of the model and investigate the role of inspection and monitoring in uncertainty reduction. The results from this study can be used as comprehensive decision-support information so that decision makers can better assess the resilience of a highway network and associated uncertainties. |
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| ISSN: | 2673-4591 |