Quantifying and Reducing Uncertainty in Resilience Assessment of Transportation Networks Using Dynamic Bayesian Network

Presentation

Quantifying and Reducing Uncertainty in Resilience Assessment of Transportation Networks Using Dynamic Bayesian Network

The Nation’s transportation systems are complex and are some of the highest-valued and largest public assets in the United States. As a result of repeated natural hazards and their significant impact on transportation functionality and socio-economic health of communities, transportation resilience has gained increasing attention in recent years. Previous studies on transportation resilience have heavily emphasized network functionality during and/or following a scenario hazard event by implicitly assuming that sufficient knowledge of structural capacity and environmental/service conditions is available at the time of an extreme event. However, such assumptions often fail to consider uncertainties that arise when an extreme hazard event occurs in the future. Thus, it is essential to quantify and reduce uncertainties to better prepare for extreme events and accurately assess transportation resilience. To this end, this study proposes a dynamic Bayesian network-based resilience assessment model for a large-scale roadway network that can explicitly quantify uncertainties in all phases of the assessment and investigate the role of inspection and monitoring programs in uncertainty reduction. Specifically, the significance of data reliability is investigated through a sensitivity analysis, where various sets of data having different reliability are used in updating system resilience. To evaluate the effectiveness of the model, a benchmark problem involving a highway network in South Carolina, USA is utilized, showcasing the systematic quantification and reduction of uncertainties in the proposed model. The benchmark problem result shows that incorporating monitoring and inspection data on important variables could improve the accuracy of predicting the seismic resilience of the network. It also suggests the need to consider equipment reliability when designing monitoring and inspection programs. With the recent development of a wide range of monitoring and inspection techniques, including non-destructive testing, health monitoring equipment, satellite imagery, LiDAR, etc., these findings can be useful in assisting transportation managers in identifying necessary equipment reliability levels and prioritizing inspection and monitoring efforts.

Speaker

Ji Yun Lee, PhD
Washington State University

Dr. Ji Yun Lee is an Assistant Professor in the Department of Civil and Environmental Engineering at Washington State University (WSU). Prior to joining WSU in 2017, Dr. Lee served as a Postdoctoral Scholar in the Department of Civil and Environmental Engineering at the University of California, Los Angeles (2016-2017) and a Visiting Faculty in the Department of Civil, Environmental and Construction Engineering at the University of Central Florida (2015-2016). She received her Ph.D. (2015) in Civil Engineering from the Georgia Institute of Technology, her M.S. (2011) in Civil Engineering from Stanford University, and her B.S. (2009) in Architectural Engineering from Korea University.

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